diff --git a/configs/callbacks/csv_prediction_writer.yaml b/configs/callbacks/csv_prediction_writer.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b7fc6fb93940f0910938553caec31c02a3aeb60b
--- /dev/null
+++ b/configs/callbacks/csv_prediction_writer.yaml
@@ -0,0 +1,4 @@
+csv_prediction_writer:
+ _target_: deepscreen.utils.lightning.CSVPredictionWriter
+ output_dir: ${paths.output_dir}
+ write_interval: batch
diff --git a/configs/callbacks/default.yaml b/configs/callbacks/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f94c639e898f22416a21af826e6453b99fbf01f1
--- /dev/null
+++ b/configs/callbacks/default.yaml
@@ -0,0 +1,5 @@
+defaults:
+ - model_checkpoint
+ - early_stopping
+ - model_summary
+ - rich_progress_bar
diff --git a/configs/callbacks/early_stopping.yaml b/configs/callbacks/early_stopping.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1d2bb37ad3f056b1fb6cff81f162f7222a3c5a3e
--- /dev/null
+++ b/configs/callbacks/early_stopping.yaml
@@ -0,0 +1,17 @@
+# https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.EarlyStopping.html
+
+# Monitor a metric and stop training when it stops improving.
+# Look at the above link for more detailed information.
+early_stopping:
+ _target_: lightning.pytorch.callbacks.EarlyStopping
+ monitor: ${oc.select:callbacks.model_checkpoint.monitor,"val/loss"} # quantity to be monitored, must be specified!!!
+ min_delta: 0. # minimum change in the monitored quantity to qualify as an improvement
+ patience: 50 # number of checks with no improvement after which training will be stopped
+ verbose: False # verbosity mode
+ mode: ${callbacks.model_checkpoint.mode} # "max" means higher metric value is better, can be also "min"
+ strict: True # whether to crash the training if monitor is not found in the validation metrics
+ check_finite: True # when set True, stops training when the monitor becomes NaN or infinite
+ stopping_threshold: null # stop training immediately once the monitored quantity reaches this threshold
+ divergence_threshold: null # stop training as soon as the monitored quantity becomes worse than this threshold
+ check_on_train_epoch_end: False # whether to run early stopping at the end of the training epoch
+ log_rank_zero_only: False # logs the status of the early stopping callback only for rank 0 process
diff --git a/configs/callbacks/inference.yaml b/configs/callbacks/inference.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..22fe5db38092387f72718e3046e86f57663e8c00
--- /dev/null
+++ b/configs/callbacks/inference.yaml
@@ -0,0 +1,6 @@
+defaults:
+ - model_summary
+ - rich_progress_bar
+
+model_summary:
+ max_depth: 2
diff --git a/configs/callbacks/model_checkpoint.yaml b/configs/callbacks/model_checkpoint.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f8e51b2bb6036ce4a0b1ce3dc47c2869514b2eeb
--- /dev/null
+++ b/configs/callbacks/model_checkpoint.yaml
@@ -0,0 +1,19 @@
+# https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.ModelCheckpoint.html
+
+# Save the model periodically by monitoring a quantity.
+# Look at the above link for more detailed information.
+model_checkpoint:
+ _target_: lightning.pytorch.callbacks.ModelCheckpoint
+ dirpath: ${paths.output_dir} # directory to save the model file
+ filename: "checkpoints/epoch_{epoch:03d}" # checkpoint filename
+ monitor: ${eval:'"val/loss" if ${data.train_val_test_split}[1] else "train/loss"'} # name of the logged metric which determines when model is improving
+ verbose: False # verbosity mode
+ save_last: True # additionally always save an exact copy of the last checkpoint to a file last.ckpt
+ save_top_k: 1 # save k best models (determined by above metric)
+ mode: "min" # "max" means higher metric value is better, can be also "min"
+ auto_insert_metric_name: False # when True, the checkpoints filenames will contain the metric name
+ save_weights_only: False # if True, then only the model’s weights will be saved
+ every_n_train_steps: null # number of training steps between checkpoints
+ train_time_interval: null # checkpoints are monitored at the specified time interval
+ every_n_epochs: null # number of epochs between checkpoints
+ save_on_train_epoch_end: null # whether to run checkpointing at the end of the training epoch or the end of validation
diff --git a/configs/callbacks/model_summary.yaml b/configs/callbacks/model_summary.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f854fd9fd17863a8c5930f482dc0d235cfc03698
--- /dev/null
+++ b/configs/callbacks/model_summary.yaml
@@ -0,0 +1,7 @@
+# https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.RichModelSummary.html
+
+# Generates a summary of all layers in a LightningModule with rich text formatting.
+# Look at the above link for more detailed information.
+model_summary:
+ _target_: lightning.pytorch.callbacks.RichModelSummary
+ max_depth: 2 # The maximum depth of layer nesting that the summary will include. `-1` for all modules `0` for none.
diff --git a/configs/callbacks/none.yaml b/configs/callbacks/none.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/configs/callbacks/rich_progress_bar.yaml b/configs/callbacks/rich_progress_bar.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..82d2f89d28be7511f2360e1b047656afeee16141
--- /dev/null
+++ b/configs/callbacks/rich_progress_bar.yaml
@@ -0,0 +1,6 @@
+# https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.RichProgressBar.html
+
+# Create a progress bar with rich text formatting.
+# Look at the above link for more detailed information.
+rich_progress_bar:
+ _target_: lightning.pytorch.callbacks.RichProgressBar
diff --git a/configs/data/collator/default.yaml b/configs/data/collator/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..514c0837c358aeb82d22ec2c34a7226d05580423
--- /dev/null
+++ b/configs/data/collator/default.yaml
@@ -0,0 +1,5 @@
+_target_: deepscreen.data.utils.collator.collate_fn
+_partial_: true
+
+automatic_padding: false
+padding_value: 0.0
diff --git a/configs/data/collator/none.yaml b/configs/data/collator/none.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..29899b9fc6b0164c149e17b31e350be9135add49
--- /dev/null
+++ b/configs/data/collator/none.yaml
@@ -0,0 +1,2 @@
+_target_: deepscreen.utils.passthrough
+_partial_: true
\ No newline at end of file
diff --git a/configs/data/drug_featurizer/ecfp.yaml b/configs/data/drug_featurizer/ecfp.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a341607f3f242e6f993bf758e7341b376f8b4ef3
--- /dev/null
+++ b/configs/data/drug_featurizer/ecfp.yaml
@@ -0,0 +1,6 @@
+_target_: deepscreen.data.featurizers.fingerprint.smiles_to_fingerprint
+_partial_: true
+
+fingerprint: MorganFP
+nBits: 1024
+radius: 2
\ No newline at end of file
diff --git a/configs/data/drug_featurizer/fcs.yaml b/configs/data/drug_featurizer/fcs.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..77a0cdbec7168829ce846fa28543707a000d0c0d
--- /dev/null
+++ b/configs/data/drug_featurizer/fcs.yaml
@@ -0,0 +1,4 @@
+_target_: deepscreen.data.featurizers.fcs.drug_to_embedding
+_partial_: true
+
+max_sequence_length: 205
\ No newline at end of file
diff --git a/configs/data/drug_featurizer/graph.yaml b/configs/data/drug_featurizer/graph.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d3d65a1fbec937c9484584d8f9f89bbc999539f9
--- /dev/null
+++ b/configs/data/drug_featurizer/graph.yaml
@@ -0,0 +1,2 @@
+_target_: deepscreen.data.featurizers.graph.smiles_to_graph
+_partial_: true
\ No newline at end of file
diff --git a/configs/data/drug_featurizer/label.yaml b/configs/data/drug_featurizer/label.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..80ffe2fbdf7038953a8d94e4fa9def36196d66bb
--- /dev/null
+++ b/configs/data/drug_featurizer/label.yaml
@@ -0,0 +1,15 @@
+#_target_: deepscreen.data.featurizers.categorical.smiles_to_label
+#_partial_: true
+#
+#max_sequence_length: 100
+##in_channels: 63
+
+_target_: deepscreen.data.featurizers.categorical.sequence_to_label
+_partial_: true
+charset: ['#', '%', ')', '(', '+', '-', '.', '1', '0', '3', '2', '5', '4',
+ '7', '6', '9', '8', '=', 'A', 'C', 'B', 'E', 'D', 'G', 'F', 'I',
+ 'H', 'K', 'M', 'L', 'O', 'N', 'P', 'S', 'R', 'U', 'T', 'W', 'V',
+ 'Y', '[', 'Z', ']', '_', 'a', 'c', 'b', 'e', 'd', 'g', 'f', 'i',
+ 'h', 'm', 'l', 'o', 'n', 's', 'r', 'u', 't', 'y']
+
+max_sequence_length: 100
\ No newline at end of file
diff --git a/configs/data/drug_featurizer/mol_features.yaml b/configs/data/drug_featurizer/mol_features.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b0652eace74a5041d7bfa166152e347b9f009c64
--- /dev/null
+++ b/configs/data/drug_featurizer/mol_features.yaml
@@ -0,0 +1,4 @@
+_target_: deepscreen.data.featurizers.graph.smiles_to_mol_features
+_partial_: true
+
+num_atom_feat: 34
diff --git a/configs/data/drug_featurizer/none.yaml b/configs/data/drug_featurizer/none.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..29899b9fc6b0164c149e17b31e350be9135add49
--- /dev/null
+++ b/configs/data/drug_featurizer/none.yaml
@@ -0,0 +1,2 @@
+_target_: deepscreen.utils.passthrough
+_partial_: true
\ No newline at end of file
diff --git a/configs/data/drug_featurizer/onehot.yaml b/configs/data/drug_featurizer/onehot.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..827114b5c04984b2bbd78697dda712a109db3514
--- /dev/null
+++ b/configs/data/drug_featurizer/onehot.yaml
@@ -0,0 +1,15 @@
+#_target_: deepscreen.data.featurizers.categorical.smiles_to_onehot
+#_partial_: true
+#
+#max_sequence_length: 100
+##in_channels: 63
+
+_target_: deepscreen.data.featurizers.categorical.sequence_to_onehot
+_partial_: true
+charset: ['#', '%', ')', '(', '+', '-', '.', '1', '0', '3', '2', '5', '4',
+ '7', '6', '9', '8', '=', 'A', 'C', 'B', 'E', 'D', 'G', 'F', 'I',
+ 'H', 'K', 'M', 'L', 'O', 'N', 'P', 'S', 'R', 'U', 'T', 'W', 'V',
+ 'Y', '[', 'Z', ']', '_', 'a', 'c', 'b', 'e', 'd', 'g', 'f', 'i',
+ 'h', 'm', 'l', 'o', 'n', 's', 'r', 'u', 't', 'y']
+
+max_sequence_length: 100
\ No newline at end of file
diff --git a/configs/data/drug_featurizer/tokenizer.yaml b/configs/data/drug_featurizer/tokenizer.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..369aa42da9f2a5f3990dff0097afd0fe8cf5da55
--- /dev/null
+++ b/configs/data/drug_featurizer/tokenizer.yaml
@@ -0,0 +1,6 @@
+_target_: deepscreen.data.featurizers.token.sequence_to_token_ids
+_partial_: true
+
+tokenizer:
+ _target_: deepscreen.data.featurizers.token.SmilesTokenizer
+ vocab_file: resources/vocabs/smiles.txt
diff --git a/configs/data/dti.yaml.bak b/configs/data/dti.yaml.bak
new file mode 100644
index 0000000000000000000000000000000000000000..93682ed7b88a14ac5e7afecb4327aa50d51d29b8
--- /dev/null
+++ b/configs/data/dti.yaml.bak
@@ -0,0 +1,21 @@
+_target_: deepscreen.data.dti_datamodule.DTIdatamodule
+
+defaults:
+ - _self_
+ - split: null
+ - drug_featurizer: null
+ - protein_featurizer: null
+
+task: ${task.task}
+n_class: ${oc.select:task.task.n_class,null}
+
+data_dir: ${paths.data_dir}
+dataset_name: null
+
+batch_size: 16
+train_val_test_split: [0.7, 0.1, 0.2]
+
+num_workers: 0
+pin_memory: false
+
+train: ${train}
\ No newline at end of file
diff --git a/configs/data/dti_data.yaml b/configs/data/dti_data.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5b65659c15f8fac1a229e16586ffb226293b4f14
--- /dev/null
+++ b/configs/data/dti_data.yaml
@@ -0,0 +1,20 @@
+_target_: deepscreen.data.dti.DTIDataModule
+
+defaults:
+ - split: null
+ - drug_featurizer: none # ???
+ - protein_featurizer: none # ???
+ - collator: default
+
+task: ${task.task}
+num_classes: ${task.num_classes}
+
+data_dir: ${paths.data_dir}
+data_file: null
+train_val_test_split: null
+
+batch_size: ???
+num_workers: 0
+pin_memory: false
+
+#train: ${train}
\ No newline at end of file
diff --git a/configs/data/protein_featurizer/fcs.yaml b/configs/data/protein_featurizer/fcs.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..cbf1f380d2bd951a321971676cb20f7de5e17e7c
--- /dev/null
+++ b/configs/data/protein_featurizer/fcs.yaml
@@ -0,0 +1,4 @@
+_target_: deepscreen.data.featurizers.fcs.protein_to_embedding
+_partial_: true
+
+max_sequence_length: 545
\ No newline at end of file
diff --git a/configs/data/protein_featurizer/label.yaml b/configs/data/protein_featurizer/label.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1824297352e8d0706126486f1a00585e78c4f892
--- /dev/null
+++ b/configs/data/protein_featurizer/label.yaml
@@ -0,0 +1,12 @@
+#_target_: deepscreen.data.featurizers.categorical.fasta_to_label
+#_partial_: true
+#
+#max_sequence_length: 1000
+##in_channels: 26
+
+_target_: deepscreen.data.featurizers.categorical.sequence_to_label
+_partial_: true
+charset: ['A', 'C', 'B', 'E', 'D', 'G', 'F', 'I', 'H', 'K', 'M', 'L', 'O',
+ 'N', 'Q', 'P', 'S', 'R', 'U', 'T', 'W', 'V', 'Y', 'X', 'Z']
+
+max_sequence_length: 1000
\ No newline at end of file
diff --git a/configs/data/protein_featurizer/none.yaml b/configs/data/protein_featurizer/none.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..29899b9fc6b0164c149e17b31e350be9135add49
--- /dev/null
+++ b/configs/data/protein_featurizer/none.yaml
@@ -0,0 +1,2 @@
+_target_: deepscreen.utils.passthrough
+_partial_: true
\ No newline at end of file
diff --git a/configs/data/protein_featurizer/onehot.yaml b/configs/data/protein_featurizer/onehot.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d44e8a5fcc0ac0cfd47e17fda8f302e73bcc359d
--- /dev/null
+++ b/configs/data/protein_featurizer/onehot.yaml
@@ -0,0 +1,12 @@
+#_target_: deepscreen.data.featurizers.categorical.fasta_to_onehot
+#_partial_: true
+#
+#max_sequence_length: 1000
+##in_channels: 26
+
+_target_: deepscreen.data.featurizers.categorical.sequence_to_onehot
+_partial_: true
+charset: ['A', 'C', 'B', 'E', 'D', 'G', 'F', 'I', 'H', 'K', 'M', 'L', 'O',
+ 'N', 'Q', 'P', 'S', 'R', 'U', 'T', 'W', 'V', 'Y', 'X', 'Z']
+
+max_sequence_length: 1000
\ No newline at end of file
diff --git a/configs/data/protein_featurizer/tokenizer.yaml b/configs/data/protein_featurizer/tokenizer.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9e264020782f84719e5370dd3cfc10f44a7a236c
--- /dev/null
+++ b/configs/data/protein_featurizer/tokenizer.yaml
@@ -0,0 +1,6 @@
+_target_: deepscreen.data.featurizers.token.sequence_to_token_ids
+_partial_: true
+
+tokenizer:
+ _target_: tape.TAPETokenizer.from_pretrained
+ vocab: iupac
diff --git a/configs/data/protein_featurizer/word2vec.yaml b/configs/data/protein_featurizer/word2vec.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0d9f4761a374a05ce0f5b20cd80073632f0c030f
--- /dev/null
+++ b/configs/data/protein_featurizer/word2vec.yaml
@@ -0,0 +1,6 @@
+_target_: deepscreen.data.featurizers.word.protein_to_word_embedding
+_partial_: true
+
+model:
+ _target_: gensim.models.Word2Vec.load
+ fname: ${paths.resource_dir}/models/word2vec_30.model
\ No newline at end of file
diff --git a/configs/data/split/cold_drug.yaml b/configs/data/split/cold_drug.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3c0416a876682bb9759822d493e70a21ac0f28f6
--- /dev/null
+++ b/configs/data/split/cold_drug.yaml
@@ -0,0 +1,4 @@
+_target_: deepscreen.data.utils.split.cold_start
+_partial_: true
+
+entity: drug
\ No newline at end of file
diff --git a/configs/data/split/cold_protein.yaml b/configs/data/split/cold_protein.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7a3694841cc5cb00549d3563791a872781d0d775
--- /dev/null
+++ b/configs/data/split/cold_protein.yaml
@@ -0,0 +1,4 @@
+_target_: deepscreen.data.utils.split.cold_start
+_partial_: true
+
+entity: protein
\ No newline at end of file
diff --git a/configs/data/split/none.yaml b/configs/data/split/none.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/configs/data/split/random.yaml b/configs/data/split/random.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e8c00c9b5b38f57fda3b03185a1e91dbefedff8c
--- /dev/null
+++ b/configs/data/split/random.yaml
@@ -0,0 +1,10 @@
+#_target_: torch.utils.data.random_split
+#_partial_: true
+
+#generator:
+# _target_: torch.Generator # will use global seed set by lightning.seed_everything or torch.manual_seed automatically
+
+_target_: deepscreen.data.utils.split.random_split
+_partial_: true
+
+seed: ${seed}
diff --git a/configs/data/transform/minmax.yaml b/configs/data/transform/minmax.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3c4e68561d5768d6b1677900484c386ce3b8df7c
--- /dev/null
+++ b/configs/data/transform/minmax.yaml
@@ -0,0 +1,5 @@
+_target_: deepscreen.data.utils.transform
+_partial_: true
+
+scaler:
+ _target_: sklearn.preprocessing.MinMaxScaler
diff --git a/configs/data/transform/none.yaml b/configs/data/transform/none.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..29899b9fc6b0164c149e17b31e350be9135add49
--- /dev/null
+++ b/configs/data/transform/none.yaml
@@ -0,0 +1,2 @@
+_target_: deepscreen.utils.passthrough
+_partial_: true
\ No newline at end of file
diff --git a/configs/debug/advanced.yaml b/configs/debug/advanced.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2c249cc45699de1d1d8f25aa7c873b127e3df6f9
--- /dev/null
+++ b/configs/debug/advanced.yaml
@@ -0,0 +1,25 @@
+# @package _global_
+
+# advanced debug mode that enables callbacks, loggers and gpu during debugging
+job_name: "debug"
+
+extras:
+ ignore_warnings: False
+ enforce_tags: False
+
+hydra:
+ job_logging:
+ root:
+ level: DEBUG
+ verbose: True
+
+trainer:
+ max_epochs: 1
+ accelerator: gpu
+ devices: 1
+ detect_anomaly: true
+ deterministic: false
+
+data:
+ num_workers: 0
+ pin_memory: False
diff --git a/configs/debug/default.yaml b/configs/debug/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..84e7dc1e80b355c8ab075a28e65d8fae08127eea
--- /dev/null
+++ b/configs/debug/default.yaml
@@ -0,0 +1,35 @@
+# @package _global_
+
+# default debugging setup, runs 1 full epoch
+# other debugging configs can inherit from this one
+
+# overwrite job name so debugging logs are stored in separate folder
+job_name: "debug"
+
+# disable callbacks and loggers during debugging
+callbacks: null
+logger: null
+
+extras:
+ ignore_warnings: False
+ enforce_tags: False
+
+# sets level of all command line loggers to 'DEBUG'
+# https://hydra.cc/docs/tutorials/basic/running_your_app/logging/
+hydra:
+ job_logging:
+ root:
+ level: DEBUG
+ # use this to also set hydra loggers to 'DEBUG'
+ verbose: True
+
+trainer:
+ max_epochs: 1
+ accelerator: cpu # debuggers don't like gpus
+ devices: 1 # debuggers don't like multiprocessing
+ detect_anomaly: true # raise exception if NaN or +/-inf is detected in any tensor
+ deterministic: false
+
+data:
+ num_workers: 0 # debuggers don't like multiprocessing
+ pin_memory: False # disable gpu memory pin
diff --git a/configs/debug/fdr.yaml b/configs/debug/fdr.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..111e2f35924bf77db6706867a5a381cb90e2e855
--- /dev/null
+++ b/configs/debug/fdr.yaml
@@ -0,0 +1,11 @@
+# @package _global_
+
+# runs 1 train, 1 validation and 1 test step
+
+defaults:
+ - default
+
+trainer:
+ accelerator: gpu
+ fast_dev_run: true
+ detect_anomaly: true
diff --git a/configs/debug/fdr_advanced.yaml b/configs/debug/fdr_advanced.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..570718adfc2148760067ac799cdc3f4ae38f6c78
--- /dev/null
+++ b/configs/debug/fdr_advanced.yaml
@@ -0,0 +1,11 @@
+# @package _global_
+
+# runs 1 train, 1 validation and 1 test step
+
+defaults:
+ - advanced
+
+trainer:
+ accelerator: gpu
+ fast_dev_run: true
+ detect_anomaly: true
\ No newline at end of file
diff --git a/configs/debug/limit.yaml b/configs/debug/limit.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..514d77fbd1475b03fff0372e3da3c2fa7ea7d190
--- /dev/null
+++ b/configs/debug/limit.yaml
@@ -0,0 +1,12 @@
+# @package _global_
+
+# uses only 1% of the training data and 5% of validation/test data
+
+defaults:
+ - default
+
+trainer:
+ max_epochs: 3
+ limit_train_batches: 0.01
+ limit_val_batches: 0.05
+ limit_test_batches: 0.05
diff --git a/configs/debug/overfit.yaml b/configs/debug/overfit.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9906586a67a12aa81ff69138f589a366dbe2222f
--- /dev/null
+++ b/configs/debug/overfit.yaml
@@ -0,0 +1,13 @@
+# @package _global_
+
+# overfits to 3 batches
+
+defaults:
+ - default
+
+trainer:
+ max_epochs: 20
+ overfit_batches: 3
+
+# model ckpt and early stopping need to be disabled during overfitting
+callbacks: null
diff --git a/configs/debug/profiler.yaml b/configs/debug/profiler.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2bd7da87ae23ed425ace99b09250a76a5634a3fb
--- /dev/null
+++ b/configs/debug/profiler.yaml
@@ -0,0 +1,12 @@
+# @package _global_
+
+# runs with execution time profiling
+
+defaults:
+ - default
+
+trainer:
+ max_epochs: 1
+ profiler: "simple"
+ # profiler: "advanced"
+ # profiler: "pytorch"
diff --git a/configs/experiment/bindingdb.yaml b/configs/experiment/bindingdb.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e3e170d30a9bbdd3d0252b43979b2861aeb39ce1
--- /dev/null
+++ b/configs/experiment/bindingdb.yaml
@@ -0,0 +1,9 @@
+# @package _global_
+defaults:
+ - dti_experiment
+ - override /task: binary
+
+data:
+ train_val_test_split: [dti_benchmark/random_split_update/bindingdb_train.csv,
+ dti_benchmark/random_split_update/bindingdb_valid.csv,
+ dti_benchmark/random_split_update/bindingdb_test.csv]
diff --git a/configs/experiment/chembl_random.yaml b/configs/experiment/chembl_random.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..01b9e8aca42b0925a22303d9f2c3f8c4f5998340
--- /dev/null
+++ b/configs/experiment/chembl_random.yaml
@@ -0,0 +1,9 @@
+# @package _global_
+defaults:
+ - dti_experiment
+ - override /task: binary
+
+data:
+ train_val_test_split: [chembl_random_global_balance_1_train.csv,
+ chembl_random_global_balance_1_valid.csv,
+ chembl_random_global_balance_1_test.csv]
diff --git a/configs/experiment/chembl_rmfh_random.yaml b/configs/experiment/chembl_rmfh_random.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a15e8ebfac7db334dc82d22adae2181c1473d9c8
--- /dev/null
+++ b/configs/experiment/chembl_rmfh_random.yaml
@@ -0,0 +1,9 @@
+# @package _global_
+defaults:
+ - dti_experiment
+ - override /task: binary
+
+data:
+ train_val_test_split: [chembl_rmFH_random_global_balance_1_train.csv,
+ chembl_rmFH_random_global_balance_1_valid.csv,
+ chembl_rmFH_random_global_balance_1_test.csv]
\ No newline at end of file
diff --git a/configs/experiment/davis.yaml b/configs/experiment/davis.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..95c628e21ba17d808363a8f89f36ad0513e6a1de
--- /dev/null
+++ b/configs/experiment/davis.yaml
@@ -0,0 +1,9 @@
+# @package _global_
+defaults:
+ - dti_experiment
+ - override /task: binary
+
+data:
+ train_val_test_split: [dti_benchmark/random_split_update/davis_train.csv,
+ dti_benchmark/random_split_update/davis_valid.csv,
+ dti_benchmark/random_split_update/davis_test.csv]
diff --git a/configs/experiment/demo_bindingdb.yaml b/configs/experiment/demo_bindingdb.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0ccc1bcdfd5a12025a0ff6aa591a6989f3db2982
--- /dev/null
+++ b/configs/experiment/demo_bindingdb.yaml
@@ -0,0 +1,9 @@
+# @package _global_
+defaults:
+ - dti_experiment
+ - override /task: binary
+ - override /data/split: random
+
+data:
+ data_file: demo/binddb_ic50_demo.csv
+ train_val_test_split: [0.7, 0.1, 0.2]
diff --git a/configs/experiment/dti_experiment.yaml b/configs/experiment/dti_experiment.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5fd6b2c81f23cb029e660ccc7bc146ef903b28a8
--- /dev/null
+++ b/configs/experiment/dti_experiment.yaml
@@ -0,0 +1,19 @@
+# @package _global_
+defaults:
+ - override /data: dti_data
+ - override /model: dti_model
+ - override /trainer: gpu
+
+seed: 12345
+
+trainer:
+ min_epochs: 1
+ max_epochs: 500
+ precision: bf16
+
+callbacks:
+ early_stopping:
+ patience: 50
+
+data:
+ num_workers: 8
diff --git a/configs/experiment/example.yaml b/configs/experiment/example.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..03babeb44c0eec70b050e1f92534b6cd8de770d8
--- /dev/null
+++ b/configs/experiment/example.yaml
@@ -0,0 +1,35 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+defaults:
+ - override /data: dti_data
+ - override /data/drug_featurizer: onehot
+ - override /data/protein_featurizer: onehot
+ - override /model: dti_model
+ - override /model/protein_encoder: cnn
+ - override /model/drug_encoder: cnn
+ - override /model/decoder: concat_mlp
+ - override /callbacks: default
+ - override /trainer: default
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["dti"]
+
+seed: 12345
+
+data:
+ data_file: davis.csv
+ batch_size: 64
+
+model:
+ optimizer:
+ lr: 0.0001
+
+trainer:
+ min_epochs: 1
+ max_epochs: 100
+ accelerator: gpu
\ No newline at end of file
diff --git a/configs/experiment/ion_channels.yaml b/configs/experiment/ion_channels.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..261d7dac49ccb908a63719c01fd04374e4d21019
--- /dev/null
+++ b/configs/experiment/ion_channels.yaml
@@ -0,0 +1,9 @@
+# @package _global_
+defaults:
+ - dti_experiment
+ - override /task: binary
+
+data:
+ train_val_test_split: [dti_benchmark/ChEMBL33/train/Ion_channels_train_data.csv,
+ dti_benchmark/ChEMBL33/valid/Ion_channels_valid_data.csv,
+ dti_benchmark/ChEMBL33/test/Ion_channels_both_unseen_test_data.csv]
diff --git a/configs/experiment/kiba.yaml b/configs/experiment/kiba.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f3a83942f41b75c800bf467690b88b8397955615
--- /dev/null
+++ b/configs/experiment/kiba.yaml
@@ -0,0 +1,9 @@
+# @package _global_
+defaults:
+ - dti_experiment
+ - /task: binary
+
+data:
+ train_val_test_split: [dti_benchmark/random_split_update/kiba_train.csv,
+ dti_benchmark/random_split_update/kiba_valid.csv,
+ dti_benchmark/random_split_update/kiba_test.csv]
diff --git a/configs/experiment/kinase.yaml b/configs/experiment/kinase.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c7d129aca1c9d93934efbe61a243cf7603d30143
--- /dev/null
+++ b/configs/experiment/kinase.yaml
@@ -0,0 +1,13 @@
+# @package _global_
+defaults:
+ - dti_experiment
+ - override /task: binary
+
+data:
+ train_val_test_split:
+ - dti_benchmark/ChEMBL33/train/kinase_train_data.csv
+ - null
+ - null
+# dti_benchmark/ChEMBL33/valid/kinase_valid_data.csv,
+# dti_benchmark/ChEMBL33/test/kinase_both_unseen_test_data.csv
+
diff --git a/configs/experiment/membrane_receptors.yaml b/configs/experiment/membrane_receptors.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a06d357351dba800c885d7d92e51a7a7ccffc474
--- /dev/null
+++ b/configs/experiment/membrane_receptors.yaml
@@ -0,0 +1,13 @@
+# @package _global_
+defaults:
+ - dti_experiment
+ - override /task: binary
+
+data:
+ train_val_test_split:
+ - dti_benchmark/ChEMBL33/train/Membrane_receptor_train_data.csv
+ - null
+ - null
+# dti_benchmark/ChEMBL33/valid/Membrane_receptor_valid_data.csv,
+# dti_benchmark/ChEMBL33/test/Membrane_receptor_drug_repo_test_data.csv
+
diff --git a/configs/experiment/non_kinase_enzymes.yaml b/configs/experiment/non_kinase_enzymes.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..314eeb66a368dfcab29313a647e620ee5bcd7cd9
--- /dev/null
+++ b/configs/experiment/non_kinase_enzymes.yaml
@@ -0,0 +1,13 @@
+# @package _global_
+defaults:
+ - dti_experiment
+ - override /task: binary
+
+data:
+ train_val_test_split:
+ - dti_benchmark/ChEMBL33/train/Non_kinase_enzyme_train_data.csv
+ - null
+ - null
+# dti_benchmark/ChEMBL33/valid/Non_kinase_enzyme_valid_data.csv,
+# dti_benchmark/ChEMBL33/test/Non_kinase_enzyme_both_unseen_test_data.csv
+
diff --git a/configs/experiment/nuclear_receptors.yaml b/configs/experiment/nuclear_receptors.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2edf8a4dea610a9a7c2a9b73178f452888bb20c2
--- /dev/null
+++ b/configs/experiment/nuclear_receptors.yaml
@@ -0,0 +1,9 @@
+# @package _global_
+defaults:
+ - dti_experiment
+ - override /task: binary
+
+data:
+ train_val_test_split: [dti_benchmark/ChEMBL33/train/Nuclear_receptors_train_data.csv,
+ dti_benchmark/ChEMBL33/valid/Nuclear_receptors_valid_data.csv,
+ dti_benchmark/ChEMBL33/test/Nuclear_receptors_both_unseen_test_data.csv]
diff --git a/configs/experiment/other_protein_targets.yaml b/configs/experiment/other_protein_targets.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ffe3f295621ee3ffc160521e88e2bdf547719190
--- /dev/null
+++ b/configs/experiment/other_protein_targets.yaml
@@ -0,0 +1,9 @@
+# @package _global_
+defaults:
+ - dti_experiment
+ - override /task: binary
+
+data:
+ train_val_test_split: [dti_benchmark/ChEMBL33/train/Other_protein_targets_train_data.csv,
+ dti_benchmark/ChEMBL33/valid/Other_protein_targets_valid_data.csv,
+ dti_benchmark/ChEMBL33/test/Other_protein_targets_both_unseen_test_data.csv]
diff --git a/configs/extras/default.yaml b/configs/extras/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b9c6b622283a647fbc513166fc14f016cc3ed8a0
--- /dev/null
+++ b/configs/extras/default.yaml
@@ -0,0 +1,8 @@
+# disable python warnings if they annoy you
+ignore_warnings: False
+
+# ask user for tags if none are provided in the config
+enforce_tags: True
+
+# pretty print config tree at the start of the run using Rich library
+print_config: True
diff --git a/configs/hydra/callbacks/csv_experiment_summary.yaml b/configs/hydra/callbacks/csv_experiment_summary.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1b0d00b4f7c73dfdc02846e428fc6793133cc549
--- /dev/null
+++ b/configs/hydra/callbacks/csv_experiment_summary.yaml
@@ -0,0 +1,3 @@
+csv_experiment_summary:
+ _target_: deepscreen.utils.hydra.CSVExperimentSummary
+ prefix: ['test/', 'epoch']
\ No newline at end of file
diff --git a/configs/hydra/callbacks/default.yaml b/configs/hydra/callbacks/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3e7617d233fb7f325cdd03caf82bcc0e2ba6bf6d
--- /dev/null
+++ b/configs/hydra/callbacks/default.yaml
@@ -0,0 +1,2 @@
+defaults:
+ - csv_experiment_summary
diff --git a/configs/hydra/default.yaml b/configs/hydra/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8d76171f4585f3c26e6c249ba9d4e1617994b421
--- /dev/null
+++ b/configs/hydra/default.yaml
@@ -0,0 +1,27 @@
+# https://hydra.cc/docs/configure_hydra/intro/
+
+# enable color logging
+defaults:
+ - override callbacks: default
+ - override hydra_logging: colorlog
+ - override job_logging: colorlog
+
+# output directory, generated dynamically on each run
+run:
+ dir: ${paths.log_dir}/${job_name}/runs/${now:%Y-%m-%d}_${now:%H-%M-%S-%f}_[${eval:'",".join(${tags})'}]
+sweep:
+ dir: ${paths.log_dir}/${job_name}/multiruns/${now:%Y-%m-%d}_${now:%H-%M-%S-%f}_[${eval:'",".join(${tags})'}]
+ # Sanitize override_dirname by replacing unsafe characters to avoid unintended subdirectory creation
+ subdir: ${sanitize_path:'${hydra:job.id}-${hydra:job.override_dirname}'}
+
+job_logging:
+ handlers:
+ file:
+ filename: ${hydra:runtime.output_dir}/${hydra.job.name}.log
+
+job:
+ config:
+ override_dirname:
+ kv_sep: '='
+ item_sep: ';'
+ exclude_keys: ['tags', 'sweep', 'data.data_file', 'data.train_val_test_split', 'ckpt_path', 'trainer']
diff --git a/configs/hydra/launcher/submitit_local_example.yaml b/configs/hydra/launcher/submitit_local_example.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2f47752804b52ea1216d750ac3229d12e463aaff
--- /dev/null
+++ b/configs/hydra/launcher/submitit_local_example.yaml
@@ -0,0 +1,12 @@
+# @package _global_
+defaults:
+ - submitit_local
+
+submitit_folder: ${hydra.sweep.dir}/.submitit/%j
+timeout_min: 60
+cpus_per_task: 1
+gpus_per_node: 1
+tasks_per_node: 8
+mem_gb: 16
+nodes: 1
+name: ${hydra.job.name}
diff --git a/configs/hydra/launcher/submitit_slurm_example.yaml b/configs/hydra/launcher/submitit_slurm_example.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1dec61801ed47679ea6598ef2540965e110249c4
--- /dev/null
+++ b/configs/hydra/launcher/submitit_slurm_example.yaml
@@ -0,0 +1,31 @@
+# @package _global_
+defaults:
+ - submitit_slurm
+
+hydra:
+ mode: "MULTIRUN"
+ launcher:
+ submitit_folder: ${hydra.sweep.dir}/.submitit/%j
+ timeout_min: null
+ cpus_per_task: null
+ gpus_per_node: null
+ tasks_per_node: 1
+ mem_gb: null
+ nodes: 1
+ name: ${hydra.job.name}
+ partition: null
+ qos: null
+ comment: null
+ constraint: null
+ exclude: null
+ gres: null
+ cpus_per_gpu: null
+ gpus_per_task: null
+ mem_per_gpu: null
+ mem_per_cpu: null
+ account: null
+ signal_delay_s: 120
+ max_num_timeout: 0
+ additional_parameters: {}
+ array_parallelism: 256
+ setup: null
diff --git a/configs/hydra/sweeper/optuna_hps.yaml b/configs/hydra/sweeper/optuna_hps.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..316cf24ab252557a08a5e82fa8c9c462b9835246
--- /dev/null
+++ b/configs/hydra/sweeper/optuna_hps.yaml
@@ -0,0 +1,44 @@
+# @package _global_
+
+# example batch experiment of some experiment with Optuna:
+# python train.py -m sweep=optuna experiment=example
+
+defaults:
+ - optuna
+
+# choose metric which will be optimized by Optuna
+# make sure this is the correct name of some metric logged in lightning module!
+objective_metrics: ["val/auroc"]
+
+# here we define Optuna hyperparameter search
+# it optimizes for value returned from function with @hydra.main decorator
+# docs: https://hydra.cc/docs/next/plugins/optuna_sweeper
+hydra:
+ mode: "MULTIRUN" # set hydra to multirun by default if this config is attached
+ sweeper:
+ # storage URL to persist optimization results
+ # for example, you can use SQLite if you set 'sqlite:///example.db'
+ storage: null
+
+ # name of the study to persist optimization results
+ study_name: null
+
+ # number of parallel workers
+ n_jobs: 1
+
+ # 'minimize' or 'maximize' the objective
+ direction: minimize
+
+ # total number of runs that will be executed
+ n_trials: 20
+
+ # choose Optuna hyperparameter sampler
+ # you can choose bayesian sampler (tpe), random search (without optimization), grid sampler, and others
+ # docs: https://optuna.readthedocs.io/en/stable/reference/samplers.html
+ sampler:
+ _target_: optuna.samplers.TPESampler
+ seed: 12345
+ n_startup_trials: 10 # number of random sampling runs before optimization starts
+
+ # define hyperparameter search space
+ params: ???
diff --git a/configs/local/.gitkeep b/configs/local/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/configs/local/gpu3090.yaml b/configs/local/gpu3090.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..407f3e2b68c5f268b917310112398cdd607c51e6
--- /dev/null
+++ b/configs/local/gpu3090.yaml
@@ -0,0 +1,15 @@
+# @package _global_
+defaults:
+ - override /hydra/launcher: submitit_slurm
+
+hydra:
+ launcher:
+ submitit_folder: ${hydra.sweep.dir}/.submitit
+ timeout_min: 6000
+ cpus_per_task: 8
+ gpus_per_task: 1
+ gres: gpu:1
+ partition: gpu3090
+ qos: gpu3090
+ additional_parameters: {}
+ array_parallelism: 256
diff --git a/configs/logger/comet.yaml b/configs/logger/comet.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ebe6c6f19bd172255e9f29148df10a4d2ec42c84
--- /dev/null
+++ b/configs/logger/comet.yaml
@@ -0,0 +1,12 @@
+# https://www.comet.ml
+
+comet:
+ _target_: lightning.pytorch.loggers.comet.CometLogger
+ api_key: ${oc.env:COMET_API_TOKEN} # api key is loaded from environment variable
+ save_dir: "${paths.output_dir}"
+ project_name: "deepscreen"
+ rest_api_key: null
+ # experiment_name: ""
+ experiment_key: null # set to resume experiment
+ offline: False
+ prefix: ""
\ No newline at end of file
diff --git a/configs/logger/csv.yaml b/configs/logger/csv.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..bf9af6fb5e70b1382a1421397b12ea677dd8926f
--- /dev/null
+++ b/configs/logger/csv.yaml
@@ -0,0 +1,8 @@
+# csv logger built in lightning
+
+csv:
+ _target_: lightning.pytorch.loggers.csv_logs.CSVLogger
+ save_dir: "${paths.output_dir}"
+ name: "csv/"
+ prefix: ""
+ version: ""
diff --git a/configs/logger/default.yaml b/configs/logger/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..bfdb2248689fcc8c5827c6c99c0d42bfad37b3c8
--- /dev/null
+++ b/configs/logger/default.yaml
@@ -0,0 +1,5 @@
+defaults:
+ - csv
+# - mlflow
+# - wandb
+# - comet
\ No newline at end of file
diff --git a/configs/logger/mlflow.yaml b/configs/logger/mlflow.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..bed42abc9cd686d89137e236a009c2c9277f2608
--- /dev/null
+++ b/configs/logger/mlflow.yaml
@@ -0,0 +1,12 @@
+# https://mlflow.org
+
+mlflow:
+ _target_: lightning.pytorch.loggers.mlflow.MLFlowLogger
+ # experiment_name: ""
+ # run_name: ""
+ tracking_uri: "file://${paths.output_dir}/mlflow/" # run `mlflow ui` command inside the `logs/mlflow/` dir to open the UI
+ tags: ${tags}
+ # save_dir: "./mlruns"
+ prefix: ""
+ artifact_location: null
+ # run_id: ""
\ No newline at end of file
diff --git a/configs/logger/multiple_loggers.yaml b/configs/logger/multiple_loggers.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..40d561d1b723cb314de4272210a96c4343dcace9
--- /dev/null
+++ b/configs/logger/multiple_loggers.yaml
@@ -0,0 +1,6 @@
+# train with multiple loggers at once
+defaults:
+ - csv
+ - tensorboard
+# - mlflow
+# - wandb
\ No newline at end of file
diff --git a/configs/logger/neptune.yaml b/configs/logger/neptune.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..086e85fc59117f4a8035d0c1773e4c856fd7e995
--- /dev/null
+++ b/configs/logger/neptune.yaml
@@ -0,0 +1,9 @@
+# https://neptune.ai
+
+neptune:
+ _target_: lightning.pytorch.loggers.neptune.NeptuneLogger
+ api_key: ${oc.env:NEPTUNE_API_TOKEN} # api key is loaded from environment variable
+ project: username/deepscreen
+ # name: ""
+ log_model_checkpoints: True
+ prefix: ""
\ No newline at end of file
diff --git a/configs/logger/tensorboard.yaml b/configs/logger/tensorboard.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f438664df015f88bd3cabffaa520da3ef98dc379
--- /dev/null
+++ b/configs/logger/tensorboard.yaml
@@ -0,0 +1,10 @@
+# https://www.tensorflow.org/tensorboard/
+
+tensorboard:
+ _target_: lightning.pytorch.loggers.tensorboard.TensorBoardLogger
+ save_dir: "${paths.output_dir}/tensorboard/"
+ name: null
+ log_graph: False
+ default_hp_metric: True
+ prefix: ""
+ version: ""
diff --git a/configs/logger/wandb.yaml b/configs/logger/wandb.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9ec9ba26d7673c20005d8e67e3bed2b647c80218
--- /dev/null
+++ b/configs/logger/wandb.yaml
@@ -0,0 +1,16 @@
+# https://wandb.ai
+
+wandb:
+ _target_: lightning.pytorch.loggers.wandb.WandbLogger
+ # name: "" # name of the run (normally generated by wandb)
+ save_dir: "${paths.output_dir}"
+ offline: True
+ id: null # pass correct id to resume experiment!
+ anonymous: null # enable anonymous logging
+ project: "deepscreen"
+ log_model: False # upload lightning ckpts
+ prefix: "" # a string to put at the beginning of metric keys
+ # entity: "" # set to name of your wandb team
+ group: ""
+ tags: ${tags}
+ job_type: ""
\ No newline at end of file
diff --git a/configs/model/dti_model.yaml b/configs/model/dti_model.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..72b1c9ee2b75a2f6ffce9a33b8d42cafd9cc67f0
--- /dev/null
+++ b/configs/model/dti_model.yaml
@@ -0,0 +1,12 @@
+_target_: deepscreen.models.dti.DTILightningModule
+
+defaults:
+ - _self_
+ - optimizer: adam
+ - scheduler: default
+ - predictor: none
+ - metrics: dti_metrics
+
+out: ${task.out}
+loss: ${task.loss}
+activation: ${task.activation}
diff --git a/configs/model/loss/multitask_loss.yaml b/configs/model/loss/multitask_loss.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6dfe6353808f3f91a42e151a34a3cae056e0fc7e
--- /dev/null
+++ b/configs/model/loss/multitask_loss.yaml
@@ -0,0 +1,7 @@
+_target_: deepscreen.models.loss.multitask_loss.MultitaskLoss
+
+loss_fns:
+ - _target_: torch.nn.MSELoss
+ - _target_: torch.nn.CrossEntropyLoss
+ weight: null
+reduction: sum
\ No newline at end of file
diff --git a/configs/model/metrics/accuracy.yaml b/configs/model/metrics/accuracy.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..80a9d3f8a6837571b70d0cfb70ba7c6f59ee1c3e
--- /dev/null
+++ b/configs/model/metrics/accuracy.yaml
@@ -0,0 +1,4 @@
+accuracy:
+ _target_: torchmetrics.Accuracy
+ task: ${task.task}
+ num_classes: ${task.num_classes}
\ No newline at end of file
diff --git a/configs/model/metrics/auprc.yaml b/configs/model/metrics/auprc.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b9de03c65fdeec91e8b39f38dd773d9956e2e8ce
--- /dev/null
+++ b/configs/model/metrics/auprc.yaml
@@ -0,0 +1,4 @@
+auprc:
+ _target_: torchmetrics.AveragePrecision
+ task: ${task.task}
+ num_classes: ${task.num_classes}
\ No newline at end of file
diff --git a/configs/model/metrics/auroc.yaml b/configs/model/metrics/auroc.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a4bcdbbd885ae5ba05120e5568ca7d3a323f213f
--- /dev/null
+++ b/configs/model/metrics/auroc.yaml
@@ -0,0 +1,4 @@
+auroc:
+ _target_: torchmetrics.AUROC
+ task: ${task.task}
+ num_classes: ${task.num_classes}
\ No newline at end of file
diff --git a/configs/model/metrics/bedroc.yaml b/configs/model/metrics/bedroc.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..86c68585882faefde5ee711dfe647d406a06dd09
--- /dev/null
+++ b/configs/model/metrics/bedroc.yaml
@@ -0,0 +1,3 @@
+bedroc:
+ _target_: deepscreen.models.metrics.bedroc.BEDROC
+ alpha: 80.5
\ No newline at end of file
diff --git a/configs/model/metrics/concordance_index.yaml b/configs/model/metrics/concordance_index.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..634ea1906b5d0307e2e0342beec04c28e66af308
--- /dev/null
+++ b/configs/model/metrics/concordance_index.yaml
@@ -0,0 +1,2 @@
+# FIXME: implement concordance index
+_target_:
\ No newline at end of file
diff --git a/configs/model/metrics/confusion_matrix.yaml b/configs/model/metrics/confusion_matrix.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d0be32b6b92a93e743d3926bf19fc3849c2e9260
--- /dev/null
+++ b/configs/model/metrics/confusion_matrix.yaml
@@ -0,0 +1 @@
+_target_: torchmetrics.ConfusionMatrix
\ No newline at end of file
diff --git a/configs/model/metrics/dta_metrics.yaml b/configs/model/metrics/dta_metrics.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2b0f108cb9df18dfcceae6af9fd94789685a54b2
--- /dev/null
+++ b/configs/model/metrics/dta_metrics.yaml
@@ -0,0 +1,2 @@
+defaults:
+ - mean_squared_error
diff --git a/configs/model/metrics/dti_metrics.yaml b/configs/model/metrics/dti_metrics.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..93ca0c40a0d77ffb095ab91d52863667bf5746cc
--- /dev/null
+++ b/configs/model/metrics/dti_metrics.yaml
@@ -0,0 +1,14 @@
+# train with many loggers at once
+
+defaults:
+ - auroc
+ - auprc
+ - specificity
+ - sensitivity
+ - precision
+ - recall
+ - f1_score
+# Common virtual screening metrics:
+# - ef
+# - bedroc
+# - hit_rate
diff --git a/configs/model/metrics/ef.yaml b/configs/model/metrics/ef.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..82553b414da98c55508db9830d82b12af3db786d
--- /dev/null
+++ b/configs/model/metrics/ef.yaml
@@ -0,0 +1,7 @@
+ef1:
+ _target_: deepscreen.models.metrics.ef.EF
+ alpha: 0.01
+
+ef5:
+ _target_: deepscreen.models.metrics.ef.EF
+ alpha: 0.05
\ No newline at end of file
diff --git a/configs/model/metrics/f1_score.yaml b/configs/model/metrics/f1_score.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..abfb6e4ca37a9dad399aeb3b1244d958d542e238
--- /dev/null
+++ b/configs/model/metrics/f1_score.yaml
@@ -0,0 +1,4 @@
+f1_score:
+ _target_: torchmetrics.F1Score
+ task: ${task.task}
+ num_classes: ${task.num_classes}
\ No newline at end of file
diff --git a/configs/model/metrics/hit_rate.yaml b/configs/model/metrics/hit_rate.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..70976774eb365fdf8b8c1f97bd3ad19d6cb64cb8
--- /dev/null
+++ b/configs/model/metrics/hit_rate.yaml
@@ -0,0 +1,3 @@
+hit_rate:
+ _target_: deepscreen.models.metrics.hit_rate.HitRate
+ alpha: 0.05
diff --git a/configs/model/metrics/mean_squared_error.yaml b/configs/model/metrics/mean_squared_error.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0d9a18c60d43210b16878ff479b1cfa3168788cf
--- /dev/null
+++ b/configs/model/metrics/mean_squared_error.yaml
@@ -0,0 +1,2 @@
+mean_squared_error:
+ _target_: torchmetrics.MeanSquaredError
\ No newline at end of file
diff --git a/configs/model/metrics/prc.yaml b/configs/model/metrics/prc.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..75e3ee320d5b9a32a9acdb55d564fbacab975088
--- /dev/null
+++ b/configs/model/metrics/prc.yaml
@@ -0,0 +1,4 @@
+prc:
+ _target_: torchmetrics.PrecisionRecallCurve
+ task: ${task.task}
+ num_classes: ${task.num_classes}
\ No newline at end of file
diff --git a/configs/model/metrics/precision.yaml b/configs/model/metrics/precision.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..4b8212b1999c10b627de5859e034260a39022608
--- /dev/null
+++ b/configs/model/metrics/precision.yaml
@@ -0,0 +1,4 @@
+precision:
+ _target_: torchmetrics.Precision
+ task: ${task.task}
+ num_classes: ${task.num_classes}
diff --git a/configs/model/metrics/recall.yaml b/configs/model/metrics/recall.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..eadad752ad1c4e1ac137580a289d4d32916ca975
--- /dev/null
+++ b/configs/model/metrics/recall.yaml
@@ -0,0 +1,4 @@
+recall:
+ _target_: torchmetrics.Recall
+ task: ${task.task}
+ num_classes: ${task.num_classes}
\ No newline at end of file
diff --git a/configs/model/metrics/roc.yaml b/configs/model/metrics/roc.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..91968a6f42e3d399f39e587785eaddabda523a23
--- /dev/null
+++ b/configs/model/metrics/roc.yaml
@@ -0,0 +1,4 @@
+roc:
+ _target_: torchmetrics.ROC
+ task: ${task.task}
+ num_classes: ${task.num_classes}
\ No newline at end of file
diff --git a/configs/model/metrics/sensitivity.yaml b/configs/model/metrics/sensitivity.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..49568b4512c2b75ebfce98d8ee03e2b1148966cc
--- /dev/null
+++ b/configs/model/metrics/sensitivity.yaml
@@ -0,0 +1,4 @@
+sensitivity:
+ _target_: deepscreen.models.metrics.sensitivity.Sensitivity
+ task: ${task.task}
+ num_classes: ${task.num_classes}
\ No newline at end of file
diff --git a/configs/model/metrics/specificity.yaml b/configs/model/metrics/specificity.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5b161be947876081ce9b6b070ed7201874f5cbc6
--- /dev/null
+++ b/configs/model/metrics/specificity.yaml
@@ -0,0 +1,4 @@
+specificity:
+ _target_: torchmetrics.Specificity
+ task: ${task.task}
+ num_classes: ${task.num_classes}
\ No newline at end of file
diff --git a/configs/model/metrics/test_metrics.yaml b/configs/model/metrics/test_metrics.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2ff2b80870d15e9863158e6dfbd17cf3dd9586c2
--- /dev/null
+++ b/configs/model/metrics/test_metrics.yaml
@@ -0,0 +1,11 @@
+# train with many loggers at once
+
+defaults:
+ - auroc
+ - auprc
+ - roc
+ - prc
+# Common virtual screening metrics:
+# - ef
+# - bedroc
+# - hit_rate
diff --git a/configs/model/optimizer/adam.yaml b/configs/model/optimizer/adam.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..67ab7f2cfeeb16daef186434fd4655afab56bf37
--- /dev/null
+++ b/configs/model/optimizer/adam.yaml
@@ -0,0 +1,5 @@
+_target_: torch.optim.Adam
+_partial_: true
+
+lr: 0.0001
+weight_decay: 0.0
\ No newline at end of file
diff --git a/configs/model/optimizer/none.yaml b/configs/model/optimizer/none.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/configs/model/predictor/custom.yaml b/configs/model/predictor/custom.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d7a867b87888c03a7209b4b78bbdf2dadfff07a2
--- /dev/null
+++ b/configs/model/predictor/custom.yaml
@@ -0,0 +1,6 @@
+_target_: deepscreen.models.predictors.custom.CustomPredictor
+
+defaults:
+ - drug_encoder: cnn
+ - protein_encoder: cnn
+ - decoder: concat_mlp
diff --git a/configs/model/predictor/decoder/concat_mlp.yaml b/configs/model/predictor/decoder/concat_mlp.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..17b92c9493a45d51b3463dc069a73295fc22eb4f
--- /dev/null
+++ b/configs/model/predictor/decoder/concat_mlp.yaml
@@ -0,0 +1,6 @@
+_target_: deepscreen.models.components.mlp.ConcatMLP
+
+input_channels: ${eval:${model.drug_encoder.out_channels}+${model.protein_encoder.out_channels}}
+out_channels: 512
+hidden_channels: [1024,1024]
+dropout: 0.1
\ No newline at end of file
diff --git a/configs/model/predictor/decoder/mlp_deepdta.yaml b/configs/model/predictor/decoder/mlp_deepdta.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b6ee28076224a373a6879ece9296491dccfd280c
--- /dev/null
+++ b/configs/model/predictor/decoder/mlp_deepdta.yaml
@@ -0,0 +1,6 @@
+_target_: deepscreen.models.components.mlp.MLP2
+
+input_channels: ${eval:${model.drug_encoder.out_channels}+${model.protein_encoder.out_channels}}
+out_channels: 1
+hidden_channels: [1024,1024,512]
+dropout: 0.1
\ No newline at end of file
diff --git a/configs/model/predictor/decoder/mlp_lazy.yaml b/configs/model/predictor/decoder/mlp_lazy.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..832863817e37fed6e5ef54eba4de99341310c4dc
--- /dev/null
+++ b/configs/model/predictor/decoder/mlp_lazy.yaml
@@ -0,0 +1,5 @@
+_target_: deepscreen.models.components.mlp.LazyMLP
+
+out_channels: 1
+hidden_channels: [1024,1024,512]
+dropout: 0.1
\ No newline at end of file
diff --git a/configs/model/predictor/deep_dta.yaml b/configs/model/predictor/deep_dta.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7f6cfc42a5e850554e0825d803f79575abdcdb1b
--- /dev/null
+++ b/configs/model/predictor/deep_dta.yaml
@@ -0,0 +1,15 @@
+_target_: deepscreen.models.predictors.deep_dta.DeepDTA
+
+defaults:
+ - drug_encoder@drug_cnn: cnn
+ - protein_encoder@protein_cnn: cnn
+# - /model/decoder@fc: concat_mlp
+
+num_features_drug: 63
+num_features_protein: 26
+embed_dim: 128
+
+drug_cnn:
+ in_channels: ${data.drug_featurizer.max_sequence_length}
+protein_cnn:
+ in_channels: ${data.protein_featurizer.max_sequence_length}
\ No newline at end of file
diff --git a/configs/model/predictor/drug_encoder/cnn.yaml b/configs/model/predictor/drug_encoder/cnn.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..453ef99dec6c2a5db821181e72eef24c9faab966
--- /dev/null
+++ b/configs/model/predictor/drug_encoder/cnn.yaml
@@ -0,0 +1,9 @@
+_target_: deepscreen.models.components.cnn.CNN
+
+max_sequence_length: ${data.drug_featurizer.max_sequence_length}
+filters: [32, 64, 96]
+kernels: [4, 6, 8]
+in_channels: ${data.drug_featurizer.in_channels}
+out_channels: 256
+
+# TODO refactor the in_channels argument pipeline to be more reasonable
\ No newline at end of file
diff --git a/configs/model/predictor/drug_encoder/cnn_deepdta.yaml b/configs/model/predictor/drug_encoder/cnn_deepdta.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..97bf3a4870e224b2bc7a5eab946d38b57c279d26
--- /dev/null
+++ b/configs/model/predictor/drug_encoder/cnn_deepdta.yaml
@@ -0,0 +1,7 @@
+_target_: deepscreen.models.components.cnn_deepdta.CNN_DeepDTA
+
+max_sequence_length: ${data.drug_featurizer.max_sequence_length}
+filters: [32, 64, 96]
+kernels: [4, 6, 8]
+in_channels: ${data.drug_featurizer.in_channels}
+out_channels: 128
\ No newline at end of file
diff --git a/configs/model/predictor/drug_encoder/gat.yaml b/configs/model/predictor/drug_encoder/gat.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3b8dddd5ab5fac2b3cefff8191c01d3ab393c8a5
--- /dev/null
+++ b/configs/model/predictor/drug_encoder/gat.yaml
@@ -0,0 +1,5 @@
+_target_: deepscreen.models.components.gat.GAT
+
+num_features: 78
+out_channels: 128
+dropout: 0.2
\ No newline at end of file
diff --git a/configs/model/predictor/drug_encoder/gcn.yaml b/configs/model/predictor/drug_encoder/gcn.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5e2da337b324610297bd76b6783340d03bd681a8
--- /dev/null
+++ b/configs/model/predictor/drug_encoder/gcn.yaml
@@ -0,0 +1,5 @@
+_target_: deepscreen.models.components.gcn.GCN
+
+num_features: 78
+out_channels: 128
+dropout: 0.2
\ No newline at end of file
diff --git a/configs/model/predictor/drug_encoder/gin.yaml b/configs/model/predictor/drug_encoder/gin.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..caf5820c158ff7956b2440d25f7b5f901936f683
--- /dev/null
+++ b/configs/model/predictor/drug_encoder/gin.yaml
@@ -0,0 +1,5 @@
+_target_: deepscreen.models.components.gin.GIN
+
+num_features: 78
+out_channels: 128
+dropout: 0.2
\ No newline at end of file
diff --git a/configs/model/predictor/drug_encoder/lstm.yaml b/configs/model/predictor/drug_encoder/lstm.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/configs/model/predictor/drug_encoder/transformer.yaml b/configs/model/predictor/drug_encoder/transformer.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5eee1571675bcb2d2d539dbaa69ea0268c7b3908
--- /dev/null
+++ b/configs/model/predictor/drug_encoder/transformer.yaml
@@ -0,0 +1,11 @@
+_target_: deepscreen.models.components.transformer
+
+input_dim: 1024
+emb_size: 128
+max_position_size: 50
+dropout: 0.1
+n_layer: 8
+intermediate_size: 512
+num_attention_heads: 8
+attention_probs_dropout: 0.1
+hidden_dropout: 0.1
\ No newline at end of file
diff --git a/configs/model/predictor/drug_vqa.yaml b/configs/model/predictor/drug_vqa.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8ee31b6b3e5a62bec7998d3bdb4c4619ecd27e7f
--- /dev/null
+++ b/configs/model/predictor/drug_vqa.yaml
@@ -0,0 +1,15 @@
+_target_: deepscreen.models.predictors.drug_vqa.DrugVQA
+
+conv_dim: 1
+lstm_hid_dim: 64
+d_a: 32
+r: 10
+n_chars_smi: 247
+n_chars_seq: 21
+dropout: 0.2
+in_channels: 8
+cnn_channels: 32
+cnn_layers: 4
+emb_dim: 30
+dense_hid: 64
+
diff --git a/configs/model/predictor/graph_dta.yaml b/configs/model/predictor/graph_dta.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..35bfdf8adc5ab69b780e81c828a195e48e8ec1a0
--- /dev/null
+++ b/configs/model/predictor/graph_dta.yaml
@@ -0,0 +1,16 @@
+defaults:
+ - drug_encoder@gnn: gat
+ - _self_
+
+_target_: deepscreen.models.predictors.graph_dta.GraphDTA
+
+gnn:
+ num_features: 34
+ out_channels: 128
+ dropout: 0.2
+
+num_features_protein: 26
+n_filters: 32
+embed_dim: 128
+output_dim: 128
+dropout: 0.2
diff --git a/configs/model/predictor/hyper_attention_dti.yaml b/configs/model/predictor/hyper_attention_dti.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..87dde659688073d247953fce5ceef65a38786789
--- /dev/null
+++ b/configs/model/predictor/hyper_attention_dti.yaml
@@ -0,0 +1,8 @@
+_target_: deepscreen.models.predictors.hyper_attention_dti.HyperAttentionDTI
+
+protein_kernel: [4,8,12]
+drug_kernel: [4,6,8]
+conv: 40
+char_dim: 64
+protein_max_len: ${data.protein_featurizer.max_sequence_length}
+drug_max_len: ${data.drug_featurizer.max_sequence_length}
\ No newline at end of file
diff --git a/configs/model/predictor/none.yaml b/configs/model/predictor/none.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/configs/model/predictor/protein_encoder/cnn.yaml b/configs/model/predictor/protein_encoder/cnn.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9363e5e6130a5292d173b90eba8430c12edef3d1
--- /dev/null
+++ b/configs/model/predictor/protein_encoder/cnn.yaml
@@ -0,0 +1,7 @@
+_target_: deepscreen.models.components.cnn.CNN
+
+max_sequence_length: ${data.protein_featurizer.max_sequence_length}
+filters: [32, 64, 96]
+kernels: [4, 8, 12]
+in_channels: ${data.protein_featurizer.in_channels}
+out_channels: 256
\ No newline at end of file
diff --git a/configs/model/predictor/protein_encoder/cnn_deepdta.yaml b/configs/model/predictor/protein_encoder/cnn_deepdta.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8ac5f6d9064695cdfe2739946c1e55d55aa588d5
--- /dev/null
+++ b/configs/model/predictor/protein_encoder/cnn_deepdta.yaml
@@ -0,0 +1,7 @@
+_target_: deepscreen.models.components.cnn_deepdta.CNN_DeepDTA
+
+max_sequence_length: ${data.protein_featurizer.max_sequence_length}
+filters: [32, 64, 96]
+kernels: [4, 8, 12]
+in_channels: ${data.protein_featurizer.in_channels}
+out_channels: 128
\ No newline at end of file
diff --git a/configs/model/predictor/protein_encoder/lstm.yaml b/configs/model/predictor/protein_encoder/lstm.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/configs/model/predictor/protein_encoder/tape_bert.yaml b/configs/model/predictor/protein_encoder/tape_bert.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6a64b46c70850a5d796a19615ba553e69cb26a39
--- /dev/null
+++ b/configs/model/predictor/protein_encoder/tape_bert.yaml
@@ -0,0 +1,3 @@
+_target_: tape.ProteinBertModel.from_pretrained
+
+pretrained_model_name_or_path: bert-base
\ No newline at end of file
diff --git a/configs/model/predictor/protein_encoder/transformer.yaml b/configs/model/predictor/protein_encoder/transformer.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..4fb7c9761bc098da84773f56bc949ce7c7d34c5f
--- /dev/null
+++ b/configs/model/predictor/protein_encoder/transformer.yaml
@@ -0,0 +1,12 @@
+_target_: deepscreen.models.components.transformer
+
+input_dim: 8420
+emb_size: 64
+max_position_size: 545 50
+dropout: 0.1
+n_layer: 2
+intermediate_size: 256
+num_attention_heads: 4
+attention_probs_dropout: 0.1
+hidden_dropout: 0.1
+
diff --git a/configs/model/predictor/transformer_cpi.yaml b/configs/model/predictor/transformer_cpi.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c7d137ba108186e77e67878c4ad6ba40dbad52d3
--- /dev/null
+++ b/configs/model/predictor/transformer_cpi.yaml
@@ -0,0 +1,10 @@
+_target_: deepscreen.models.predictors.transformer_cpi.TransformerCPI
+
+protein_dim: 100
+atom_dim: 34
+hid_dim: 64
+n_layers: 3
+n_heads: 8
+pf_dim: 256
+dropout: 0.1
+kernel_size: 7
\ No newline at end of file
diff --git a/configs/model/predictor/transformer_cpi_2.yaml b/configs/model/predictor/transformer_cpi_2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2f9439a99c02720650635f2024acb36ef45b18fa
--- /dev/null
+++ b/configs/model/predictor/transformer_cpi_2.yaml
@@ -0,0 +1,14 @@
+_target_: deepscreen.models.predictors.transformer_cpi_2.TransformerCPI2
+
+encoder:
+ _target_: deepscreen.models.predictors.transformer_cpi_2.Encoder
+ # /model/protein_encoder@pretrain: tape_bert
+ n_layers: 3
+ pretrain:
+ _target_: tape.ProteinBertModel.from_pretrained
+ pretrained_model_name_or_path: bert-base
+
+decoder:
+ _target_: deepscreen.models.predictors.transformer_cpi_2.Decoder
+ n_layers: 3
+ dropout: 0.1
diff --git a/configs/model/scheduler/default.yaml b/configs/model/scheduler/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a319ea4099d233cec67de9477bbce25744368a91
--- /dev/null
+++ b/configs/model/scheduler/default.yaml
@@ -0,0 +1,11 @@
+scheduler:
+ _target_: torch.optim.lr_scheduler.ReduceLROnPlateau
+ _partial_: true
+
+ mode: min
+ factor: 0.1
+ patience: 10
+
+monitor: ${oc.select:callbacks.model_checkpoint.monitor,"val/loss"}
+interval: "epoch"
+frequency: 1
\ No newline at end of file
diff --git a/configs/model/scheduler/none.yaml b/configs/model/scheduler/none.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/configs/model/scheduler/reduce_lr_on_plateau.yaml b/configs/model/scheduler/reduce_lr_on_plateau.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..221efd82d131473c6a51da966d153bb2ec85b69c
--- /dev/null
+++ b/configs/model/scheduler/reduce_lr_on_plateau.yaml
@@ -0,0 +1,6 @@
+_target_: torch.optim.lr_scheduler.ReduceLROnPlateau
+_partial_: true
+
+mode: min
+factor: 0.1
+patience: 10
diff --git a/configs/paths/default.yaml b/configs/paths/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..862261ca7797da591715936cfd88a047c1722da3
--- /dev/null
+++ b/configs/paths/default.yaml
@@ -0,0 +1,19 @@
+# path to root directory
+root_dir: .
+
+# path to data directory
+data_dir: ${paths.root_dir}/data
+
+# path to log directory
+log_dir: ${paths.root_dir}/logs
+
+# path to resource directory
+resource_dir: ${paths.root_dir}/resources
+
+# path to output directory, created dynamically by hydra
+# path generation pattern is specified in `configs/hydra/default.yaml`
+# use it to store all files generated during the run, like ckpts and metrics
+output_dir: ${hydra:runtime.output_dir}
+
+# path to working directory
+work_dir: ${hydra:runtime.cwd}
diff --git a/configs/predict.yaml b/configs/predict.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2626280078af1ed4a3160e07f344afdef509f327
--- /dev/null
+++ b/configs/predict.yaml
@@ -0,0 +1,27 @@
+# @package _global_
+
+defaults:
+ - model: dti_model # fixed for web server version
+ - task: null
+ - data: dti_data # fixed for web server version
+ - callbacks:
+ - model_summary
+ - rich_progress_bar
+ - csv_prediction_writer
+ - trainer: default
+ - paths: default
+ - extras: default
+ - hydra: default
+ - _self_
+ - preset: null
+ - experiment: null
+ - sweep: null
+ - debug: null
+ - optional local: default
+
+job_name: "predict"
+
+tags: ???
+
+# passing checkpoint path is necessary for prediction
+ckpt_path: ???
diff --git a/configs/preset/bacpi.yaml b/configs/preset/bacpi.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9dbea130fd0f6b6fe07f009c340f33f6f8f6c558
--- /dev/null
+++ b/configs/preset/bacpi.yaml
@@ -0,0 +1,37 @@
+# @package _global_
+model:
+ predictor:
+ _target_: deepscreen.models.predictors.bacpi.BACPI
+
+ n_atom: 20480
+ n_amino: 8448
+ comp_dim: 80
+ prot_dim: 80
+ latent_dim: 80
+ gat_dim: 50
+ num_head: 3
+ dropout: 0.1
+ alpha: 0.1
+ window: 5
+ layer_cnn: 3
+ optimizer:
+ lr: 5e-4
+
+data:
+ batch_size: 16
+
+ collator:
+ automatic_padding: True
+
+ drug_featurizer:
+ _target_: deepscreen.models.predictors.bacpi.drug_featurizer
+ _partial_: true
+ radius: 2
+
+ protein_featurizer:
+ _target_: deepscreen.models.predictors.bacpi.split_sequence
+ _partial_: true
+ ngram: 3
+# collator:
+# _target_: deepscreen.models.predictors.transformer_cpi_2.pack
+# _partial_: true
diff --git a/configs/preset/coa_dti_pro.yaml b/configs/preset/coa_dti_pro.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9ad501a6176a630b8edb97653607ef223946367d
--- /dev/null
+++ b/configs/preset/coa_dti_pro.yaml
@@ -0,0 +1,28 @@
+# @package _global_
+defaults:
+ - override /data/protein_featurizer: none
+
+model:
+ predictor:
+ _target_: deepscreen.models.predictors.coa_dti_pro.CoaDTIPro
+
+ n_fingerprint: 20480
+ n_word: 26
+ dim: 512
+ layer_output: 3
+ layer_coa: 1
+ nhead: 8
+ dropout: 0.1
+ co_attention: 'inter'
+ gcn_pooling: False
+
+ esm_model_and_alphabet:
+ _target_: esm.pretrained.load_model_and_alphabet
+ model_name: resources/models/esm/esm1_t6_43M_UR50S.pt
+
+data:
+ drug_featurizer:
+ _target_: deepscreen.models.predictors.coa_dti_pro.drug_featurizer
+ _partial_: true
+ radius: 2
+ batch_size: 1
diff --git a/configs/preset/deep_conv_dti.yaml b/configs/preset/deep_conv_dti.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..025b151cf1539205f245feb41f9fd60f133c4497
--- /dev/null
+++ b/configs/preset/deep_conv_dti.yaml
@@ -0,0 +1,23 @@
+# @package _global_
+defaults:
+ - override /data/drug_featurizer: ecfp
+ - override /data/protein_featurizer: label
+
+model:
+ predictor:
+ _target_: deepscreen.models.predictors.deep_conv_dti.DeepConvDTI
+
+ activation:
+ _target_: torch.nn.ELU
+
+ dropout: 0.0
+ drug_layers: [512, 128]
+ protein_windows: [10, 15, 20, 25, 30]
+ n_filters: 128
+ decay: 0.0001
+ convolution: true
+ protein_layers: [128,]
+ fc_layers: [128,]
+
+data:
+ batch_size: 512
diff --git a/configs/preset/deep_dta.yaml b/configs/preset/deep_dta.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f72d5c942226d88e798cb6a2fd6fef080df69053
--- /dev/null
+++ b/configs/preset/deep_dta.yaml
@@ -0,0 +1,8 @@
+# @package _global_
+defaults:
+ - override /data/drug_featurizer: label
+ - override /data/protein_featurizer: label
+ - override /model/predictor: deep_dta
+
+data:
+ batch_size: 512
diff --git a/configs/preset/deep_dtaf.yaml b/configs/preset/deep_dtaf.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a2f2a8d143d28834b4c94e300c463bc776e23adf
--- /dev/null
+++ b/configs/preset/deep_dtaf.yaml
@@ -0,0 +1,19 @@
+# @package _global_
+defaults:
+ - override /data/drug_featurizer: label
+ - override /data/protein_featurizer: label
+ - override /model/predictor: deep_dta
+
+data:
+ drug_featurizer:
+ charset: {'Z', 'Y', 'H', '[', 'O', ']', '5', 'M', 'K', '.', '9', 'e',
+ '(', 'l', 'U', 'V', 'L', 'B', 'y', 'm', 'd', 'h', 'T', 'A',
+ 'W', 'b', 'i', 'D', 'R', '8', '/', 's', '#', 'u', '+', '@',
+ 'n', '%', 'F', 'r', 't', 'I', 'S', '6', 'P', 'G', 'f', ')',
+ '-', '\\', 'C', 'E', 'o', '3', '2', '1', '=', 'g', 'c', 'N',
+ '7', '4', 'a', '0']
+ batch_size: 512
+
+model:
+ predictor:
+ smi_charset_len: ${eval:'len(${data.protein_featurizer.charset})+1'}
diff --git a/configs/preset/drug_ban.yaml b/configs/preset/drug_ban.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..537c9d79276383542e956ff4ef27178c4c358fc4
--- /dev/null
+++ b/configs/preset/drug_ban.yaml
@@ -0,0 +1,28 @@
+# @package _global_
+defaults:
+ - override /data/protein_featurizer: label
+
+model:
+ predictor:
+ _target_: deepscreen.models.predictors.drug_ban.DrugBAN
+
+ drug_in_feats: 75
+ drug_embedding: 128
+ drug_hidden_feats: [128, 128, 128]
+ drug_padding: True
+ protein_emb_dim: 128
+ num_filters: [128, 128, 128]
+ kernel_size: [3, 6, 9]
+ protein_padding: True
+ mlp_in_dim: 256
+ mlp_hidden_dim: 512
+ mlp_out_dim: 128
+ ban_heads: 2
+
+data:
+ drug_featurizer:
+ _target_: deepscreen.models.predictors.drug_ban.drug_featurizer
+ _partial_: true
+ max_drug_nodes: 330
+
+ batch_size: 512
diff --git a/configs/preset/drug_vqa.yaml b/configs/preset/drug_vqa.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..26361c45c25c936a1d9c9b28942343ba7a96a127
--- /dev/null
+++ b/configs/preset/drug_vqa.yaml
@@ -0,0 +1,21 @@
+# @package _global_
+defaults:
+ - override /data/drug_featurizer: tokenizer
+ - override /data/protein_featurizer: label
+ - override /model/predictor: drug_vqa
+
+model:
+ loss:
+ _target_: deepscreen.models.loss.multitask_loss.MultitaskWeightedLoss
+ loss_fns:
+ - ${task.loss}
+ - _target_: deepscreen.models.predictors.drug_vqa.AttentionL2Regularization
+ weights: [1, 0.001]
+
+data:
+ batch_size: 512
+ drug_featurizer:
+ tokenizer:
+ _target_: deepscreen.data.featurizers.token.SmilesTokenizer
+ vocab_file: resources/vocabs/drug_vqa/combinedVoc-wholeFour.voc
+ regex_pattern: '(\[[^\[\]]{1,10}\])'
\ No newline at end of file
diff --git a/configs/preset/graph_dta.yaml b/configs/preset/graph_dta.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..159cb4e619f252663dccda18294600602674e01d
--- /dev/null
+++ b/configs/preset/graph_dta.yaml
@@ -0,0 +1,8 @@
+# @package _global_
+defaults:
+ - override /data/drug_featurizer: graph
+ - override /data/protein_featurizer: label
+ - override /model/predictor: graph_dta
+
+data:
+ batch_size: 512
\ No newline at end of file
diff --git a/configs/preset/hyper_attention_dti.yaml b/configs/preset/hyper_attention_dti.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ae63593517d0ce34310ba3f0dbf1201f66e7e3a5
--- /dev/null
+++ b/configs/preset/hyper_attention_dti.yaml
@@ -0,0 +1,8 @@
+# @package _global_
+defaults:
+ - override /data/drug_featurizer: label
+ - override /data/protein_featurizer: label
+ - override /model/predictor: hyper_attention_dti
+
+data:
+ batch_size: 32
\ No newline at end of file
diff --git a/configs/preset/m_graph_dta.yaml b/configs/preset/m_graph_dta.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8609b499b94168c7c5da2b5779b310be5c769020
--- /dev/null
+++ b/configs/preset/m_graph_dta.yaml
@@ -0,0 +1,22 @@
+# @package _global_
+defaults:
+ - override /data/drug_featurizer: graph
+ - override /data/protein_featurizer: label
+
+model:
+ predictor:
+ _target_: deepscreen.models.predictors.m_graph_dta.MGraphDTA
+ block_num: 3
+ vocab_protein_size: ${eval:'len(${data.protein_featurizer.charset})+1'}
+ embedding_size: 128
+ filter_num: 32
+
+data:
+ drug_featurizer:
+ atom_features:
+ _target_: deepscreen.models.predictors.m_graph_dta.atom_features
+ _partial_: true
+ batch_size: 512
+
+trainer:
+ precision: 'bf16'
\ No newline at end of file
diff --git a/configs/preset/mol_trans.yaml b/configs/preset/mol_trans.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ed7fcf5ce9d43b0e047b9720cb544faf707fb64f
--- /dev/null
+++ b/configs/preset/mol_trans.yaml
@@ -0,0 +1,39 @@
+# @package _global_
+defaults:
+ - override /data/drug_featurizer: fcs
+ - override /data/protein_featurizer: fcs
+
+data:
+ batch_size: 16
+ drug_featurizer:
+ max_sequence_length: 205
+ protein_featurizer:
+ max_sequence_length: 545
+
+model:
+ predictor:
+ _target_: deepscreen.models.predictors.mol_trans.MolTrans
+
+ input_dim_drug: 23532
+ input_dim_target: 16693
+ max_drug_seq: ${data.drug_featurizer.max_sequence_length}
+ max_protein_seq: ${data.protein_featurizer.max_sequence_length}
+ emb_size: 384
+ dropout_rate: 0.1
+
+ # DenseNet
+ scale_down_ratio: 0.25
+ growth_rate: 20
+ transition_rate: 0.5
+ num_dense_blocks: 4
+ kernal_dense_size: 3
+
+ # Encoder
+ intermediate_size: 1536
+ num_attention_heads: 12
+ attention_probs_dropout_prob: 0.1
+ hidden_dropout_prob: 0.1
+ #flatten_dim: 293412
+
+ optimizer:
+ lr: 1e-6
\ No newline at end of file
diff --git a/configs/preset/monn.yaml b/configs/preset/monn.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..117031aaf9f51f3f935c0a679af40883474b7880
--- /dev/null
+++ b/configs/preset/monn.yaml
@@ -0,0 +1,17 @@
+# @package _global_
+defaults:
+ - dti_experiment
+ # TODO MONN featurizers not fully implemented yet
+ - override /data/drug_featurizer: label
+ - override /data/protein_featurizer: label
+ - override /model/predictor: monn
+ - override /task: binary
+ - _self_
+
+model:
+ loss:
+ _target_: deepscreen.models.loss.multitask_loss.MultitaskWeightedLoss
+ loss_fns:
+ - _target_: ${model.loss}
+ - _target_: deepscreen.models.predictors.monn.MaskedBCELoss
+ weights: [1, 0.1]
diff --git a/configs/preset/transformer_cpi_2.yaml b/configs/preset/transformer_cpi_2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e1c224f88a6de1266ee4d836c15ff5e88b06f119
--- /dev/null
+++ b/configs/preset/transformer_cpi_2.yaml
@@ -0,0 +1,35 @@
+# @package _global_
+defaults:
+ - override /data/drug_featurizer: mol_features
+ - override /data/protein_featurizer: tokenizer
+
+model:
+ predictor:
+ _target_: deepscreen.models.predictors.transformer_cpi_2.TransformerCPI2
+
+ encoder:
+ _target_: deepscreen.models.predictors.transformer_cpi_2.Encoder
+ # /model/protein_encoder@pretrain: tape_bert
+ n_layers: 3
+ pretrain:
+ _target_: tape.ProteinBertModel.from_pretrained
+ pretrained_model_name_or_path: resources/models/tape/bert-base/ # bert-base
+
+ decoder:
+ _target_: deepscreen.models.predictors.transformer_cpi_2.Decoder
+ n_layers: 3
+ dropout: 0.1
+
+data:
+ batch_size: 16
+ collator:
+ automatic_padding: True
+
+ protein_featurizer:
+ tokenizer:
+ _target_: tape.TAPETokenizer.from_pretrained
+ vocab: iupac
+
+# collator:
+# _target_: deepscreen.models.predictors.transformer_cpi_2.pack
+# _partial_: true
diff --git a/configs/sweep/chembl33_benchmark.yaml b/configs/sweep/chembl33_benchmark.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b18bcbb0656411aa393181a5bfd79663d144dc59
--- /dev/null
+++ b/configs/sweep/chembl33_benchmark.yaml
@@ -0,0 +1,6 @@
+# @package _global_
+tags: ['sweep', 'benchmark']
+hydra:
+ sweeper:
+ params:
+ experiment: kinase,membrane_receptors,non_kinase_enzymes #,ion_channels,nuclear_receptors,other_protein_targets
\ No newline at end of file
diff --git a/configs/sweep/ddp_multirun.yaml b/configs/sweep/ddp_multirun.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..fbf6641a20019ae41cf8d231d01433b2562a9c77
--- /dev/null
+++ b/configs/sweep/ddp_multirun.yaml
@@ -0,0 +1,6 @@
+# @package _global_
+hydra:
+ sweeper:
+ params:
+ preset: graph_dta,deep_dta # drug_vqa,mol_trans,hyper_attention_dti,transformer_cpi_2
+ experiment: kiba,davis,bindingdb
\ No newline at end of file
diff --git a/configs/sweep/dti_benchmark.yaml b/configs/sweep/dti_benchmark.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b404a86d52131cbde1d7b877be92a80d8000d5b8
--- /dev/null
+++ b/configs/sweep/dti_benchmark.yaml
@@ -0,0 +1,8 @@
+# @package _global_
+tags: ['sweep', 'benchmark']
+hydra:
+ sweeper:
+ params:
+ preset: transformer_cpi_2 #graph_dta,deep_dta #,mol_trans,hyper_attention_dti,m_graph_dta
+ experiment: other_protein_targets
+
diff --git a/configs/sweep/example_multirun.yaml b/configs/sweep/example_multirun.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b2d7edad480a88165ca51475c7fd076110d2fef0
--- /dev/null
+++ b/configs/sweep/example_multirun.yaml
@@ -0,0 +1,6 @@
+# @package _global_
+hydra:
+ sweeper:
+ params:
+ preset: graph_dta,deep_dta
+ experiment: chembl_random
diff --git a/configs/sweep/example_multirun_test.yaml b/configs/sweep/example_multirun_test.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a609000341c88314547dfe300ec59e059c0d9825
--- /dev/null
+++ b/configs/sweep/example_multirun_test.yaml
@@ -0,0 +1,7 @@
+# @package _global_
+hydra:
+ sweeper:
+ params:
+# ckpt_path: "'C:/Users/libok/Documents/GitHub/DeepScreen/logs/train/runs/2023-11-07_23-21-55-442205_[debug]/checkpoints/epoch_000.ckpt','C:/Users/libok/Documents/GitHub/DeepScreen/logs/train/runs/2023-11-07_19-46-08-740035_[debug]/checkpoints/epoch_000.ckpt'"
+ data.data_file: dti_benchmark/random_split_update/davis_reserved_test.csv,dti_benchmark/random_split_update/kiba_reserved_test.csv
+ ckpt_path: "'C:/Users/libok/Documents/GitHub/DeepScreen/logs/test/multiruns/2023-11-10_10-31-15-339335_[multirun,test,dev]/metrics_summary.csv'"
\ No newline at end of file
diff --git a/configs/sweep/example_submitit.yaml b/configs/sweep/example_submitit.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..510522a9cb855b9afd2ac3ebd6a0f50baea93459
--- /dev/null
+++ b/configs/sweep/example_submitit.yaml
@@ -0,0 +1,17 @@
+# @package _global_
+defaults:
+ - basic
+ - override /hydra/launcher: submitit_local
+
+hydra:
+ sweeper:
+ params:
+ preset: graph_dta,deep_dta
+ experiment: chembl_random
+ launcher:
+ timeout_min: 60
+ cpus_per_task: 1
+ gpus_per_node: 1
+ tasks_per_node: 4
+ mem_gb: 32
+ nodes: 1
\ No newline at end of file
diff --git a/configs/sweep/experiment_summary.csv b/configs/sweep/experiment_summary.csv
new file mode 100644
index 0000000000000000000000000000000000000000..73e924007e4c3936dadc918f7cece50574336c3e
--- /dev/null
+++ b/configs/sweep/experiment_summary.csv
@@ -0,0 +1,21 @@
+test/loss,test/auprc,test/auroc,test/f1_score,test/precision,test/recall,test/sensitivity,test/specificity,ckpt_path,job_status,preset,experiment,sweep,tags,model.optimizer.lr,local,data.batch_size
+0.1525115370750427,0.735183835029602,0.9363504648208618,0.661261260509491,0.7816826701164246,0.5729898810386658,0.5729898810386658,0.984080135822296,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-16_14-53-13-645337_[benchmark,ddta,gdta]/experiment=kiba;preset=deep_dta;sweep=slurm_test;tags=[benchmark,ddta,gdta]/checkpoints/epoch_041.ckpt",COMPLETED,deep_dta,kiba,slurm_test,"[benchmark,ddta,gdta]",,,
+0.193635880947113,0.4078962802886963,0.8581838607788086,0.3916666805744171,0.5371428728103638,0.3081967234611511,0.3081967234611511,0.9788622260093688,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-16_14-53-13-645337_[benchmark,ddta,gdta]/experiment=davis;preset=deep_dta;sweep=slurm_test;tags=[benchmark,ddta,gdta]/checkpoints/epoch_008.ckpt",COMPLETED,deep_dta,davis,slurm_test,"[benchmark,ddta,gdta]",,,
+0.5104637742042542,0.7619075775146484,0.8163812756538391,0.6808292269706726,0.6954700350761414,0.6667922139167786,0.6667922139167786,0.789406955242157,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-16_14-53-13-645337_[benchmark,ddta,gdta]/experiment=bindingdb;preset=deep_dta;sweep=slurm_test;tags=[benchmark,ddta,gdta]/checkpoints/epoch_003.ckpt",COMPLETED,deep_dta,bindingdb,slurm_test,"[benchmark,ddta,gdta]",,,
+0.2221491634845733,0.3560383319854736,0.78690105676651,0.2659846544265747,0.604651153087616,0.1704917997121811,0.1704917997121811,0.9911273717880248,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-16_14-53-13-645337_[benchmark,ddta,gdta]/experiment=davis;preset=graph_dta;sweep=slurm_test;tags=[benchmark,ddta,gdta]/checkpoints/epoch_054.ckpt",COMPLETED,graph_dta,davis,slurm_test,"[benchmark,ddta,gdta]",,,
+0.2281630784273147,0.4483628869056701,0.8383051156997681,0.2730720639228821,0.7176079750061035,0.1686182618141174,0.1686182618141174,0.9933990836143494,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-16_14-53-13-645337_[benchmark,ddta,gdta]/experiment=kiba;preset=graph_dta;sweep=slurm_test;tags=[benchmark,ddta,gdta]/checkpoints/epoch_065.ckpt",COMPLETED,graph_dta,kiba,slurm_test,"[benchmark,ddta,gdta]",,,
+0.4569543302059173,0.828099250793457,0.8623664379119873,0.7299983501434326,0.7756586670875549,0.6894148588180542,0.6894148588180542,0.8561919927597046,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-16_14-53-13-645337_[benchmark,ddta,gdta]/experiment=bindingdb;preset=graph_dta;sweep=slurm_test;tags=[benchmark,ddta,gdta]/checkpoints/epoch_034.ckpt",COMPLETED,graph_dta,bindingdb,slurm_test,"[benchmark,ddta,gdta]",,,
+0.1881016194820404,0.5213174819946289,0.8876519203186035,0.5207100510597229,0.6534653306007385,0.4327868819236755,0.4327868819236755,0.9817327857017516,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-16_18-08-40-028557_[sweep,benchmark]/experiment=davis;preset=hyper_attention_dti;sweep=slurm_test/checkpoints/epoch_017.ckpt",COMPLETED,hyper_attention_dti,davis,slurm_test,,,,
+0.144176036119461,0.7704265117645264,0.9501243829727172,0.6897732019424438,0.7632575631141663,0.6291959285736084,0.6291959285736084,0.980585515499115,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-16_18-08-40-028557_[sweep,benchmark]/experiment=kiba;preset=hyper_attention_dti;sweep=slurm_test/checkpoints/epoch_014.ckpt",COMPLETED,hyper_attention_dti,kiba,slurm_test,,,,
+0.3445079922676086,0.915877878665924,0.934882640838623,0.8371888399124146,0.8385065793991089,0.8358752131462097,0.8358752131462097,0.8838841319084167,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-16_18-08-40-028557_[sweep,benchmark]/experiment=bindingdb;preset=hyper_attention_dti;sweep=slurm_test/checkpoints/epoch_017.ckpt",COMPLETED,hyper_attention_dti,bindingdb,slurm_test,,,,
+0.3915603458881378,0.3016493022441864,0.7910888195037842,0.085626907646656,0.6363636255264282,0.0459016375243663,0.0459016375243663,0.9979122877120972,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-20_18-50-02-680654_[mol_trans,benchmark]/experiment=davis;preset=mol_trans;sweep=slurm_test/checkpoints/epoch_000.ckpt",COMPLETED,mol_trans,davis,slurm_test,,,,
+0.1662539541721344,0.7397991418838501,0.9369943141937256,0.6784178614616394,0.7550238966941833,0.6159250736236572,0.6159250736236572,0.9801195859909058,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-20_18-50-02-680654_[mol_trans,benchmark]/experiment=kiba;preset=mol_trans;sweep=slurm_test/checkpoints/epoch_008.ckpt",COMPLETED,mol_trans,kiba,slurm_test,,,,
+0.2160931378602981,0.3230183720588684,0.796424388885498,0.3271028101444244,0.5691056847572327,0.2295081913471222,0.2295081913471222,0.9861690998077391,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-20_20-20-11-193061_[tcpi2,kiba,davis]/experiment=davis;model.optimizer.lr=1e-06;preset=transformer_cpi_2;sweep=slurm_test;tags=[tcpi2,kiba,davis]/checkpoints/epoch_023.ckpt",COMPLETED,transformer_cpi_2,davis,slurm_test,"[tcpi2,kiba,davis]",1e-06,,
+0.2221459150314331,0.5159928798675537,0.8539965152740479,0.4716618657112121,0.612983763217926,0.3832943141460418,0.3832943141460418,0.9759260416030884,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-20_20-20-11-193061_[tcpi2,kiba,davis]/experiment=kiba;model.optimizer.lr=1e-06;preset=transformer_cpi_2;sweep=slurm_test;tags=[tcpi2,kiba,davis]/checkpoints/epoch_077.ckpt",COMPLETED,transformer_cpi_2,kiba,slurm_test,"[tcpi2,kiba,davis]",1e-06,,
+0.1689525544643402,0.5410239696502686,0.9028607606887816,0.4678111672401428,0.6770186424255371,0.3573770523071289,0.3573770523071289,0.986430048942566,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-20_21-25-54-050914_[deep_conv_dti,benchmark]/experiment=davis;local=gpu3090;preset=deep_conv_dti;tags=[deep_conv_dti,benchmark]/checkpoints/epoch_012.ckpt",COMPLETED,deep_conv_dti,davis,,"[deep_conv_dti,benchmark]",,gpu3090,
+0.1273866593837738,0.8186066746711731,0.95548677444458,0.7409326434135437,0.8289855122566223,0.6697892546653748,0.6697892546653748,0.9862545728683472,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-20_21-25-54-050914_[deep_conv_dti,benchmark]/experiment=kiba;local=gpu3090;preset=deep_conv_dti;tags=[deep_conv_dti,benchmark]/checkpoints/epoch_026.ckpt",COMPLETED,deep_conv_dti,kiba,,"[deep_conv_dti,benchmark]",,gpu3090,
+0.3221746683120727,0.9174473285675048,0.9374850988388062,0.8440826535224915,0.8421315550804138,0.846042811870575,0.846042811870575,0.8856043219566345,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-20_21-25-54-050914_[deep_conv_dti,benchmark]/experiment=bindingdb;local=gpu3090;preset=deep_conv_dti;tags=[deep_conv_dti,benchmark]/checkpoints/epoch_008.ckpt",COMPLETED,deep_conv_dti,bindingdb,,"[deep_conv_dti,benchmark]",,gpu3090,
+0.3808988630771637,0.9017271995544434,0.92254638671875,0.8203105330467224,0.818312406539917,0.822318434715271,0.822318434715271,0.8683114647865295,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-23_23-21-02-704802_[mol_trans,bindingdb]/experiment=bindingdb;local=gpu3090;preset=mol_trans;tags=[mol_trans,bindingdb]/checkpoints/epoch_064.ckpt",COMPLETED,mol_trans,bindingdb,,"[mol_trans,bindingdb]",,gpu3090,
+0.202189102768898,0.3614169955253601,0.8401678204536438,0.330232560634613,0.5680000185966492,0.2327868789434433,0.2327868789434433,0.9859081506729126,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-23_23-27-18-387556_[m_graph_dta,benchmark]/experiment=davis;local=gpu3090;preset=m_graph_dta;tags=[m_graph_dta,benchmark]/checkpoints/epoch_094.ckpt",COMPLETED,m_graph_dta,davis,,"[m_graph_dta,benchmark]",,gpu3090,
+0.1921116858720779,0.6082723736763,0.8893994092941284,0.4803767800331116,0.7285714149475098,0.3583138287067413,0.3583138287067413,0.9867205023765564,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-23_23-27-18-387556_[m_graph_dta,benchmark]/experiment=kiba;local=gpu3090;preset=m_graph_dta;tags=[m_graph_dta,benchmark]/checkpoints/epoch_246.ckpt",COMPLETED,m_graph_dta,kiba,,"[m_graph_dta,benchmark]",,gpu3090,
+0.6574236750602722,0.5444095730781555,0.6328268051147461,0.483252614736557,0.5203881859779358,0.4510641098022461,0.4510641098022461,0.7001991868019104,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-11-06_14-16-25-154180_[benchmark,tcpi2,bindingdb]/data.batch_size=32;experiment=bindingdb;preset=transformer_cpi_2;tags=[benchmark,tcpi2,bindingdb]/checkpoints/epoch_017.ckpt",COMPLETED,transformer_cpi_2,bindingdb,,"[benchmark,tcpi2,bindingdb]",,,32.0
diff --git a/configs/sweep/experiment_summary_bindingdb.csv b/configs/sweep/experiment_summary_bindingdb.csv
new file mode 100644
index 0000000000000000000000000000000000000000..b2a3ee1ab0192c82662c03cf59ed07bda14b1862
--- /dev/null
+++ b/configs/sweep/experiment_summary_bindingdb.csv
@@ -0,0 +1,7 @@
+test/loss,test/auprc,test/auroc,test/f1_score,test/precision,test/recall,test/sensitivity,test/specificity,ckpt_path,job_status,preset,experiment,sweep,tags,model.optimizer.lr,local,data.batch_size
+0.5104637742042542,0.7619075775146484,0.8163812756538391,0.6808292269706726,0.6954700350761414,0.6667922139167786,0.6667922139167786,0.789406955242157,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-16_14-53-13-645337_[benchmark,ddta,gdta]/experiment=bindingdb;preset=deep_dta;sweep=slurm_test;tags=[benchmark,ddta,gdta]/checkpoints/epoch_003.ckpt",COMPLETED,deep_dta,bindingdb,slurm_test,"[benchmark,ddta,gdta]",,,
+0.4569543302059173,0.828099250793457,0.8623664379119873,0.7299983501434326,0.7756586670875549,0.6894148588180542,0.6894148588180542,0.8561919927597046,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-16_14-53-13-645337_[benchmark,ddta,gdta]/experiment=bindingdb;preset=graph_dta;sweep=slurm_test;tags=[benchmark,ddta,gdta]/checkpoints/epoch_034.ckpt",COMPLETED,graph_dta,bindingdb,slurm_test,"[benchmark,ddta,gdta]",,,
+0.3445079922676086,0.915877878665924,0.934882640838623,0.8371888399124146,0.8385065793991089,0.8358752131462097,0.8358752131462097,0.8838841319084167,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-16_18-08-40-028557_[sweep,benchmark]/experiment=bindingdb;preset=hyper_attention_dti;sweep=slurm_test/checkpoints/epoch_017.ckpt",COMPLETED,hyper_attention_dti,bindingdb,slurm_test,,,,
+0.3221746683120727,0.9174473285675048,0.9374850988388062,0.8440826535224915,0.8421315550804138,0.846042811870575,0.846042811870575,0.8856043219566345,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-20_21-25-54-050914_[deep_conv_dti,benchmark]/experiment=bindingdb;local=gpu3090;preset=deep_conv_dti;tags=[deep_conv_dti,benchmark]/checkpoints/epoch_008.ckpt",COMPLETED,deep_conv_dti,bindingdb,,"[deep_conv_dti,benchmark]",,gpu3090,
+0.3808988630771637,0.9017271995544434,0.92254638671875,0.8203105330467224,0.818312406539917,0.822318434715271,0.822318434715271,0.8683114647865295,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-23_23-21-02-704802_[mol_trans,bindingdb]/experiment=bindingdb;local=gpu3090;preset=mol_trans;tags=[mol_trans,bindingdb]/checkpoints/epoch_064.ckpt",COMPLETED,mol_trans,bindingdb,,"[mol_trans,bindingdb]",,gpu3090,
+0.6574236750602722,0.5444095730781555,0.6328268051147461,0.483252614736557,0.5203881859779358,0.4510641098022461,0.4510641098022461,0.7001991868019104,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-11-06_14-16-25-154180_[benchmark,tcpi2,bindingdb]/data.batch_size=32;experiment=bindingdb;preset=transformer_cpi_2;tags=[benchmark,tcpi2,bindingdb]/checkpoints/epoch_017.ckpt",COMPLETED,transformer_cpi_2,bindingdb,,"[benchmark,tcpi2,bindingdb]",,,32.0
diff --git a/configs/sweep/experiment_summary_davis.csv b/configs/sweep/experiment_summary_davis.csv
new file mode 100644
index 0000000000000000000000000000000000000000..1cc9af384ea7c91cdc510d734862bbe56edc3f43
--- /dev/null
+++ b/configs/sweep/experiment_summary_davis.csv
@@ -0,0 +1,8 @@
+test/loss,test/auprc,test/auroc,test/f1_score,test/precision,test/recall,test/sensitivity,test/specificity,ckpt_path,job_status,preset,experiment,sweep,tags,model.optimizer.lr,local,data.batch_size
+0.193635880947113,0.4078962802886963,0.8581838607788086,0.3916666805744171,0.5371428728103638,0.3081967234611511,0.3081967234611511,0.9788622260093688,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-16_14-53-13-645337_[benchmark,ddta,gdta]/experiment=davis;preset=deep_dta;sweep=slurm_test;tags=[benchmark,ddta,gdta]/checkpoints/epoch_008.ckpt",COMPLETED,deep_dta,davis,slurm_test,"[benchmark,ddta,gdta]",,,
+0.2221491634845733,0.3560383319854736,0.78690105676651,0.2659846544265747,0.604651153087616,0.1704917997121811,0.1704917997121811,0.9911273717880248,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-16_14-53-13-645337_[benchmark,ddta,gdta]/experiment=davis;preset=graph_dta;sweep=slurm_test;tags=[benchmark,ddta,gdta]/checkpoints/epoch_054.ckpt",COMPLETED,graph_dta,davis,slurm_test,"[benchmark,ddta,gdta]",,,
+0.1881016194820404,0.5213174819946289,0.8876519203186035,0.5207100510597229,0.6534653306007385,0.4327868819236755,0.4327868819236755,0.9817327857017516,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-16_18-08-40-028557_[sweep,benchmark]/experiment=davis;preset=hyper_attention_dti;sweep=slurm_test/checkpoints/epoch_017.ckpt",COMPLETED,hyper_attention_dti,davis,slurm_test,,,,
+0.3915603458881378,0.3016493022441864,0.7910888195037842,0.085626907646656,0.6363636255264282,0.0459016375243663,0.0459016375243663,0.9979122877120972,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-20_18-50-02-680654_[mol_trans,benchmark]/experiment=davis;preset=mol_trans;sweep=slurm_test/checkpoints/epoch_000.ckpt",COMPLETED,mol_trans,davis,slurm_test,,,,
+0.2160931378602981,0.3230183720588684,0.796424388885498,0.3271028101444244,0.5691056847572327,0.2295081913471222,0.2295081913471222,0.9861690998077391,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-20_20-20-11-193061_[tcpi2,kiba,davis]/experiment=davis;model.optimizer.lr=1e-06;preset=transformer_cpi_2;sweep=slurm_test;tags=[tcpi2,kiba,davis]/checkpoints/epoch_023.ckpt",COMPLETED,transformer_cpi_2,davis,slurm_test,"[tcpi2,kiba,davis]",1e-06,,
+0.1689525544643402,0.5410239696502686,0.9028607606887816,0.4678111672401428,0.6770186424255371,0.3573770523071289,0.3573770523071289,0.986430048942566,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-20_21-25-54-050914_[deep_conv_dti,benchmark]/experiment=davis;local=gpu3090;preset=deep_conv_dti;tags=[deep_conv_dti,benchmark]/checkpoints/epoch_012.ckpt",COMPLETED,deep_conv_dti,davis,,"[deep_conv_dti,benchmark]",,gpu3090,
+0.202189102768898,0.3614169955253601,0.8401678204536438,0.330232560634613,0.5680000185966492,0.2327868789434433,0.2327868789434433,0.9859081506729126,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-23_23-27-18-387556_[m_graph_dta,benchmark]/experiment=davis;local=gpu3090;preset=m_graph_dta;tags=[m_graph_dta,benchmark]/checkpoints/epoch_094.ckpt",COMPLETED,m_graph_dta,davis,,"[m_graph_dta,benchmark]",,gpu3090,
diff --git a/configs/sweep/experiment_summary_kiba.csv b/configs/sweep/experiment_summary_kiba.csv
new file mode 100644
index 0000000000000000000000000000000000000000..fea61408c0c735bb75f05494db511650a4beff79
--- /dev/null
+++ b/configs/sweep/experiment_summary_kiba.csv
@@ -0,0 +1,8 @@
+test/loss,test/auprc,test/auroc,test/f1_score,test/precision,test/recall,test/sensitivity,test/specificity,ckpt_path,job_status,preset,experiment,sweep,tags,model.optimizer.lr,local,data.batch_size
+0.1525115370750427,0.735183835029602,0.9363504648208618,0.661261260509491,0.7816826701164246,0.5729898810386658,0.5729898810386658,0.984080135822296,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-16_14-53-13-645337_[benchmark,ddta,gdta]/experiment=kiba;preset=deep_dta;sweep=slurm_test;tags=[benchmark,ddta,gdta]/checkpoints/epoch_041.ckpt",COMPLETED,deep_dta,kiba,slurm_test,"[benchmark,ddta,gdta]",,,
+0.2281630784273147,0.4483628869056701,0.8383051156997681,0.2730720639228821,0.7176079750061035,0.1686182618141174,0.1686182618141174,0.9933990836143494,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-16_14-53-13-645337_[benchmark,ddta,gdta]/experiment=kiba;preset=graph_dta;sweep=slurm_test;tags=[benchmark,ddta,gdta]/checkpoints/epoch_065.ckpt",COMPLETED,graph_dta,kiba,slurm_test,"[benchmark,ddta,gdta]",,,
+0.144176036119461,0.7704265117645264,0.9501243829727172,0.6897732019424438,0.7632575631141663,0.6291959285736084,0.6291959285736084,0.980585515499115,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-16_18-08-40-028557_[sweep,benchmark]/experiment=kiba;preset=hyper_attention_dti;sweep=slurm_test/checkpoints/epoch_014.ckpt",COMPLETED,hyper_attention_dti,kiba,slurm_test,,,,
+0.1662539541721344,0.7397991418838501,0.9369943141937256,0.6784178614616394,0.7550238966941833,0.6159250736236572,0.6159250736236572,0.9801195859909058,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-20_18-50-02-680654_[mol_trans,benchmark]/experiment=kiba;preset=mol_trans;sweep=slurm_test/checkpoints/epoch_008.ckpt",COMPLETED,mol_trans,kiba,slurm_test,,,,
+0.2221459150314331,0.5159928798675537,0.8539965152740479,0.4716618657112121,0.612983763217926,0.3832943141460418,0.3832943141460418,0.9759260416030884,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-20_20-20-11-193061_[tcpi2,kiba,davis]/experiment=kiba;model.optimizer.lr=1e-06;preset=transformer_cpi_2;sweep=slurm_test;tags=[tcpi2,kiba,davis]/checkpoints/epoch_077.ckpt",COMPLETED,transformer_cpi_2,kiba,slurm_test,"[tcpi2,kiba,davis]",1e-06,,
+0.1273866593837738,0.8186066746711731,0.95548677444458,0.7409326434135437,0.8289855122566223,0.6697892546653748,0.6697892546653748,0.9862545728683472,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-20_21-25-54-050914_[deep_conv_dti,benchmark]/experiment=kiba;local=gpu3090;preset=deep_conv_dti;tags=[deep_conv_dti,benchmark]/checkpoints/epoch_026.ckpt",COMPLETED,deep_conv_dti,kiba,,"[deep_conv_dti,benchmark]",,gpu3090,
+0.1921116858720779,0.6082723736763,0.8893994092941284,0.4803767800331116,0.7285714149475098,0.3583138287067413,0.3583138287067413,0.9867205023765564,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-23_23-27-18-387556_[m_graph_dta,benchmark]/experiment=kiba;local=gpu3090;preset=m_graph_dta;tags=[m_graph_dta,benchmark]/checkpoints/epoch_246.ckpt",COMPLETED,m_graph_dta,kiba,,"[m_graph_dta,benchmark]",,gpu3090,
diff --git a/configs/sweep/experiment_summary_test.csv b/configs/sweep/experiment_summary_test.csv
new file mode 100644
index 0000000000000000000000000000000000000000..a1ce0b60c2e5c9e1cd678cc7a7a8867a2472d15f
--- /dev/null
+++ b/configs/sweep/experiment_summary_test.csv
@@ -0,0 +1,4 @@
+test/loss,test/auprc,test/auroc,test/f1_score,test/precision,test/recall,test/sensitivity,test/specificity,ckpt_path,job_status,preset,experiment,sweep,tags,model.optimizer.lr,local,data.batch_size
+0.193635880947113,0.4078962802886963,0.8581838607788086,0.3916666805744171,0.5371428728103638,0.3081967234611511,0.3081967234611511,0.9788622260093688,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-16_14-53-13-645337_[benchmark,ddta,gdta]/experiment=davis;preset=deep_dta;sweep=slurm_test;tags=[benchmark,ddta,gdta]/checkpoints/epoch_008.ckpt",COMPLETED,deep_dta,davis,slurm_test,"[benchmark,ddta,gdta]",,,
+0.2221491634845733,0.3560383319854736,0.78690105676651,0.2659846544265747,0.604651153087616,0.1704917997121811,0.1704917997121811,0.9911273717880248,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-16_14-53-13-645337_[benchmark,ddta,gdta]/experiment=davis;preset=graph_dta;sweep=slurm_test;tags=[benchmark,ddta,gdta]/checkpoints/epoch_054.ckpt",COMPLETED,graph_dta,davis,slurm_test,"[benchmark,ddta,gdta]",,,
+0.1689525544643402,0.5410239696502686,0.9028607606887816,0.4678111672401428,0.6770186424255371,0.3573770523071289,0.3573770523071289,0.986430048942566,"/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-20_21-25-54-050914_[deep_conv_dti,benchmark]/experiment=davis;local=gpu3090;preset=deep_conv_dti;tags=[deep_conv_dti,benchmark]/checkpoints/epoch_012.ckpt",COMPLETED,deep_conv_dti,davis,,"[deep_conv_dti,benchmark]",,gpu3090,
diff --git a/configs/sweep/midterm_benchmark.yaml b/configs/sweep/midterm_benchmark.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..17c6fbef87d5df1a6b3a5362a3332f710c30031b
--- /dev/null
+++ b/configs/sweep/midterm_benchmark.yaml
@@ -0,0 +1,6 @@
+# @package _global_
+tags: ['midterm', 'benchmark']
+hydra:
+ sweeper:
+ params:
+ experiment: kinase,membrane_receptors,non_kinase_enzymes #,ion_channels,nuclear_receptors,other_protein_targets
\ No newline at end of file
diff --git a/configs/sweep/multi_test.yaml b/configs/sweep/multi_test.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8fe120a7805b77823572d7ed83ab168ec685c939
--- /dev/null
+++ b/configs/sweep/multi_test.yaml
@@ -0,0 +1,8 @@
+# @package _global_
+hydra:
+ sweeper:
+ params:
+# ckpt_path: "'C:/Users/libok/Documents/GitHub/DeepScreen/logs/train/runs/2023-11-07_23-21-55-442205_[debug]/checkpoints/epoch_000.ckpt','C:/Users/libok/Documents/GitHub/DeepScreen/logs/train/runs/2023-11-07_19-46-08-740035_[debug]/checkpoints/epoch_000.ckpt'"
+ data.data_file: dti_benchmark/random_split_update/bindingdb_reserved_test.csv
+# ckpt_path: "'/gpfs/work/pha/daiyunhuang/WavyWaffle/logs/train/multiruns/2023-10-16_14-53-13-645337_[benchmark,ddta,gdta]/metrics_summary.csv'"
+# ckpt_path: "/gpfs/work/pha/daiyunhuang/WavyWaffle/configs/sweep/experiment_summary_bindingdb.csv"
diff --git a/configs/sweep/slurm_test.yaml b/configs/sweep/slurm_test.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..530ab519eed0ffe86445f2960c667e4484097459
--- /dev/null
+++ b/configs/sweep/slurm_test.yaml
@@ -0,0 +1,8 @@
+# @package _global_
+tags: ['sweep', 'benchmark']
+hydra:
+ sweeper:
+ params:
+ preset: graph_dta,deep_dta,drug_vqa, mol_trans,hyper_attention_dti,transformer_cpi_2
+ experiment: kiba,davis,bindingdb
+
diff --git a/configs/task/binary.yaml b/configs/task/binary.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..31eafe56289ebcb591ddd4254a943015c0d3e429
--- /dev/null
+++ b/configs/task/binary.yaml
@@ -0,0 +1,12 @@
+task: binary
+num_classes: null
+
+out:
+ _target_: torch.nn.LazyLinear
+ out_features: 1
+
+loss:
+ _target_: torch.nn.BCEWithLogitsLoss
+
+activation:
+ _target_: torch.nn.Sigmoid
\ No newline at end of file
diff --git a/configs/task/multiclass.yaml b/configs/task/multiclass.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..686e9f9b96c98839ec4bdad32351d29aca026618
--- /dev/null
+++ b/configs/task/multiclass.yaml
@@ -0,0 +1,12 @@
+task: multiclass
+num_classes: 3
+
+out:
+ _target_: torch.nn.LazyLinear
+ out_features: ${num_classes}
+
+loss:
+ _target_: torch.nn.CrossEntropyLoss
+
+activation:
+ _target_: torch.nn.Softmax
\ No newline at end of file
diff --git a/configs/task/regression.yaml b/configs/task/regression.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0de6eeb0179fcf63608df8a62719a5f6792baf1d
--- /dev/null
+++ b/configs/task/regression.yaml
@@ -0,0 +1,12 @@
+task: regression
+num_classes: null
+
+out:
+ _target_: torch.nn.LazyLinear
+ out_features: 1
+
+loss:
+ _target_: torch.nn.MSELoss
+
+activation:
+ _target_: torch.nn.Identity
\ No newline at end of file
diff --git a/configs/test.yaml b/configs/test.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..cc96bf8ed23b5f342b1bd2e49d39a31e401a5db4
--- /dev/null
+++ b/configs/test.yaml
@@ -0,0 +1,33 @@
+# @package _global_
+defaults:
+ - model: dti_model # fixed for web server version
+ - task: null
+ - data: dti_data # fixed for web server version
+ - callbacks:
+ - model_summary
+ - rich_progress_bar
+ - logger: csv
+ - trainer: default
+ - paths: default
+ - extras: default
+ - hydra: default
+# - hydra/callbacks:
+# - csv_metrics_summary
+ - _self_
+ - preset: null
+ - experiment: null
+ - sweep: null
+ - debug: null
+ - optional local: default
+
+
+job_name: "test"
+
+tags: ???
+
+# passing checkpoint path is necessary for evaluation
+ckpt_path: ???
+
+model:
+ optimizer: null
+ scheduler: null
\ No newline at end of file
diff --git a/configs/train.yaml b/configs/train.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8ccc566f93ae5ce7d80ebe65e215b6df3877c533
--- /dev/null
+++ b/configs/train.yaml
@@ -0,0 +1,53 @@
+# @package _global_
+
+# specify here default configuration
+# order of defaults determines the order in which configs override each other
+defaults:
+ - model: dti_model # fixed for web server version
+ - task: null
+ - data: dti_data # fixed for web server version
+ - callbacks: default
+ - logger: multiple_loggers # set logger here or use command line (e.g. `python train.py logger=tensorboard`)
+ - trainer: default
+ - paths: default
+ - extras: default
+ - hydra: default
+
+ - _self_
+
+ - preset: null
+ # Experiment configs allow for version control of specific hyperparameters
+ # e.g. the best hyperparameters for given model and data
+ - experiment: null
+ # Sweeping config, for batch experiments, hyperparameter optimization, etc.
+ - sweep: null
+ # Debugging config (enable through command line, e.g. `python train.py debug=default)
+ - debug: null
+
+ - optional local: default
+
+# job name, determines output directory path
+job_name: "train"
+
+# tags to help you identify your experiments
+# you can overwrite this in experiment configs
+# overwrite from command line with `python train.py tags="[first_tag, second_tag]"`
+tags: ???
+
+# evaluate on test set, using best model weights achieved during training
+# lightning chooses best weights based on the metric specified in checkpoint callback
+# test: True
+
+# compile model for faster training with PyTorch 2.0+
+compile: False
+
+# simply provide checkpoint path to resume training
+ckpt_path: null
+
+# seed for random number generators in torch, numpy and python.random
+seed: 12345
+
+trainer:
+ check_val_every_n_epoch: 1
+ limit_val_batches: ${eval:'1.0 if ${data.train_val_test_split}[1] else 0'}
+ num_sanity_val_steps: ${eval:'2 if ${data.train_val_test_split}[1] else 0'}
diff --git a/configs/trainer/cpu.yaml b/configs/trainer/cpu.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b7d6767e60c956567555980654f15e7bb673a41f
--- /dev/null
+++ b/configs/trainer/cpu.yaml
@@ -0,0 +1,5 @@
+defaults:
+ - default
+
+accelerator: cpu
+devices: 1
diff --git a/configs/trainer/ddp.yaml b/configs/trainer/ddp.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c3d1e80223544668cba9356d95c96ae7e488b389
--- /dev/null
+++ b/configs/trainer/ddp.yaml
@@ -0,0 +1,10 @@
+defaults:
+ - default
+
+strategy: ddp
+
+accelerator: gpu
+devices: 4
+num_nodes: 1
+sync_batchnorm: True
+precision: bf16
diff --git a/configs/trainer/ddp_sim.yaml b/configs/trainer/ddp_sim.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8404419e5c295654967d0dfb73a7366e75be2f1f
--- /dev/null
+++ b/configs/trainer/ddp_sim.yaml
@@ -0,0 +1,7 @@
+defaults:
+ - default
+
+# simulate DDP on CPU, useful for debugging
+accelerator: cpu
+devices: 2
+strategy: ddp_spawn
diff --git a/configs/trainer/default.yaml b/configs/trainer/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f951d826c290b3bc14175ca427de675d3a30a4da
--- /dev/null
+++ b/configs/trainer/default.yaml
@@ -0,0 +1,18 @@
+_target_: lightning.Trainer
+
+default_root_dir: ${paths.output_dir}
+
+min_epochs: 1
+max_epochs: 50
+
+precision: bf16
+
+gradient_clip_val: 0.5
+gradient_clip_algorithm: norm
+
+# deterministic algorithms might make training slower but offers more reproducibility than only setting seeds
+# True: use deterministic always, throwing an error on an operation that doesn't support deterministic
+# warn: use deterministic when possible, throwing warnings on operations that don’t support deterministic
+deterministic: warn
+
+inference_mode: True
\ No newline at end of file
diff --git a/configs/trainer/gpu.yaml b/configs/trainer/gpu.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5e1e7d1809558ea3f8fad31a80c5f6ff5f36fcc4
--- /dev/null
+++ b/configs/trainer/gpu.yaml
@@ -0,0 +1,6 @@
+defaults:
+ - default
+
+accelerator: gpu
+devices: 1
+precision: bf16
\ No newline at end of file
diff --git a/configs/trainer/mps.yaml b/configs/trainer/mps.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1ecf6d5cc3a34ca127c5510f4a18e989561e38e4
--- /dev/null
+++ b/configs/trainer/mps.yaml
@@ -0,0 +1,5 @@
+defaults:
+ - default
+
+accelerator: mps
+devices: 1
diff --git a/deepscreen/__init__.py b/deepscreen/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7341f113811ead2dcaf82f3e2cafa0e0e2d89ce
--- /dev/null
+++ b/deepscreen/__init__.py
@@ -0,0 +1,101 @@
+"""
+DeepScreen package initialization, registering custom objects and monkey patching for some libraries.
+"""
+import sys
+from builtins import eval
+
+import lightning.fabric.strategies.launchers.subprocess_script as subprocess_script
+import torch
+from omegaconf import OmegaConf
+
+from deepscreen.utils import get_logger
+
+log = get_logger(__name__)
+
+# Allow basic Python operations in hydra interpolation; examples:
+# `in_channels: ${eval:${model.drug_encoder.out_channels}+${model.protein_encoder.out_channels}}`
+# `subdir: ${eval:${hydra.job.override_dirname}.replace('/', '.')}`
+OmegaConf.register_new_resolver("eval", eval)
+
+
+def sanitize_path(path_str: str):
+ """
+ Sanitize a string for path creation by replacing unsafe characters.
+ """
+ return path_str.replace("/", ".").replace("\\", ".").replace(":", "-")
+
+
+OmegaConf.register_new_resolver("sanitize_path", sanitize_path)
+
+
+def _hydra_subprocess_cmd(local_rank: int):
+ """
+ Monkey patching for lightning.fabric.strategies.launchers.subprocess_script._hydra_subprocess_cmd
+ Temporarily fixes the problem of unnecessarily creating log folders for DDP subprocesses in Hydra multirun/sweep.
+ """
+ import __main__ # local import to avoid https://github.com/Lightning-AI/lightning/issues/15218
+ from hydra.core.hydra_config import HydraConfig
+ from hydra.utils import get_original_cwd, to_absolute_path
+
+ # when user is using hydra find the absolute path
+ if __main__.__spec__ is None: # pragma: no-cover
+ command = [sys.executable, to_absolute_path(sys.argv[0])]
+ else:
+ command = [sys.executable, "-m", __main__.__spec__.name]
+
+ command += sys.argv[1:]
+
+ cwd = get_original_cwd()
+ rundir = f'"{HydraConfig.get().runtime.output_dir}"'
+ # Set output_subdir null since we don't want different subprocesses trying to write to config.yaml
+ command += [f"hydra.job.name=train_ddp_process_{local_rank}",
+ "hydra.output_subdir=null,"
+ f"hydra.runtime.output_dir={rundir}"]
+ return command, cwd
+
+
+subprocess_script._hydra_subprocess_cmd = _hydra_subprocess_cmd
+
+# from torch import Tensor
+# from lightning.fabric.utilities.distributed import _distributed_available
+# from lightning.pytorch.utilities.rank_zero import WarningCache
+# from lightning.pytorch.utilities.warnings import PossibleUserWarning
+# from lightning.pytorch.trainer.connectors.logger_connector.result import _ResultCollection
+
+# warning_cache = WarningCache()
+#
+# @staticmethod
+# def _get_cache(result_metric, on_step: bool):
+# cache = None
+# if on_step and result_metric.meta.on_step:
+# cache = result_metric._forward_cache
+# elif not on_step and result_metric.meta.on_epoch:
+# if result_metric._computed is None:
+# should = result_metric.meta.sync.should
+# if not should and _distributed_available() and result_metric.is_tensor:
+# warning_cache.warn(
+# f"It is recommended to use `self.log({result_metric.meta.name!r}, ..., sync_dist=True)`"
+# " when logging on epoch level in distributed setting to accumulate the metric across"
+# " devices.",
+# category=PossibleUserWarning,
+# )
+# result_metric.compute()
+# result_metric.meta.sync.should = should
+#
+# cache = result_metric._computed
+#
+# if cache is not None:
+# if isinstance(cache, Tensor):
+# if not result_metric.meta.enable_graph:
+# return cache.detach()
+#
+# return cache
+#
+#
+# _ResultCollection._get_cache = _get_cache
+
+if torch.cuda.is_available():
+ if torch.cuda.get_device_capability() >= (8, 0):
+ torch.set_float32_matmul_precision("high")
+ log.info("Your GPU supports tensor cores, "
+ "we will enable it automatically by setting `torch.set_float32_matmul_precision('high')`")
diff --git a/deepscreen/__pycache__/__init__.cpython-311.pyc b/deepscreen/__pycache__/__init__.cpython-311.pyc
new file mode 100644
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diff --git a/deepscreen/data/__pycache__/dti_datamodule.cpython-311.pyc b/deepscreen/data/__pycache__/dti_datamodule.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..868b4f30b00539c0675f19112a5dbd11c3381f43
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diff --git a/deepscreen/data/dti.py b/deepscreen/data/dti.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f4ade229f3347c4b8fe60f2c6ea1e20b95b989f
--- /dev/null
+++ b/deepscreen/data/dti.py
@@ -0,0 +1,376 @@
+from functools import partial
+from numbers import Number
+from pathlib import Path
+from typing import Any, Dict, Optional, Sequence, Union, Literal
+
+from lightning import LightningDataModule
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+from torch.utils.data import Dataset, DataLoader
+
+from deepscreen.data.utils import label_transform, collate_fn, SafeBatchSampler
+from deepscreen.utils import get_logger
+
+log = get_logger(__name__)
+
+
+# TODO: save a list of corrupted records
+
+
+class DTIDataset(Dataset):
+ def __init__(
+ self,
+ task: Literal['regression', 'binary', 'multiclass'],
+ num_classes: Optional[int],
+ data_path: str | Path,
+ drug_featurizer: callable,
+ protein_featurizer: callable,
+ thresholds: Optional[Union[Number, Sequence[Number]]] = None,
+ discard_intermediate: Optional[bool] = False,
+ ):
+ df = pd.read_csv(
+ data_path,
+ engine='python',
+ header=0,
+ usecols=lambda x: x in ['X1', 'ID1', 'X2', 'ID2', 'Y', 'U'],
+ dtype={
+ 'X1': 'str',
+ 'ID1': 'str',
+ 'X2': 'str',
+ 'ID2': 'str',
+ 'Y': 'float32',
+ 'U': 'str',
+ },
+ )
+ # Read the whole data table
+
+ # if 'ID1' in df:
+ # self.x1_to_id1 = dict(zip(df['X1'], df['ID1']))
+ # if 'ID2' in df:
+ # self.x2_to_id2 = dict(zip(df['X2'], df['ID2']))
+ # self.id2_to_indexes = dict(zip(df['ID2'], range(len(df['ID2']))))
+ # self.x2_to_indexes = dict(zip(df['X2'], range(len(df['X2']))))
+
+ # # train and eval mode data processing (fully labelled)
+ # if 'Y' in df.columns and df['Y'].notnull().all():
+ log.info(f"Processing data file: {data_path}")
+
+ # Forward-fill all non-label columns
+ df.loc[:, df.columns != 'Y'] = df.loc[:, df.columns != 'Y'].ffill(axis=0)
+
+ if 'Y' in df:
+ log.info(f"Performing pre-transformation target validation.")
+ # TODO: check sklearn.utils.multiclass.check_classification_targets
+ match task:
+ case 'regression':
+ assert all(df['Y'].apply(lambda x: isinstance(x, Number))), \
+ f"""`Y` must be numeric for `regression` task,
+ but it has {set(df['Y'].apply(type))}."""
+
+ case 'binary':
+ if all(df['Y'].isin([0, 1])):
+ assert not thresholds, \
+ f"""`Y` is already 0 or 1 for `binary` (classification) `task`,
+ but still got `thresholds` {thresholds}.
+ Double check your choices of `task` and `thresholds` and records in the `Y` column."""
+ else:
+ assert thresholds, \
+ f"""`Y` must be 0 or 1 for `binary` (classification) `task`,
+ but it has {pd.unique(df['Y'])}.
+ You must set `thresholds` to discretize continuous labels."""
+
+ case 'multiclass':
+ assert num_classes >= 3, f'`num_classes` for `task=multiclass` must be at least 3.'
+
+ if all(df['Y'].apply(lambda x: x.is_integer() and x >= 0)):
+ assert not thresholds, \
+ f"""`Y` is already non-negative integers for
+ `multiclass` (classification) `task`, but still got `thresholds` {thresholds}.
+ Double check your choice of `task`, `thresholds` and records in the `Y` column."""
+ else:
+ assert thresholds, \
+ f"""`Y` must be non-negative integers for
+ `multiclass` (classification) 'task',but it has {pd.unique(df['Y'])}.
+ You must set `thresholds` to discretize continuous labels."""
+
+ if 'U' in df.columns:
+ units = df['U']
+ else:
+ units = None
+ log.warning("Units ('U') not in the data table. "
+ "Assuming all labels to be discrete or in p-scale (-log10[M]).")
+
+ # Transform labels
+ df['Y'] = label_transform(labels=df['Y'], units=units, thresholds=thresholds,
+ discard_intermediate=discard_intermediate)
+
+ # Filter out rows with a NaN in Y (missing values)
+ df.dropna(subset=['Y'], inplace=True)
+
+ log.info(f"Performing post-transformation target validation.")
+ match task:
+ case 'regression':
+ df['Y'] = df['Y'].astype('float32')
+ assert all(df['Y'].apply(lambda x: isinstance(x, Number))), \
+ f"""`Y` must be numeric for `regression` task,
+ but after transformation it still has {set(df['Y'].apply(type))}.
+ Double check your choices of `task` and `thresholds` and records in the `Y` and `U` columns."""
+
+ case 'binary':
+ df['Y'] = df['Y'].astype('int')
+ assert all(df['Y'].isin([0, 1])), \
+ f"""`Y` must be 0 or 1 for `task=binary`, "
+ but after transformation it still has {pd.unique(df['Y'])}.
+ Double check your choices of `task` and `thresholds` and records in the `Y` and `U` columns."""
+
+ case 'multiclass':
+ df['Y'] = df['Y'].astype('int')
+ assert all(df['Y'].apply(lambda x: x.is_integer() and x >= 0)), \
+ f"""Y must be non-negative integers for `task=multiclass`
+ but after transformation it still has {pd.unique(df['Y'])}.
+ Double check your choices of `task` and `thresholds` and records in the `Y` and `U` columns."""
+
+ target_n_unique = df['Y'].nunique()
+ assert target_n_unique == num_classes, \
+ f"""You have set `num_classes` for `task=multiclass` to {num_classes},
+ but after transformation Y still has {target_n_unique} unique labels.
+ Double check your choices of `task` and `thresholds` and records in the `Y` and `U` columns."""
+
+ # Indexed protein/FASTA for retrieval metrics
+ df['IDX'] = LabelEncoder().fit_transform(df['X2'])
+
+ self.df = df
+ self.drug_featurizer = drug_featurizer if drug_featurizer is not None else (lambda x: x)
+ self.protein_featurizer = protein_featurizer if protein_featurizer is not None else (lambda x: x)
+
+ def __len__(self):
+ return len(self.df.index)
+
+ def __getitem__(self, i):
+ sample = self.df.loc[i]
+ return {
+ 'N': i,
+ 'X1': self.drug_featurizer(sample['X1']),
+ 'ID1': sample.get('ID1', sample['X1']),
+ 'X2': self.protein_featurizer(sample['X2']),
+ 'ID2': sample.get('ID2', sample['X2']),
+ 'Y': sample.get('Y'),
+ 'IDX': sample['IDX'],
+ }
+
+
+class DTIDataModule(LightningDataModule):
+ """
+ DTI DataModule
+
+ A DataModule implements 5 key methods:
+
+ def prepare_data(self):
+ # things to do on 1 GPU/TPU (not on every GPU/TPU in DDP)
+ # download data, pre-process, split, save to disk, etc.
+ def setup(self, stage):
+ # things to do on every process in DDP
+ # load data, set variables, etc.
+ def train_dataloader(self):
+ # return train dataloader
+ def val_dataloader(self):
+ # return validation dataloader
+ def test_dataloader(self):
+ # return test dataloader
+ def teardown(self):
+ # called on every process in DDP
+ # clean up after fit or test
+
+ This allows you to share a full dataset without explaining how to download,
+ split, transform and process the data.
+
+ Read the docs:
+ https://pytorch-lightning.readthedocs.io/en/latest/extensions/datamodules.html
+ """
+
+ def __init__(
+ self,
+ task: Literal['regression', 'binary', 'multiclass'],
+ num_classes: Optional[int],
+ batch_size: int,
+ # train: bool,
+ drug_featurizer: callable,
+ protein_featurizer: callable,
+ collator: callable = collate_fn,
+ data_dir: str = "data/",
+ data_file: Optional[str] = None,
+ train_val_test_split: Optional[Union[Sequence[Number | str]]] = None,
+ split: Optional[callable] = None,
+ thresholds: Optional[Union[Number, Sequence[Number]]] = None,
+ discard_intermediate: Optional[bool] = False,
+ num_workers: int = 0,
+ pin_memory: bool = False,
+ ):
+ super().__init__()
+
+ self.train_data: Optional[Dataset] = None
+ self.val_data: Optional[Dataset] = None
+ self.test_data: Optional[Dataset] = None
+ self.predict_data: Optional[Dataset] = None
+ self.split = split
+ self.collator = collator
+ self.dataset = partial(
+ DTIDataset,
+ task=task,
+ num_classes=num_classes,
+ drug_featurizer=drug_featurizer,
+ protein_featurizer=protein_featurizer,
+ thresholds=thresholds,
+ discard_intermediate=discard_intermediate
+ )
+
+ # this line allows to access init params with 'self.hparams' ensures init params will be stored in ckpt
+ self.save_hyperparameters(logger=False) # ignore=['split']
+
+ def prepare_data(self):
+ """
+ Download data if needed.
+ Do not use it to assign state (e.g., self.x = x).
+ """
+
+ def setup(self, stage: Optional[str] = None, encoding: str = None):
+ """
+ Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`.
+ This method is called by lightning with both `trainer.fit()` and `trainer.test()`, so be
+ careful not to execute data splitting twice.
+ """
+ # load and split datasets only if not loaded in initialization
+ if not any([self.train_data, self.test_data, self.val_data, self.predict_data]):
+ if self.hparams.train_val_test_split:
+ if len(self.hparams.train_val_test_split) != 3:
+ raise ValueError('Length of `train_val_test_split` must be 3. '
+ 'Set the second element to None for training without validation. '
+ 'Set the third element to None for training without testing.')
+
+ self.train_data = self.hparams.train_val_test_split[0]
+ self.val_data = self.hparams.train_val_test_split[1]
+ self.test_data = self.hparams.train_val_test_split[2]
+
+ if all([self.hparams.data_file, self.split]):
+ if all(isinstance(split, Number) or split is None
+ for split in self.hparams.train_val_test_split):
+ split_data = self.split(
+ dataset=self.dataset(data_path=Path(self.hparams.data_dir, self.hparams.data_file)),
+ lengths=[split for split in self.hparams.train_val_test_split if split is not None]
+ )
+ for dataset in ['train_data', 'val_data', 'test_data']:
+ if getattr(self, dataset) is not None:
+ setattr(self, dataset, split_data.pop(0))
+
+ else:
+ raise ValueError('`train_val_test_split` must be a sequence numbers or None'
+ '(float for percentages and int for sample numbers) '
+ 'if both `data_file` and `split` have been specified.')
+
+ elif (all(isinstance(split, str) or split is None
+ for split in self.hparams.train_val_test_split)
+ and not any([self.hparams.data_file, self.split])):
+ for dataset in ['train_data', 'val_data', 'test_data']:
+ if getattr(self, dataset) is not None:
+ data_path = Path(getattr(self, dataset))
+ if not data_path.is_absolute():
+ data_path = Path(self.hparams.data_dir, data_path)
+ setattr(self, dataset, self.dataset(data_path=data_path))
+
+ else:
+ raise ValueError('For training, you must specify either all of `data_file`, `split`, '
+ 'and `train_val_test_split` as a sequence of numbers or '
+ 'solely `train_val_test_split` as a sequence of data file paths.')
+
+ elif self.hparams.data_file and not any([self.split, self.hparams.train_val_test_split]):
+ data_path = Path(self.hparams.data_file)
+ if not data_path.is_absolute():
+ data_path = Path(self.hparams.data_dir, data_path)
+ self.test_data = self.predict_data = self.dataset(data_path=data_path)
+
+ else:
+ raise ValueError("For training, you must specify `train_val_test_split`. "
+ "For testing/predicting, you must specify only `data_file` without "
+ "`train_val_test_split` or `split`.")
+
+ def train_dataloader(self):
+ return DataLoader(
+ dataset=self.train_data,
+ batch_sampler=SafeBatchSampler(
+ data_source=self.train_data,
+ batch_size=self.hparams.batch_size,
+ # Dropping the last batch prevents problems caused by variable batch sizes in training, e.g.,
+ # batch_size=1 in BatchNorm, and shuffling ensures the model be trained on all samples over epochs.
+ drop_last=True,
+ shuffle=True,
+ ),
+ # batch_size=self.hparams.batch_size,
+ # shuffle=True,
+ num_workers=self.hparams.num_workers,
+ pin_memory=self.hparams.pin_memory,
+ collate_fn=self.collator,
+ persistent_workers=True if self.hparams.num_workers > 0 else False
+ )
+
+ def val_dataloader(self):
+ return DataLoader(
+ dataset=self.val_data,
+ batch_sampler=SafeBatchSampler(
+ data_source=self.val_data,
+ batch_size=self.hparams.batch_size,
+ drop_last=False,
+ shuffle=False
+ ),
+ # batch_size=self.hparams.batch_size,
+ # shuffle=False,
+ num_workers=self.hparams.num_workers,
+ pin_memory=self.hparams.pin_memory,
+ collate_fn=self.collator,
+ persistent_workers=True if self.hparams.num_workers > 0 else False
+ )
+
+ def test_dataloader(self):
+ return DataLoader(
+ dataset=self.test_data,
+ batch_sampler=SafeBatchSampler(
+ data_source=self.test_data,
+ batch_size=self.hparams.batch_size,
+ drop_last=False,
+ shuffle=False
+ ),
+ # batch_size=self.hparams.batch_size,
+ # shuffle=False,
+ num_workers=self.hparams.num_workers,
+ pin_memory=self.hparams.pin_memory,
+ collate_fn=self.collator,
+ persistent_workers=True if self.hparams.num_workers > 0 else False
+ )
+
+ def predict_dataloader(self):
+ return DataLoader(
+ dataset=self.predict_data,
+ batch_sampler=SafeBatchSampler(
+ data_source=self.predict_data,
+ batch_size=self.hparams.batch_size,
+ drop_last=False,
+ shuffle=False
+ ),
+ # batch_size=self.hparams.batch_size,
+ # shuffle=False,
+ num_workers=self.hparams.num_workers,
+ pin_memory=self.hparams.pin_memory,
+ collate_fn=self.collator,
+ persistent_workers=True if self.hparams.num_workers > 0 else False
+ )
+
+ def teardown(self, stage: Optional[str] = None):
+ """Clean up after fit or test."""
+ pass
+
+ def state_dict(self):
+ """Extra things to save to checkpoint."""
+ return {}
+
+ def load_state_dict(self, state_dict: Dict[str, Any]):
+ """Things to do when loading checkpoint."""
+ pass
diff --git a/deepscreen/data/dti.py.bak b/deepscreen/data/dti.py.bak
new file mode 100644
index 0000000000000000000000000000000000000000..a531948e5acf64b3e1f2404ce9a82cbafbcd84d4
--- /dev/null
+++ b/deepscreen/data/dti.py.bak
@@ -0,0 +1,369 @@
+from functools import partial
+from numbers import Number
+from pathlib import Path
+from typing import Any, Dict, Optional, Sequence, Union, Literal
+
+from lightning import LightningDataModule
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+from torch.utils.data import Dataset, DataLoader
+
+from deepscreen.data.utils import label_transform, collate_fn, SafeBatchSampler
+from deepscreen.utils import get_logger
+
+log = get_logger(__name__)
+
+
+# TODO: save a list of corrupted records
+
+
+class DTIDataset(Dataset):
+ def __init__(
+ self,
+ task: Literal['regression', 'binary', 'multiclass'],
+ n_class: Optional[int],
+ data_path: str | Path,
+ drug_featurizer: callable,
+ protein_featurizer: callable,
+ thresholds: Optional[Union[Number, Sequence[Number]]] = None,
+ discard_intermediate: Optional[bool] = False,
+ ):
+ df = pd.read_csv(
+ data_path,
+ engine='python',
+ header=0,
+ usecols=lambda x: x in ['X1', 'ID1', 'X2', 'ID2', 'Y', 'U'],
+ dtype={
+ 'X1': 'str',
+ 'ID1': 'str',
+ 'X2': 'str',
+ 'ID2': 'str',
+ 'Y': 'float32',
+ 'U': 'str',
+ },
+ )
+ # Read the whole data table
+
+ # if 'ID1' in df:
+ # self.x1_to_id1 = dict(zip(df['X1'], df['ID1']))
+ # if 'ID2' in df:
+ # self.x2_to_id2 = dict(zip(df['X2'], df['ID2']))
+ # self.id2_to_indexes = dict(zip(df['ID2'], range(len(df['ID2']))))
+ # self.x2_to_indexes = dict(zip(df['X2'], range(len(df['X2']))))
+
+ # # train and eval mode data processing (fully labelled)
+ # if 'Y' in df.columns and df['Y'].notnull().all():
+ log.info(f"Processing data file: {data_path}")
+
+ # Forward-fill all non-label columns
+ df.loc[:, df.columns != 'Y'] = df.loc[:, df.columns != 'Y'].ffill(axis=0)
+
+ if 'Y' in df:
+ log.info(f"Performing pre-transformation target validation.")
+ # TODO: check sklearn.utils.multiclass.check_classification_targets
+ match task:
+ case 'regression':
+ assert all(df['Y'].apply(lambda x: isinstance(x, Number))), \
+ f"""`Y` must be numeric for `regression` task,
+ but it has {set(df['Y'].apply(type))}."""
+
+ case 'binary':
+ if all(df['Y'].isin([0, 1])):
+ assert not thresholds, \
+ f"""`Y` is already 0 or 1 for `binary` (classification) `task`,
+ but still got `thresholds` {thresholds}.
+ Double check your choices of `task` and `thresholds` and records in the `Y` column."""
+ else:
+ assert thresholds, \
+ f"""`Y` must be 0 or 1 for `binary` (classification) `task`,
+ but it has {pd.unique(df['Y'])}.
+ You must set `thresholds` to discretize continuous labels."""
+
+ case 'multiclass':
+ assert n_class >= 3, f'`n_class` for `multiclass` (classification) `task` must be at least 3.'
+
+ if all(df['Y'].apply(lambda x: x.is_integer() and x >= 0)):
+ assert not thresholds, \
+ f"""`Y` is already non-negative integers for
+ `multiclass` (classification) `task`, but still got `thresholds` {thresholds}.
+ Double check your choice of `task`, `thresholds` and records in the `Y` column."""
+ else:
+ assert thresholds, \
+ f"""`Y` must be non-negative integers for
+ `multiclass` (classification) 'task',but it has {pd.unique(df['Y'])}.
+ You must set `thresholds` to discretize continuous labels."""
+
+ if 'U' in df.columns:
+ units = df['U']
+ else:
+ units = None
+ log.warning("Units ('U') not in the data table. "
+ "Assuming all labels to be discrete or in p-scale (-log10[M]).")
+
+ # Transform labels
+ df['Y'] = label_transform(labels=df['Y'], units=units, thresholds=thresholds,
+ discard_intermediate=discard_intermediate)
+
+ # Filter out rows with a NaN in Y (missing values)
+ df.dropna(subset=['Y'], inplace=True)
+
+ log.info(f"Performing post-transformation target validation.")
+ match task:
+ case 'regression':
+ df['Y'] = df['Y'].astype('float32')
+ assert all(df['Y'].apply(lambda x: isinstance(x, Number))), \
+ f"""`Y` must be numeric for `regression` task,
+ but after transformation it still has {set(df['Y'].apply(type))}.
+ Double check your choices of `task` and `thresholds` and records in the `Y` and `U` columns."""
+
+ case 'binary':
+ df['Y'] = df['Y'].astype('int')
+ assert all(df['Y'].isin([0, 1])), \
+ f"""`Y` must be 0 or 1 for `binary` (classification) `task`, "
+ but after transformation it still has {pd.unique(df['Y'])}.
+ Double check your choices of `task` and `thresholds` and records in the `Y` and `U` columns."""
+
+ case 'multiclass':
+ df['Y'] = df['Y'].astype('int')
+ assert all(df['Y'].apply(lambda x: x.is_integer() and x >= 0)), \
+ f"""Y must be non-negative integers for task `multiclass` (classification)
+ but after transformation it still has {pd.unique(df['Y'])}.
+ Double check your choices of `task` and `thresholds` and records in the `Y` and `U` columns."""
+
+ target_n_unique = df['Y'].nunique()
+ assert target_n_unique == n_class, \
+ f"""You have set `n_class` for `multiclass` (classification) `task` to {n_class},
+ but after transformation Y still has {target_n_unique} unique labels.
+ Double check your choices of `task` and `thresholds` and records in the `Y` and `U` columns."""
+
+ # Indexed protein/FASTA for retrieval metrics
+ df['IDX'] = LabelEncoder().fit_transform(df['X2'])
+
+ self.df = df
+ self.drug_featurizer = drug_featurizer if drug_featurizer is not None else (lambda x: x)
+ self.protein_featurizer = protein_featurizer if protein_featurizer is not None else (lambda x: x)
+
+ def __len__(self):
+ return len(self.df.index)
+
+ def __getitem__(self, i):
+ sample = self.df.loc[i]
+ return {
+ 'N': i,
+ 'X1': self.drug_featurizer(sample['X1']),
+ 'ID1': sample.get('ID1', sample['X1']),
+ 'X2': self.protein_featurizer(sample['X2']),
+ 'ID2': sample.get('ID2', sample['X2']),
+ 'Y': sample.get('Y'),
+ 'IDX': sample['IDX'],
+ }
+
+
+class DTIDataModule(LightningDataModule):
+ """
+ DTI DataModule
+
+ A DataModule implements 5 key methods:
+
+ def prepare_data(self):
+ # things to do on 1 GPU/TPU (not on every GPU/TPU in DDP)
+ # download data, pre-process, split, save to disk, etc.
+ def setup(self, stage):
+ # things to do on every process in DDP
+ # load data, set variables, etc.
+ def train_dataloader(self):
+ # return train dataloader
+ def val_dataloader(self):
+ # return validation dataloader
+ def test_dataloader(self):
+ # return test dataloader
+ def teardown(self):
+ # called on every process in DDP
+ # clean up after fit or test
+
+ This allows you to share a full dataset without explaining how to download,
+ split, transform and process the data.
+
+ Read the docs:
+ https://pytorch-lightning.readthedocs.io/en/latest/extensions/datamodules.html
+ """
+
+ def __init__(
+ self,
+ task: Literal['regression', 'binary', 'multiclass'],
+ n_class: Optional[int],
+ batch_size: int,
+ # train: bool,
+ drug_featurizer: callable,
+ protein_featurizer: callable,
+ collator: callable = collate_fn,
+ data_dir: str = "data/",
+ data_file: Optional[str] = None,
+ train_val_test_split: Optional[Union[Sequence[Number | str]]] = None,
+ split: Optional[callable] = None,
+ thresholds: Optional[Union[Number, Sequence[Number]]] = None,
+ discard_intermediate: Optional[bool] = False,
+ num_workers: int = 0,
+ pin_memory: bool = False,
+ ):
+ super().__init__()
+
+ self.train_data: Optional[Dataset] = None
+ self.val_data: Optional[Dataset] = None
+ self.test_data: Optional[Dataset] = None
+ self.predict_data: Optional[Dataset] = None
+ self.split = split
+ self.collator = collator
+ self.dataset = partial(
+ DTIDataset,
+ task=task,
+ n_class=n_class,
+ drug_featurizer=drug_featurizer,
+ protein_featurizer=protein_featurizer,
+ thresholds=thresholds,
+ discard_intermediate=discard_intermediate
+ )
+
+ if train_val_test_split:
+ # TODO test behavior for trainer.test and predict when this is passed
+ if len(train_val_test_split) not in [2, 3]:
+ raise ValueError('Length of `train_val_test_split` must be 2 (for training without testing) or 3.')
+ if all([data_file, split]):
+ if all(isinstance(split, Number) for split in train_val_test_split):
+ pass
+ else:
+ raise ValueError('`train_val_test_split` must be a sequence numbers '
+ '(float for percentages and int for sample numbers) '
+ 'if both `data_file` and `split` have been specified.')
+ elif all(isinstance(split, str) for split in train_val_test_split) and not any([data_file, split]):
+ split_paths = []
+ for split in train_val_test_split:
+ split = Path(split)
+ if not split.is_absolute():
+ split = Path(data_dir, split)
+ split_paths.append(split)
+
+ self.train_data = self.dataset(data_path=split_paths[0])
+ self.val_data = self.dataset(data_path=split_paths[1])
+ if len(train_val_test_split) == 3:
+ self.test_data = self.dataset(data_path=split_paths[2])
+ else:
+ raise ValueError('For training, you must specify either `data_file`, `split`, '
+ 'and `train_val_test_split` as a sequence of numbers or '
+ 'solely `train_val_test_split` as a sequence of data file paths.')
+
+ elif data_file and not any([split, train_val_test_split]):
+ data_file = Path(data_file)
+ if not data_file.is_absolute():
+ data_file = Path(data_dir, data_file)
+ self.test_data = self.predict_data = self.dataset(data_path=data_file)
+ else:
+ raise ValueError("For training, you must specify `train_val_test_split`. "
+ "For testing/predicting, you must specify only `data_file` without "
+ "`train_val_test_split` or `split`.")
+
+ # this line allows to access init params with 'self.hparams' attribute
+ # also ensures init params will be stored in ckpt
+ self.save_hyperparameters(logger=False) # ignore=['split']
+
+ def prepare_data(self):
+ """
+ Download data if needed.
+ Do not use it to assign state (e.g., self.x = x).
+ """
+
+ def setup(self, stage: Optional[str] = None, encoding: str = None):
+ """
+ Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`.
+ This method is called by lightning with both `trainer.fit()` and `trainer.test()`, so be
+ careful not to execute data splitting twice.
+ """
+ # TODO test SafeBatchSampler (which skips samples with any None without introducing variable batch size)
+ # load and split datasets only if not loaded in initialization
+ if not any([self.train_data, self.test_data, self.val_data, self.predict_data]):
+ self.train_data, self.val_data, self.test_data = self.split(
+ dataset=self.dataset(data_path=Path(self.hparams.data_dir, self.hparams.data_file)),
+ lengths=self.hparams.train_val_test_split
+ )
+
+ def train_dataloader(self):
+ return DataLoader(
+ dataset=self.train_data,
+ batch_sampler=SafeBatchSampler(
+ data_source=self.train_data,
+ batch_size=self.hparams.batch_size,
+ # Dropping the last batch prevents problems caused by variable batch sizes in training, e.g.,
+ # batch_size=1 in BatchNorm, and shuffling ensures the model be trained on all samples over epochs.
+ drop_last=True,
+ shuffle=True,
+ ),
+ # batch_size=self.hparams.batch_size,
+ # shuffle=True,
+ num_workers=self.hparams.num_workers,
+ pin_memory=self.hparams.pin_memory,
+ collate_fn=self.collator,
+ persistent_workers=True if self.hparams.num_workers > 0 else False
+ )
+
+ def val_dataloader(self):
+ return DataLoader(
+ dataset=self.val_data,
+ batch_sampler=SafeBatchSampler(
+ data_source=self.val_data,
+ batch_size=self.hparams.batch_size,
+ drop_last=False,
+ shuffle=False
+ ),
+ # batch_size=self.hparams.batch_size,
+ # shuffle=False,
+ num_workers=self.hparams.num_workers,
+ pin_memory=self.hparams.pin_memory,
+ collate_fn=self.collator,
+ persistent_workers=True if self.hparams.num_workers > 0 else False
+ )
+
+ def test_dataloader(self):
+ return DataLoader(
+ dataset=self.test_data,
+ batch_sampler=SafeBatchSampler(
+ data_source=self.test_data,
+ batch_size=self.hparams.batch_size,
+ drop_last=False,
+ shuffle=False
+ ),
+ # batch_size=self.hparams.batch_size,
+ # shuffle=False,
+ num_workers=self.hparams.num_workers,
+ pin_memory=self.hparams.pin_memory,
+ collate_fn=self.collator,
+ persistent_workers=True if self.hparams.num_workers > 0 else False
+ )
+
+ def predict_dataloader(self):
+ return DataLoader(
+ dataset=self.predict_data,
+ batch_sampler=SafeBatchSampler(
+ data_source=self.predict_data,
+ batch_size=self.hparams.batch_size,
+ drop_last=False,
+ shuffle=False
+ ),
+ # batch_size=self.hparams.batch_size,
+ # shuffle=False,
+ num_workers=self.hparams.num_workers,
+ pin_memory=self.hparams.pin_memory,
+ collate_fn=self.collator,
+ persistent_workers=True if self.hparams.num_workers > 0 else False
+ )
+
+ def teardown(self, stage: Optional[str] = None):
+ """Clean up after fit or test."""
+ pass
+
+ def state_dict(self):
+ """Extra things to save to checkpoint."""
+ return {}
+
+ def load_state_dict(self, state_dict: Dict[str, Any]):
+ """Things to do when loading checkpoint."""
+ pass
diff --git a/deepscreen/data/dti_datamodule.py b/deepscreen/data/dti_datamodule.py
new file mode 100644
index 0000000000000000000000000000000000000000..35dc78ed63aeaec390b06cf3dcb318e6e835414d
--- /dev/null
+++ b/deepscreen/data/dti_datamodule.py
@@ -0,0 +1,314 @@
+# from itertools import product
+from collections import namedtuple
+from numbers import Number
+from typing import Any, Dict, Optional, Sequence, Union, Literal
+
+# import numpy as np
+import pandas as pd
+from lightning import LightningDataModule
+from torch.utils.data import Dataset, DataLoader, random_split
+
+from deepscreen.data.utils.label import label_transform
+from deepscreen.data.utils.collator import collate_fn
+from deepscreen.data.utils.sampler import SafeBatchSampler
+
+
+class DTIDataset(Dataset):
+ def __init__(
+ self,
+ task: Literal['regression', 'binary', 'multiclass'],
+ n_classes: Optional[int],
+ data_dir: str,
+ dataset_name: str,
+ drug_featurizer: callable,
+ protein_featurizer: callable,
+ thresholds: Optional[Union[Number, Sequence[Number]]] = None,
+ discard_intermediate: Optional[bool] = False,
+ ):
+ df = pd.read_csv(
+ f'{data_dir}{dataset_name}.csv',
+ header=0, sep=',',
+ usecols=lambda x: x in ['X1', 'ID1', 'X2', 'ID2', 'Y', 'U'],
+ dtype={'X1': 'str', 'ID1': 'str',
+ 'X2': 'str', 'ID2': 'str',
+ 'Y': 'float32', 'U': 'str'}
+ )
+ # if 'ID1' in df:
+ # self.x1_to_id1 = dict(zip(df['X1'], df['ID1']))
+ # if 'ID2' in df:
+ # self.x2_to_id2 = dict(zip(df['X2'], df['ID2']))
+ # self.id2_to_indexes = dict(zip(df['ID2'], range(len(df['ID2']))))
+ # self.x2_to_indexes = dict(zip(df['X2'], range(len(df['X2']))))
+
+ # # train and eval mode data processing (fully labelled)
+ # if 'Y' in df.columns and df['Y'].notnull().all():
+
+ # Forward-fill all non-label columns
+ df.loc[:, df.columns != 'Y'] = df.loc[:, df.columns != 'Y'].ffill(axis=0)
+
+ if 'Y' in df:
+ # Transform labels
+ df['Y'] = df['Y'].apply(label_transform, units=df.get('U', None), thresholds=thresholds,
+ discard_intermediate=discard_intermediate).astype('float32')
+
+ # Filter out rows with a NaN in Y (missing values)
+ df.dropna(subset=['Y'], inplace=True)
+
+ # Validate target labels for training/testing
+ # TODO: check sklearn.utils.multiclass.check_classification_targets
+ match task:
+ case 'regression':
+ assert all(df['Y'].apply(lambda x: isinstance(x, Number))), \
+ f"Y for task `regression` must be numeric; got {set(df['Y'].apply(type))}."
+ case 'binary':
+ assert all(df['Y'].isin([0, 1])), \
+ f"Y for task `binary` (classification) must be 0 or 1, but Y got {pd.unique(df['Y'])}." \
+ "\nYou may set `thresholds` to discretize continuous labels."
+ case 'multiclass':
+ assert n_classes >= 3, f'n_classes for task `multiclass` (classification) must be at least 3.'
+ assert all(df['Y'].apply(lambda x: x.is_integer() and x >= 0)), \
+ f"Y for task `multiclass` (classification) must be non-negative integers, " \
+ f"but Y got {pd.unique(df['Y'])}." \
+ "\nYou may set `thresholds` to discretize continuous labels."
+ target_n_unique = df['Y'].nunique()
+ assert target_n_unique == n_classes, \
+ f"You have set n_classes for task `multiclass` (classification) task to {n_classes}, " \
+ f"but Y has {target_n_unique} unique labels."
+
+ # # Predict mode data processing
+ # else:
+ # df = pd.DataFrame(product(df['X1'].dropna(), df['X2'].dropna()), columns=['X1', 'X2'])
+ # if hasattr(self, "x1_to_id1"):
+ # df['ID1'] = df['X1'].map(self.x1_to_id1)
+ # if hasattr(self, "x1_to_id2"):
+ # df['ID2'] = df['X2'].map(self.x2_to_id2)
+
+ # self.smiles = df['X1']
+ # self.fasta = df['X2']
+ # self.smiles_ids = df.get('ID1', df['X1'])
+ # self.fasta_ids = df.get('ID2', df['X2'])
+ # self.labels = df.get('Y', None)
+
+ self.df = df
+ self.drug_featurizer = drug_featurizer if drug_featurizer is not None else (lambda x: x)
+ self.protein_featurizer = protein_featurizer if protein_featurizer is not None else (lambda x: x)
+ self.n_classes = df['Y'].nunique()
+ # self.train = train
+
+ self.Data = namedtuple('Data', ['FT1', 'ID1', 'FT2', 'ID2', 'Y'])
+
+ def __len__(self):
+ return len(self.df.index)
+
+ def __getitem__(self, idx):
+ sample = self.df.loc[idx]
+ return self.Data(
+ FT1=self.drug_featurizer(sample['X1']),
+ ID1=sample.get('ID1', sample['X1']),
+ FT2=self.protein_featurizer(sample['X2']),
+ ID2=sample.get('ID2', sample['X2']),
+ Y=sample.get('Y')
+ )
+ # {
+ # 'FT1': self.drug_featurizer(sample['X1']),
+ # 'ID1': sample.get('ID1', sample['X1']),
+ # 'FT2': self.protein_featurizer(sample['X2']),
+ # 'ID2': sample.get('ID2', sample['X2']),
+ # 'Y': sample.get('Y')
+ # }
+ # if self.train:
+ # sample = self.drug_featurizer(self.smiles[idx]), self.protein_featurizer(self.fasta[idx]), self.labels[idx]
+ # sample = {
+ # 'FT1': self.drug_featurizer(self.smiles[idx]),
+ # 'FT2': self.protein_featurizer(self.fasta[idx]),
+ # 'ID2': self.smiles_ids[idx],
+ # }
+ # else:
+ # # sample = self.drug_featurizer(self.smiles[idx]), self.protein_featurizer(self.fasta[idx])
+ # sample = {
+ # 'FT1': self.drug_featurizer(self.smiles[idx]),
+ # 'FT2': self.protein_featurizer(self.fasta[idx]),
+ # }
+ #
+ # if all([True if n is not None else False for n in sample.values()]):
+ # return sample # | {
+ # # 'ID1': self.smiles_ids[idx],
+ # # 'X1': self.drug_featurizer(self.smiles[idx]),
+ # # 'ID2': self.fasta_ids[idx],
+ # # 'X2': self.protein_featurizer(self.fasta[idx]),
+ # # }
+ # else:
+ # return self.__getitem__(np.random.randint(0, self.size))
+
+
+class DTIdatamodule(LightningDataModule):
+ """
+ DTI DataModule
+
+ A DataModule implements 5 key methods:
+
+ def prepare_data(self):
+ # things to do on 1 GPU/TPU (not on every GPU/TPU in DDP)
+ # download data, pre-process, split, save to disk, etc.
+ def setup(self, stage):
+ # things to do on every process in DDP
+ # load data, set variables, etc.
+ def train_dataloader(self):
+ # return train dataloader
+ def val_dataloader(self):
+ # return validation dataloader
+ def test_dataloader(self):
+ # return test dataloader
+ def teardown(self):
+ # called on every process in DDP
+ # clean up after fit or test
+
+ This allows you to share a full dataset without explaining how to download,
+ split, transform and process the data.
+
+ Read the docs:
+ https://pytorch-lightning.readthedocs.io/en/latest/extensions/datamodules.html
+ """
+
+ def __init__(
+ self,
+ task: Literal['regression', 'binary', 'multiclass'],
+ n_classes: Optional[int],
+ train: bool,
+ drug_featurizer: callable,
+ protein_featurizer: callable,
+ batch_size: int,
+ train_val_test_split: Optional[Sequence[Number]],
+ num_workers: int = 0,
+ thresholds: Optional[Union[Number, Sequence[Number]]] = None,
+ pin_memory: bool = False,
+ data_dir: str = "data/",
+ dataset_name: Optional[str] = None,
+ split: Optional[callable] = random_split,
+ ):
+ super().__init__()
+
+ # this line allows to access init params with 'self.hparams' attribute
+ # also ensures init params will be stored in ckpt
+ self.save_hyperparameters(logger=False)
+
+ # data processing
+ self.data_split = split
+
+ self.data_train: Optional[Dataset] = None
+ self.data_val: Optional[Dataset] = None
+ self.data_test: Optional[Dataset] = None
+ self.data_predict: Optional[Dataset] = None
+
+ def prepare_data(self):
+ """
+ Download data if needed.
+ Do not use it to assign state (e.g., self.x = x).
+ """
+
+ def setup(self, stage: Optional[str] = None, encoding: str = None):
+ """
+ Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`.
+ This method is called by lightning with both `trainer.fit()` and `trainer.test()`, so be
+ careful not to execute data splitting twice.
+ """
+ # TODO test SafeBatchSampler (which skips samples with any None without introducing variable batch size)
+ # load and split datasets only if not loaded in initialization
+ if not any([self.data_train, self.data_val, self.data_test, self.data_predict]):
+ dataset = DTIDataset(
+ task=self.hparams.task,
+ n_classes=self.hparams.n_classes,
+ data_dir=self.hparams.data_dir,
+ drug_featurizer=self.hparams.drug_featurizer,
+ protein_featurizer=self.hparams.protein_featurizer,
+ dataset_name=self.hparams.dataset_name,
+ thresholds=self.hparams.thresholds,
+ )
+
+ if self.hparams.train:
+ self.data_train, self.data_val, self.data_test = self.data_split(
+ dataset=dataset,
+ lengths=self.hparams.train_val_test_split
+ )
+ else:
+ self.data_test = self.data_predict = dataset
+
+ def train_dataloader(self):
+ return DataLoader(
+ dataset=self.data_train,
+ batch_sampler=SafeBatchSampler(
+ data_source=self.data_train,
+ batch_size=self.hparams.batch_size,
+ drop_last=True,
+ shuffle=True,
+ ),
+ # batch_size=self.hparams.batch_size,
+ # shuffle=True,
+ num_workers=self.hparams.num_workers,
+ pin_memory=self.hparams.pin_memory,
+ collate_fn=collate_fn,
+ persistent_workers=True if self.hparams.num_workers > 0 else False
+ )
+
+ def val_dataloader(self):
+ return DataLoader(
+ dataset=self.data_val,
+ batch_sampler=SafeBatchSampler(
+ data_source=self.data_val,
+ batch_size=self.hparams.batch_size,
+ drop_last=False,
+ shuffle=False,
+ ),
+ # batch_size=self.hparams.batch_size,
+ # shuffle=False,
+ num_workers=self.hparams.num_workers,
+ pin_memory=self.hparams.pin_memory,
+ collate_fn=collate_fn,
+ persistent_workers=True if self.hparams.num_workers > 0 else False
+ )
+
+ def test_dataloader(self):
+ return DataLoader(
+ dataset=self.data_test,
+ batch_sampler=SafeBatchSampler(
+ data_source=self.data_test,
+ batch_size=self.hparams.batch_size,
+ drop_last=False,
+ shuffle=False,
+ ),
+ # batch_size=self.hparams.batch_size,
+ # shuffle=False,
+ num_workers=self.hparams.num_workers,
+ pin_memory=self.hparams.pin_memory,
+ collate_fn=collate_fn,
+ persistent_workers=True if self.hparams.num_workers > 0 else False
+ )
+
+ def predict_dataloader(self):
+ return DataLoader(
+ dataset=self.data_predict,
+ batch_sampler=SafeBatchSampler(
+ data_source=self.data_predict,
+ batch_size=self.hparams.batch_size,
+ drop_last=False,
+ shuffle=False,
+ ),
+ # batch_size=self.hparams.batch_size,
+ # shuffle=False,
+ num_workers=self.hparams.num_workers,
+ pin_memory=self.hparams.pin_memory,
+ collate_fn=collate_fn,
+ persistent_workers=True if self.hparams.num_workers > 0 else False
+ )
+
+ def teardown(self, stage: Optional[str] = None):
+ """Clean up after fit or test."""
+ pass
+
+ def state_dict(self):
+ """Extra things to save to checkpoint."""
+ return {}
+
+ def load_state_dict(self, state_dict: Dict[str, Any]):
+ """Things to do when loading checkpoint."""
+ pass
diff --git a/deepscreen/data/entity_datamodule.py b/deepscreen/data/entity_datamodule.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b6b2d46d251c5f07bbf68a5639d538b37f6432b
--- /dev/null
+++ b/deepscreen/data/entity_datamodule.py
@@ -0,0 +1,167 @@
+from numbers import Number
+from pathlib import Path
+from typing import Any, Dict, Optional, Sequence, Type
+
+from lightning import LightningDataModule
+from sklearn.base import TransformerMixin
+from torch.utils.data import Dataset, DataLoader
+
+from deepscreen.data.utils import collate_fn, SafeBatchSampler
+from deepscreen.data.utils.dataset import BaseEntityDataset
+
+
+class EntityDataModule(LightningDataModule):
+ """
+ def prepare_data(self):
+ # things to do on 1 GPU/TPU (not on every GPU/TPU in DDP)
+ # download data, pre-process, split, save to disk, etc.
+ def setup(self, stage):
+ # things to do on every process in DDP
+ # load data, set variables, etc.
+ def train_dataloader(self):
+ # return train dataloader
+ def val_dataloader(self):
+ # return validation dataloader
+ def test_dataloader(self):
+ # return test dataloader
+ def teardown(self):
+ # called on every process in DDP
+ # clean up after fit or test
+ """
+ def __init__(
+ self,
+ dataset: type[BaseEntityDataset],
+ transformer: type[TransformerMixin],
+ train: bool,
+ batch_size: int,
+ data_dir: str = "data/",
+ data_file: Optional[str] = None,
+ train_val_test_split: Optional[Sequence[Number], Sequence[str]] = None,
+ split: Optional[callable] = None,
+ num_workers: int = 0,
+ pin_memory: bool = False,
+ ):
+ super().__init__()
+
+ # data processing
+ self.split = split
+
+ if train:
+ if all([data_file, split]):
+ if all(isinstance(split, Number) for split in train_val_test_split):
+ pass
+ else:
+ raise ValueError('`train_val_test_split` must be a sequence of 3 numbers '
+ '(float for percentages and int for sample numbers) if '
+ '`data_file` and `split` have been specified.')
+ elif all(isinstance(split, str) for split in train_val_test_split) and not any([data_file, split]):
+ self.train_data = dataset(dataset_path=str(Path(data_dir) / train_val_test_split[0]))
+ self.val_data = dataset(dataset_path=str(Path(data_dir) / train_val_test_split[1]))
+ self.test_data = dataset(dataset_path=str(Path(data_dir) / train_val_test_split[2]))
+ else:
+ raise ValueError('For training (train=True), you must specify either '
+ '`dataset_name` and `split` with `train_val_test_split` of 3 numbers or '
+ 'solely `train_val_test_split` of 3 data file names.')
+ else:
+ if data_file and not any([split, train_val_test_split]):
+ self.test_data = self.predict_data = dataset(dataset_path=str(Path(data_dir) / data_file))
+ else:
+ raise ValueError("For testing/predicting (train=False), you must specify only `data_file` without "
+ "`train_val_test_split` or `split`")
+
+ # this line allows to access init params with 'self.hparams' attribute
+ # also ensures init params will be stored in ckpt
+ self.save_hyperparameters(logger=False)
+ def prepare_data(self):
+ """
+ Download data if needed.
+ Do not use it to assign state (e.g., self.x = x).
+ """
+
+ def setup(self, stage: Optional[str] = None, encoding: str = None):
+ """
+ Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`.
+ This method is called by lightning with both `trainer.fit()` and `trainer.test()`, so be
+ careful not to execute data splitting twice.
+ """
+ # TODO test SafeBatchSampler (which skips samples with any None without introducing variable batch size)
+ # TODO: find a way to apply transformer.fit_transform only to train and transformer.transform only to val, test
+ # load and split datasets only if not loaded in initialization
+ if not any([self.train_data, self.test_data, self.val_data, self.predict_data]):
+ self.train_data, self.val_data, self.test_data = self.split(
+ dataset=self.hparams.dataset(data_dir=self.hparams.data_dir,
+ dataset_name=self.hparams.train_dataset_name),
+ lengths=self.hparams.train_val_test_split
+ )
+
+ def train_dataloader(self):
+ return DataLoader(
+ dataset=self.train_data,
+ batch_sampler=SafeBatchSampler(
+ data_source=self.train_data,
+ batch_size=self.hparams.batch_size,
+ shuffle=True),
+ # batch_size=self.hparams.batch_size,
+ # shuffle=True,
+ num_workers=self.hparams.num_workers,
+ pin_memory=self.hparams.pin_memory,
+ collate_fn=collate_fn,
+ persistent_workers=True if self.hparams.num_workers > 0 else False
+ )
+
+ def val_dataloader(self):
+ return DataLoader(
+ dataset=self.val_data,
+ batch_sampler=SafeBatchSampler(
+ data_source=self.val_data,
+ batch_size=self.hparams.batch_size,
+ shuffle=False),
+ # batch_size=self.hparams.batch_size,
+ # shuffle=False,
+ num_workers=self.hparams.num_workers,
+ pin_memory=self.hparams.pin_memory,
+ collate_fn=collate_fn,
+ persistent_workers=True if self.hparams.num_workers > 0 else False
+ )
+
+ def test_dataloader(self):
+ return DataLoader(
+ dataset=self.test_data,
+ batch_sampler=SafeBatchSampler(
+ data_source=self.test_data,
+ batch_size=self.hparams.batch_size,
+ shuffle=False),
+ # batch_size=self.hparams.batch_size,
+ # shuffle=False,
+ num_workers=self.hparams.num_workers,
+ pin_memory=self.hparams.pin_memory,
+ collate_fn=collate_fn,
+ persistent_workers=True if self.hparams.num_workers > 0 else False
+ )
+
+ def predict_dataloader(self):
+ return DataLoader(
+ dataset=self.predict_data,
+ batch_sampler=SafeBatchSampler(
+ data_source=self.predict_data,
+ batch_size=self.hparams.batch_size,
+ shuffle=False),
+ # batch_size=self.hparams.batch_size,
+ # shuffle=False,
+ num_workers=self.hparams.num_workers,
+ pin_memory=self.hparams.pin_memory,
+ collate_fn=collate_fn,
+ persistent_workers=True if self.hparams.num_workers > 0 else False
+ )
+
+ def teardown(self, stage: Optional[str] = None):
+ """Clean up after fit or test."""
+ pass
+
+ def state_dict(self):
+ """Extra things to save to checkpoint."""
+ return {}
+
+ def load_state_dict(self, state_dict: Dict[str, Any]):
+ """Things to do when loading checkpoint."""
+ pass
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new file mode 100644
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diff --git a/deepscreen/data/featurizers/categorical.py b/deepscreen/data/featurizers/categorical.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f74dbee492801d464b02098f2b6138194088064
--- /dev/null
+++ b/deepscreen/data/featurizers/categorical.py
@@ -0,0 +1,86 @@
+import numpy as np
+
+# Sets of KNOWN characters in SMILES and FASTA sequences
+# Use list instead of set to preserve character order
+SMILES_CHARSET = ('#', '%', ')', '(', '+', '-', '.', '1', '0', '3', '2', '5', '4',
+ '7', '6', '9', '8', '=', 'A', 'C', 'B', 'E', 'D', 'G', 'F', 'I',
+ 'H', 'K', 'M', 'L', 'O', 'N', 'P', 'S', 'R', 'U', 'T', 'W', 'V',
+ 'Y', '[', 'Z', ']', '_', 'a', 'c', 'b', 'e', 'd', 'g', 'f', 'i',
+ 'h', 'm', 'l', 'o', 'n', 's', 'r', 'u', 't', 'y')
+FASTA_CHARSET = ('A', 'C', 'B', 'E', 'D', 'G', 'F', 'I', 'H', 'K', 'M', 'L', 'O',
+ 'N', 'Q', 'P', 'S', 'R', 'U', 'T', 'W', 'V', 'Y', 'X', 'Z')
+
+# Check uniqueness, create character-index dicts, and add '?' for unknown characters as index 0
+assert len(SMILES_CHARSET) == len(set(SMILES_CHARSET)), 'SMILES_CHARSET has duplicate characters.'
+SMILES_CHARSET_IDX = {character: index+1 for index, character in enumerate(SMILES_CHARSET)} | {'?': 0}
+
+assert len(FASTA_CHARSET) == len(set(FASTA_CHARSET)), 'FASTA_CHARSET has duplicate characters.'
+FASTA_CHARSET_IDX = {character: index+1 for index, character in enumerate(FASTA_CHARSET)} | {'?': 0}
+
+
+def sequence_to_onehot(sequence: str, charset, max_sequence_length: int):
+ assert len(charset) == len(set(charset)), '`charset` contains duplicate characters.'
+ charset_idx = {character: index+1 for index, character in enumerate(charset)} | {'?': 0}
+
+ onehot = np.zeros((max_sequence_length, len(charset_idx)), dtype=int)
+ for index, character in enumerate(sequence[:max_sequence_length]):
+ onehot[index, charset_idx.get(character, 0)] = 1
+
+ return onehot.transpose()
+
+
+def sequence_to_label(sequence: str, charset, max_sequence_length: int):
+ assert len(charset) == len(set(charset)), '`charset` contains duplicate characters.'
+ charset_idx = {character: index+1 for index, character in enumerate(charset)} | {'?': 0}
+
+ label = np.zeros(max_sequence_length, dtype=int)
+ for index, character in enumerate(sequence[:max_sequence_length]):
+ label[index] = charset_idx.get(character, 0)
+
+ return label
+
+
+def smiles_to_onehot(smiles: str, smiles_charset=SMILES_CHARSET, max_sequence_length: int = 100): # , in_channels: int = len(SMILES_CHARSET)
+ # assert len(SMILES_CHARSET) == len(set(SMILES_CHARSET)), 'SMILES_CHARSET has duplicate characters.'
+ # onehot = np.zeros((max_sequence_length, len(SMILES_CHARSET_IDX)))
+ # for index, character in enumerate(smiles[:max_sequence_length]):
+ # onehot[index, SMILES_CHARSET_IDX.get(character, 0)] = 1
+ # return onehot.transpose()
+ return sequence_to_onehot(smiles, smiles_charset, max_sequence_length)
+
+
+def smiles_to_label(smiles: str, smiles_charset=SMILES_CHARSET, max_sequence_length: int = 100): # , in_channels: int = len(SMILES_CHARSET)
+ # label = np.zeros(max_sequence_length)
+ # for index, character in enumerate(smiles[:max_sequence_length]):
+ # label[index] = SMILES_CHARSET_IDX.get(character, 0)
+ # return label
+ return sequence_to_label(smiles, smiles_charset, max_sequence_length)
+
+
+def fasta_to_onehot(fasta: str, fasta_charset=FASTA_CHARSET, max_sequence_length: int = 1000): # in_channels: int = len(FASTA_CHARSET)
+ # onehot = np.zeros((max_sequence_length, len(FASTA_CHARSET_IDX)))
+ # for index, character in enumerate(fasta[:max_sequence_length]):
+ # onehot[index, FASTA_CHARSET_IDX.get(character, 0)] = 1
+ # return onehot.transpose()
+ return sequence_to_onehot(fasta, fasta_charset, max_sequence_length)
+
+
+def fasta_to_label(fasta: str, fasta_charset=FASTA_CHARSET, max_sequence_length: int = 1000): # in_channels: int = len(FASTA_CHARSET)
+ # label = np.zeros(max_sequence_length)
+ # for index, character in enumerate(fasta[:max_sequence_length]):
+ # label[index] = FASTA_CHARSET_IDX.get(character, 0)
+ # return label
+ return sequence_to_label(fasta, fasta_charset, max_sequence_length)
+
+
+def one_of_k_encoding(x, allowable_set):
+ if x not in allowable_set:
+ raise Exception("input {0} not in allowable set{1}:".format(x, allowable_set))
+ return list(map(lambda s: x == s, allowable_set))
+
+
+def one_of_k_encoding_unk(x, allowable_set):
+ """Maps inputs not in the allowable set to the last element."""
+ if x not in allowable_set:
+ x = allowable_set[-1]
+ return list(map(lambda s: x == s, allowable_set))
diff --git a/deepscreen/data/featurizers/chem.py b/deepscreen/data/featurizers/chem.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f08d11e1e81681157001e9eb13598064d5ea222
--- /dev/null
+++ b/deepscreen/data/featurizers/chem.py
@@ -0,0 +1,48 @@
+"""
+Mainly adapted from MolMap:
+https://github.com/shenwanxiang/bidd-molmap/tree/master/molmap/feature/fingerprint
+"""
+import numpy as np
+from rdkit import Chem, DataStructs
+from rdkit.Chem import AllChem
+from rdkit.Chem.Fingerprints import FingerprintMols
+from rdkit.Chem.rdReducedGraphs import GetErGFingerprint
+
+from deepscreen import get_logger
+
+log = get_logger(__name__)
+
+
+def smiles_to_erg(smiles):
+ try:
+ mol = Chem.MolFromSmiles(smiles)
+ features = np.array(GetErGFingerprint(mol), dtype=bool)
+ return features
+ except Exception as e:
+ log.warning(f"Failed to convert SMILES ({smiles}) to ErGFP due to {str(e)}")
+ return None
+
+
+def smiles_to_morgan(smiles, radius=2, n_bits=1024):
+ try:
+ mol = Chem.MolFromSmiles(smiles)
+ features_vec = AllChem.GetMorganFingerprintAsBitVect(mol, radius=radius, nBits=n_bits)
+ features = np.zeros((1,))
+ DataStructs.ConvertToNumpyArray(features_vec, features)
+ except Exception as e:
+ log.warning(f"Failed to convert SMILES ({smiles}) to ErGFP due to {str(e)}")
+ return None
+
+
+def smiles_to_daylight(smiles):
+ try:
+ NumFinger = 2048
+ mol = Chem.MolFromSmiles(smiles)
+ bv = FingerprintMols.FingerprintMol(mol)
+ temp = tuple(bv.GetOnBits())
+ features = np.zeros((NumFinger,))
+ features[np.array(temp)] = 1
+ except:
+ print(f'RDKit could not find this SMILES: {smiles} convert to all 0 features')
+ features = np.zeros((2048,))
+ return features.astype(int)
diff --git a/deepscreen/data/featurizers/fcs.py b/deepscreen/data/featurizers/fcs.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f34b2cfb8f9b6079de8a4c51bbefc00cd9cd51c
--- /dev/null
+++ b/deepscreen/data/featurizers/fcs.py
@@ -0,0 +1,67 @@
+from importlib import resources
+
+import numpy as np
+import pandas as pd
+from subword_nmt.apply_bpe import BPE
+import codecs
+
+vocab_path = resources.files('deepscreen').parent.joinpath('resources/vocabs/ESPF/protein_codes_uniprot.txt')
+bpe_codes_protein = codecs.open(vocab_path)
+protein_bpe = BPE(bpe_codes_protein, merges=-1, separator='')
+
+sub_csv_path = resources.files('deepscreen').parent.joinpath('resources/vocabs/ESPF/subword_units_map_uniprot.csv')
+sub_csv = pd.read_csv(sub_csv_path)
+idx2word_protein = sub_csv['index'].values
+words2idx_protein = dict(zip(idx2word_protein, range(0, len(idx2word_protein))))
+
+vocab_path = resources.files('deepscreen').parent.joinpath('resources/vocabs/ESPF/drug_codes_chembl.txt')
+bpe_codes_drug = codecs.open(vocab_path)
+drug_bpe = BPE(bpe_codes_drug, merges=-1, separator='')
+
+sub_csv_path = resources.files('deepscreen').parent.joinpath('resources/vocabs/ESPF/subword_units_map_chembl.csv')
+sub_csv = pd.read_csv(sub_csv_path)
+idx2word_drug = sub_csv['index'].values
+words2idx_drug = dict(zip(idx2word_drug, range(0, len(idx2word_drug))))
+
+
+def protein_to_embedding(x, max_sequence_length):
+ max_p = max_sequence_length
+ t1 = protein_bpe.process_line(x).split() # split
+ try:
+ i1 = np.asarray([words2idx_protein[i] for i in t1]) # index
+ except:
+ i1 = np.array([0])
+ # print(x)
+
+ l = len(i1)
+
+ if l < max_p:
+ i = np.pad(i1, (0, max_p - l), 'constant', constant_values=0)
+ input_mask = ([1] * l) + ([0] * (max_p - l))
+ else:
+ i = i1[:max_p]
+ input_mask = [1] * max_p
+
+ return i, np.asarray(input_mask)
+
+
+def drug_to_embedding(x, max_sequence_length):
+ max_d = max_sequence_length
+ t1 = drug_bpe.process_line(x).split() # split
+ try:
+ i1 = np.asarray([words2idx_drug[i] for i in t1]) # index
+ except:
+ i1 = np.array([0])
+ # print(x)
+
+ l = len(i1)
+
+ if l < max_d:
+ i = np.pad(i1, (0, max_d - l), 'constant', constant_values=0)
+ input_mask = ([1] * l) + ([0] * (max_d - l))
+
+ else:
+ i = i1[:max_d]
+ input_mask = [1] * max_d
+
+ return i, np.asarray(input_mask)
diff --git a/deepscreen/data/featurizers/fingerprint/__init__.py b/deepscreen/data/featurizers/fingerprint/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..88551df57996188af486ac907d93b2e958bf6391
--- /dev/null
+++ b/deepscreen/data/featurizers/fingerprint/__init__.py
@@ -0,0 +1,45 @@
+from typing import Literal
+
+from .atompairs import GetAtomPairFPs
+from .avalonfp import GetAvalonFPs
+from .rdkitfp import GetRDkitFPs
+from .morganfp import GetMorganFPs
+from .estatefp import GetEstateFPs
+from .maccskeys import GetMACCSFPs
+from .pharmErGfp import GetPharmacoErGFPs
+from .pharmPointfp import GetPharmacoPFPs
+from .pubchemfp import GetPubChemFPs
+from .torsions import GetTorsionFPs
+from .mhfp6 import GetMHFP6
+from .map4 import GetMAP4
+from rdkit import Chem
+
+from deepscreen import get_logger
+
+log = get_logger(__name__)
+
+FP_MAP = {
+ 'MorganFP': GetMorganFPs,
+ 'RDkitFP': GetRDkitFPs,
+ 'AtomPairFP': GetAtomPairFPs,
+ 'TorsionFP': GetTorsionFPs,
+ 'AvalonFP': GetAvalonFPs,
+ 'EstateFP': GetEstateFPs,
+ 'MACCSFP': GetMACCSFPs,
+ 'PharmacoErGFP': GetPharmacoErGFPs,
+ 'PharmacoPFP': GetPharmacoPFPs,
+ 'PubChemFP': GetPubChemFPs,
+ 'MHFP6': GetMHFP6,
+ 'MAP4': GetMAP4,
+}
+
+
+def smiles_to_fingerprint(smiles, fingerprint: Literal[tuple(FP_MAP.keys())], **kwargs):
+ func = FP_MAP[fingerprint]
+ try:
+ mol = Chem.MolFromSmiles(smiles)
+ arr = func(mol, **kwargs)
+ return arr
+ except Exception as e:
+ log.warning(f"Failed to convert SMILES ({smiles}) to {fingerprint} due to {str(e)}")
+ return None
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diff --git a/deepscreen/data/featurizers/fingerprint/__pycache__/torsions.cpython-311.pyc b/deepscreen/data/featurizers/fingerprint/__pycache__/torsions.cpython-311.pyc
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diff --git a/deepscreen/data/featurizers/fingerprint/atompairs.py b/deepscreen/data/featurizers/fingerprint/atompairs.py
new file mode 100644
index 0000000000000000000000000000000000000000..336a90777ccec7664740b88da14be8cb07855dd1
--- /dev/null
+++ b/deepscreen/data/featurizers/fingerprint/atompairs.py
@@ -0,0 +1,18 @@
+from rdkit.Chem.AtomPairs import Pairs
+from rdkit.Chem import DataStructs
+import numpy as np
+
+_type = 'topological-based'
+
+
+def GetAtomPairFPs(mol, nBits=2048, binary=True):
+ '''
+ atompairs fingerprints
+ '''
+ fp = Pairs.GetHashedAtomPairFingerprint(mol, nBits=nBits)
+ if binary:
+ arr = np.zeros((0,), dtype=np.bool_)
+ else:
+ arr = np.zeros((0,), dtype=np.int8)
+ DataStructs.ConvertToNumpyArray(fp, arr)
+ return arr
diff --git a/deepscreen/data/featurizers/fingerprint/avalonfp.py b/deepscreen/data/featurizers/fingerprint/avalonfp.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e70f05f265993fadbed9075574a24034ae9b9ad
--- /dev/null
+++ b/deepscreen/data/featurizers/fingerprint/avalonfp.py
@@ -0,0 +1,16 @@
+from rdkit.Chem import DataStructs
+from rdkit.Avalon.pyAvalonTools import GetAvalonFP as GAFP
+import numpy as np
+
+_type = 'topological-based'
+
+
+def GetAvalonFPs(mol, nBits=2048):
+ '''
+ Avalon_fingerprints: https://pubs.acs.org/doi/pdf/10.1021/ci050413p
+ '''
+
+ fp = GAFP(mol, nBits=nBits)
+ arr = np.zeros((0,), dtype=np.bool_)
+ DataStructs.ConvertToNumpyArray(fp, arr)
+ return arr
diff --git a/deepscreen/data/featurizers/fingerprint/estatefp.py b/deepscreen/data/featurizers/fingerprint/estatefp.py
new file mode 100644
index 0000000000000000000000000000000000000000..369b20756b947c66496ecb29719915e9c806253f
--- /dev/null
+++ b/deepscreen/data/featurizers/fingerprint/estatefp.py
@@ -0,0 +1,12 @@
+from rdkit.Chem.EState import Fingerprinter
+import numpy as np
+
+_type = 'Estate-based'
+
+
+def GetEstateFPs(mol):
+ '''
+ 79 bits Estate fps
+ '''
+ x = Fingerprinter.FingerprintMol(mol)[0]
+ return x.astype(np.bool_)
diff --git a/deepscreen/data/featurizers/fingerprint/maccskeys.py b/deepscreen/data/featurizers/fingerprint/maccskeys.py
new file mode 100644
index 0000000000000000000000000000000000000000..79ba3030c49ca405b1e91031b92742a9a49753b3
--- /dev/null
+++ b/deepscreen/data/featurizers/fingerprint/maccskeys.py
@@ -0,0 +1,25 @@
+from rdkit.Chem import AllChem
+from rdkit.Chem import DataStructs
+import numpy as np
+import pandas as pd
+import os
+
+_type = 'SMARTS-based'
+
+file_path = os.path.dirname(__file__)
+
+
+def GetMACCSFPs(mol):
+ '''
+ 166 bits
+ '''
+
+ fp = AllChem.GetMACCSKeysFingerprint(mol)
+
+ arr = np.zeros((0,), dtype=np.bool_)
+ DataStructs.ConvertToNumpyArray(fp, arr)
+ return arr
+
+
+def GetMACCSFPInfos():
+ return pd.read_excel(os.path.join(file_path, 'maccskeys.xlsx'))
diff --git a/deepscreen/data/featurizers/fingerprint/maccskeys.xlsx b/deepscreen/data/featurizers/fingerprint/maccskeys.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..3037fa603e73843000a97e6ed886b44cfcd5ebd9
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diff --git a/deepscreen/data/featurizers/fingerprint/map4.py b/deepscreen/data/featurizers/fingerprint/map4.py
new file mode 100644
index 0000000000000000000000000000000000000000..e60c9267d5ad273766657b7b6ac5263f167bc1ec
--- /dev/null
+++ b/deepscreen/data/featurizers/fingerprint/map4.py
@@ -0,0 +1,130 @@
+"""
+MinHashed Atom-pair Fingerprint, MAP
+orignal paper: Capecchi, Alice, Daniel Probst, and Jean-Louis Reymond. "One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome." Journal of Cheminformatics 12.1 (2020): 1-15. orignal code: https://github.com/reymond-group/map4, thanks their orignal work
+
+A small bug is fixed: https://github.com/reymond-group/map4/issues/6
+"""
+
+_type = 'topological-based'
+
+import itertools
+from collections import defaultdict
+
+import tmap as tm
+from mhfp.encoder import MHFPEncoder
+from rdkit import Chem
+from rdkit.Chem import rdmolops
+from rdkit.Chem.rdmolops import GetDistanceMatrix
+
+
+def to_smiles(mol):
+ return Chem.MolToSmiles(mol, canonical=True, isomericSmiles=False)
+
+
+class MAP4Calculator:
+ def __init__(self, dimensions=2048, radius=2, is_counted=False, is_folded=False, fold_dimensions=2048):
+ """
+ MAP4 calculator class
+ """
+ self.dimensions = dimensions
+ self.radius = radius
+ self.is_counted = is_counted
+ self.is_folded = is_folded
+ self.fold_dimensions = fold_dimensions
+
+ if self.is_folded:
+ self.encoder = MHFPEncoder(dimensions)
+ else:
+ self.encoder = tm.Minhash(dimensions)
+
+ def calculate(self, mol):
+ """Calculates the atom pair minhashed fingerprint
+ Arguments:
+ mol -- rdkit mol object
+ Returns:
+ tmap VectorUint -- minhashed fingerprint
+ """
+
+ atom_env_pairs = self._calculate(mol)
+ if self.is_folded:
+ return self._fold(atom_env_pairs)
+ return self.encoder.from_string_array(atom_env_pairs)
+
+ def calculate_many(self, mols):
+ """ Calculates the atom pair minhashed fingerprint
+ Arguments:
+ mols -- list of mols
+ Returns:
+ list of tmap VectorUint -- minhashed fingerprints list
+ """
+
+ atom_env_pairs_list = [self._calculate(mol) for mol in mols]
+ if self.is_folded:
+ return [self._fold(pairs) for pairs in atom_env_pairs_list]
+ return self.encoder.batch_from_string_array(atom_env_pairs_list)
+
+ def _calculate(self, mol):
+ return self._all_pairs(mol, self._get_atom_envs(mol))
+
+ def _fold(self, pairs):
+ fp_hash = self.encoder.hash(set(pairs))
+ return self.encoder.fold(fp_hash, self.fold_dimensions)
+
+ def _get_atom_envs(self, mol):
+ atoms_env = {}
+ for atom in mol.GetAtoms():
+ idx = atom.GetIdx()
+ for radius in range(1, self.radius + 1):
+ if idx not in atoms_env:
+ atoms_env[idx] = []
+ atoms_env[idx].append(MAP4Calculator._find_env(mol, idx, radius))
+ return atoms_env
+
+ @classmethod
+ def _find_env(cls, mol, idx, radius):
+ env = rdmolops.FindAtomEnvironmentOfRadiusN(mol, radius, idx)
+ atom_map = {}
+
+ submol = Chem.PathToSubmol(mol, env, atomMap=atom_map)
+ if idx in atom_map:
+ smiles = Chem.MolToSmiles(submol, rootedAtAtom=atom_map[idx], canonical=True, isomericSmiles=False)
+ return smiles
+ return ''
+
+ def _all_pairs(self, mol, atoms_env):
+ atom_pairs = []
+ distance_matrix = GetDistanceMatrix(mol)
+ num_atoms = mol.GetNumAtoms()
+ shingle_dict = defaultdict(int)
+ for idx1, idx2 in itertools.combinations(range(num_atoms), 2):
+ dist = str(int(distance_matrix[idx1][idx2]))
+
+ for i in range(self.radius):
+ env_a = atoms_env[idx1][i]
+ env_b = atoms_env[idx2][i]
+
+ ordered = sorted([env_a, env_b])
+
+ shingle = '{}|{}|{}'.format(ordered[0], dist, ordered[1])
+
+ if self.is_counted:
+ shingle_dict[shingle] += 1
+ shingle += '|' + str(shingle_dict[shingle])
+
+ atom_pairs.append(shingle.encode('utf-8'))
+ return list(set(atom_pairs))
+
+
+def GetMAP4(mol, nBits=2048, radius=2, fold_dimensions=None):
+ """
+ MAP4: radius=2
+ """
+ if fold_dimensions == None:
+ fold_dimensions = nBits
+
+ calc = MAP4Calculator(dimensions=nBits, radius=radius, is_counted=False, is_folded=True,
+ fold_dimensions=fold_dimensions)
+
+ arr = calc.calculate(mol)
+
+ return arr.astype(bool)
diff --git a/deepscreen/data/featurizers/fingerprint/mhfp6.py b/deepscreen/data/featurizers/fingerprint/mhfp6.py
new file mode 100644
index 0000000000000000000000000000000000000000..2befc6bd970a8cb6ac5738020cddec4e459802ff
--- /dev/null
+++ b/deepscreen/data/featurizers/fingerprint/mhfp6.py
@@ -0,0 +1,18 @@
+"""
+Probst, Daniel, and Jean-Louis Reymond. "A probabilistic molecular fingerprint for big data settings." Journal of cheminformatics 10.1 (2018): 66.'
+
+orignal code: https://github.com/reymond-group/mhfp
+
+"""
+
+from mhfp.encoder import MHFPEncoder
+
+
+def GetMHFP6(mol, nBits=2048, radius=3):
+ """
+ MHFP6: radius=3
+ """
+ encoder = MHFPEncoder(n_permutations=nBits)
+ hash_values = encoder.encode_mol(mol, radius=radius, rings=True, kekulize=True, min_radius=1)
+ arr = encoder.fold(hash_values, nBits)
+ return arr.astype(bool)
diff --git a/deepscreen/data/featurizers/fingerprint/mnimalfatures.fdef b/deepscreen/data/featurizers/fingerprint/mnimalfatures.fdef
new file mode 100644
index 0000000000000000000000000000000000000000..ed695f788d5c9b4911de46a52417ba0fa94f2afd
--- /dev/null
+++ b/deepscreen/data/featurizers/fingerprint/mnimalfatures.fdef
@@ -0,0 +1,53 @@
+AtomType NDonor [N&!H0&v3,N&!H0&+1&v4,n&H1&+0]
+AtomType ChalcDonor [O,S;H1;+0]
+DefineFeature SingleAtomDonor [{NDonor},{ChalcDonor},!$([D1]-[C;D3]=[O,S,N])]
+ Family Donor
+ Weights 1
+EndFeature
+
+AtomType NAcceptor [$([N&v3;H1,H2]-[!$(*=[O,N,P,S])])]
+Atomtype NAcceptor [$([N;v3;H0])]
+AtomType NAcceptor [$([n;+0])]
+AtomType ChalcAcceptor [$([O,S;H1;v2]-[!$(*=[O,N,P,S])])]
+AtomType ChalcAcceptor [O,S;H0;v2]
+Atomtype ChalcAcceptor [O,S;-]
+Atomtype ChalcAcceptor [o,s;+0]
+AtomType HalogenAcceptor [F]
+DefineFeature SingleAtomAcceptor [{NAcceptor},{ChalcAcceptor},{HalogenAcceptor}]
+ Family Acceptor
+ Weights 1
+EndFeature
+
+# this one is delightfully easy:
+DefineFeature AcidicGroup [C,S](=[O,S,P])-[O;H1,H0&-1]
+ Family NegIonizable
+ Weights 1.0,1.0,1.0
+EndFeature
+
+AtomType CarbonOrArom_NonCarbonyl [$([C,a]);!$([C,a](=O))]
+AtomType BasicNH2 [$([N;H2&+0][{CarbonOrArom_NonCarbonyl}])]
+AtomType BasicNH1 [$([N;H1&+0]([{CarbonOrArom_NonCarbonyl}])[{CarbonOrArom_NonCarbonyl}])]
+AtomType BasicNH0 [$([N;H0&+0]([{CarbonOrArom_NonCarbonyl}])([{CarbonOrArom_NonCarbonyl}])[{CarbonOrArom_NonCarbonyl}])]
+AtomType BasicNakedN [N,n;X2;+0]
+DefineFeature BasicGroup [{BasicNH2},{BasicNH1},{BasicNH0},{BasicNakedN}]
+ Family PosIonizable
+ Weights 1.0
+EndFeature
+
+# aromatic rings of various sizes:
+DefineFeature Arom5 a1aaaa1
+ Family Aromatic
+ Weights 1.0,1.0,1.0,1.0,1.0
+EndFeature
+DefineFeature Arom6 a1aaaaa1
+ Family Aromatic
+ Weights 1.0,1.0,1.0,1.0,1.0,1.0
+EndFeature
+DefineFeature Arom7 a1aaaaaa1
+ Family Aromatic
+ Weights 1.0,1.0,1.0,1.0,1.0,1.0,1.0
+EndFeature
+DefineFeature Arom8 a1aaaaaaa1
+ Family Aromatic
+ Weights 1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+EndFeature
diff --git a/deepscreen/data/featurizers/fingerprint/morganfp.py b/deepscreen/data/featurizers/fingerprint/morganfp.py
new file mode 100644
index 0000000000000000000000000000000000000000..43bef977bd69572fdbd491a7047e5f60d209805a
--- /dev/null
+++ b/deepscreen/data/featurizers/fingerprint/morganfp.py
@@ -0,0 +1,18 @@
+from rdkit.Chem import AllChem
+from rdkit.Chem import DataStructs
+import numpy as np
+
+
+def GetMorganFPs(mol, nBits=2048, radius=2, return_bitInfo=False):
+ """
+ ECFP4: radius=2
+ """
+ bitInfo = {}
+ fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius=radius,
+ bitInfo=bitInfo, nBits=nBits)
+ arr = np.zeros((0,), dtype=np.bool_)
+ DataStructs.ConvertToNumpyArray(fp, arr)
+
+ if return_bitInfo:
+ return arr, bitInfo
+ return arr
diff --git a/deepscreen/data/featurizers/fingerprint/pharmErGfp.py b/deepscreen/data/featurizers/fingerprint/pharmErGfp.py
new file mode 100644
index 0000000000000000000000000000000000000000..092dc850b1b354c8edfeffc8e83591a61d66a88e
--- /dev/null
+++ b/deepscreen/data/featurizers/fingerprint/pharmErGfp.py
@@ -0,0 +1,60 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Aug 17 16:54:12 2019
+
+@author: wanxiang.shen@u.nus.edu
+
+@calculate ErG fps, more info: https://pubs.acs.org/doi/full/10.1021/ci050457y#
+"""
+
+_type = 'Pharmacophore-based'
+
+import numpy as np
+from rdkit.Chem import AllChem
+
+## get info from : https://github.com/rdkit/rdkit/blob/d41752d558bf7200ab67b98cdd9e37f1bdd378de/Code/GraphMol/ReducedGraphs/ReducedGraphs.cpp
+Donor = ["[N;!H0;v3,v4&+1]", "[O,S;H1;+0]", "[n&H1&+0]"]
+
+Acceptor = ["[O,S;H1;v2;!$(*-*=[O,N,P,S])]", "[O;H0;v2]", "[O,S;v1;-]",
+ "[N;v3;!$(N-*=[O,N,P,S])]", "[n&H0&+0]", "[o;+0;!$([o]:n);!$([o]:c:n)]"]
+
+Positive = ["[#7;+]", "[N;H2&+0][$([C,a]);!$([C,a](=O))]",
+ "[N;H1&+0]([$([C,a]);!$([C,a](=O))])[$([C,a]);!$([C,a](=O))]",
+ "[N;H0&+0]([C;!$(C(=O))])([C;!$(C(=O))])[C;!$(C(=O))]"]
+
+Negative = ["[C,S](=[O,S,P])-[O;H1,-1]"]
+
+Hydrophobic = ["[C;D3,D4](-[CH3])-[CH3]", "[S;D2](-C)-C"]
+
+Aromatic = ["a"]
+
+PROPERTY_KEY = ["Donor", "Acceptor", "Positive", "Negative", "Hydrophobic", "Aromatic"]
+
+
+def GetPharmacoErGFPs(mol, fuzzIncrement=0.3, maxPath=21, binary=True, return_bitInfo=False):
+ '''
+ https://pubs.acs.org/doi/full/10.1021/ci050457y#
+ return maxPath*21 bits
+
+ size(v) = (n(n + 1)/2) * (maxDist - minDist + 1)
+
+ '''
+ minPath = 1
+
+ arr = AllChem.GetErGFingerprint(mol, fuzzIncrement=fuzzIncrement, maxPath=maxPath, minPath=minPath)
+ arr = arr.astype(np.float32)
+
+ if binary:
+ arr = arr.astype(np.bool_)
+
+ if return_bitInfo:
+ bitInfo = []
+ for i in range(len(PROPERTY_KEY)):
+ for j in range(i, len(PROPERTY_KEY)):
+ for path in range(minPath, maxPath + 1):
+ triplet = (PROPERTY_KEY[i], PROPERTY_KEY[j], path)
+ bitInfo.append(triplet)
+ return arr, bitInfo
+
+ return arr
diff --git a/deepscreen/data/featurizers/fingerprint/pharmPointfp.py b/deepscreen/data/featurizers/fingerprint/pharmPointfp.py
new file mode 100644
index 0000000000000000000000000000000000000000..543a518dc686ab33e3e88bfd6d526cb9f481f9db
--- /dev/null
+++ b/deepscreen/data/featurizers/fingerprint/pharmPointfp.py
@@ -0,0 +1,59 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Aug 17 16:54:12 2019
+
+@author: wanxiang.shen@u.nus.edu
+
+Combining a set of chemical features with the 2D (topological) distances between them gives a 2D pharmacophore. When the distances are binned, unique integer ids can be assigned to each of these pharmacophores and they can be stored in a fingerprint. Details of the encoding are in: https://www.rdkit.org/docs/RDKit_Book.html#ph4-figure
+"""
+
+_type = 'Pharmacophore-based'
+
+from rdkit.Chem.Pharm2D.SigFactory import SigFactory
+from rdkit.Chem.Pharm2D import Generate
+from rdkit.Chem import DataStructs
+from rdkit.Chem import ChemicalFeatures
+
+import numpy as np
+import os
+
+fdef = os.path.join(os.path.dirname(__file__), 'mnimalfatures.fdef')
+featFactory = ChemicalFeatures.BuildFeatureFactory(fdef)
+
+
+def GetPharmacoPFPs(mol,
+ bins=[(i, i + 1) for i in range(20)],
+ minPointCount=2,
+ maxPointCount=2,
+ return_bitInfo=False):
+ '''
+ Note: maxPointCont with 3 is slowly
+
+ bins = [(i,i+1) for i in range(20)],
+ maxPonitCount=2 for large-scale computation
+
+ '''
+ MysigFactory = SigFactory(featFactory,
+ trianglePruneBins=False,
+ minPointCount=minPointCount,
+ maxPointCount=maxPointCount)
+ MysigFactory.SetBins(bins)
+ MysigFactory.Init()
+
+ res = Generate.Gen2DFingerprint(mol, MysigFactory)
+ arr = np.array(list(res)).astype(np.bool_)
+ if return_bitInfo:
+ description = []
+ for i in range(len(res)):
+ description.append(MysigFactory.GetBitDescription(i))
+ return arr, description
+
+ return arr
+
+
+if __name__ == '__main__':
+ from rdkit import Chem
+
+ mol = Chem.MolFromSmiles('CC#CC(=O)NC1=NC=C2C(=C1)C(=NC=N2)NC3=CC(=C(C=C3)F)Cl')
+ a = GetPharmacoPFPs(mol, bins=[(i, i + 1) for i in range(20)], minPointCount=2, maxPointCount=2)
diff --git a/deepscreen/data/featurizers/fingerprint/pubchemfp.py b/deepscreen/data/featurizers/fingerprint/pubchemfp.py
new file mode 100644
index 0000000000000000000000000000000000000000..1865a3772d19c6d2c4d12d343fba3f69662d6498
--- /dev/null
+++ b/deepscreen/data/featurizers/fingerprint/pubchemfp.py
@@ -0,0 +1,1731 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Aug 25 20:29:36 2019
+
+@author: charleshen
+
+@Note: The code are copyed from PyBioMed, with a minor repair
+
+https://www.ncbi.nlm.nih.gov/pubmed/29556758
+
+these are SMARTS patterns corresponding to the PubChem fingerprints
+https://astro.temple.edu/~tua87106/list_fingerprints.pdf
+ftp://ftp.ncbi.nlm.nih.gov/pubchem/specifications/pubchem_fingerprints.txt
+
+"""
+
+_type = 'SMARTS-based'
+
+import numpy as np
+from rdkit import Chem
+from rdkit import DataStructs
+import os
+import pandas as pd
+
+smartsPatts = {
+ 1: ('[H]', 3), # 1-115
+ 2: ('[H]', 7),
+ 3: ('[H]', 15),
+ 4: ('[H]', 31),
+ 5: ('[Li]', 0),
+ 6: ('[Li]', 1),
+ 7: ('[B]', 0),
+ 8: ('[B]', 1),
+ 9: ('[B]', 3),
+ 10: ('[C]', 1),
+ 11: ('[C]', 3),
+ 12: ('[C]', 7),
+ 13: ('[C]', 15),
+ 14: ('[C]', 31),
+ 15: ('[N]', 0),
+ 16: ('[N]', 1),
+ 17: ('[N]', 3),
+ 18: ('[N]', 7),
+ 19: ('[O]', 0),
+ 20: ('[O]', 1),
+ 21: ('[O]', 3),
+ 22: ('[O]', 7),
+ 23: ('[O]', 15),
+ 24: ('[F]', 0),
+ 25: ('[F]', 1),
+ 26: ('[F]', 3),
+ 27: ('[Na]', 0),
+ 28: ('[Na]', 1),
+ 29: ('[Si]', 0),
+ 30: ('[Si]', 1),
+ 31: ('[P]', 0),
+ 32: ('[P]', 1),
+ 33: ('[P]', 3),
+ 34: ('[S]', 0),
+ 35: ('[S]', 1),
+ 36: ('[S]', 3),
+ 37: ('[S]', 7),
+ 38: ('[Cl]', 0),
+ 39: ('[Cl]', 1),
+ 40: ('[Cl]', 3),
+ 41: ('[Cl]', 7),
+ 42: ('[K]', 0),
+ 43: ('[K]', 1),
+ 44: ('[Br]', 0),
+ 45: ('[Br]', 1),
+ 46: ('[Br]', 3),
+ 47: ('[I]', 0),
+ 48: ('[I]', 1),
+ 49: ('[I]', 3),
+ 50: ('[Be]', 0),
+ 51: ('[Mg]', 0),
+ 52: ('[Al]', 0),
+ 53: ('[Ca]', 0),
+ 54: ('[Sc]', 0),
+ 55: ('[Ti]', 0),
+ 56: ('[V]', 0),
+ 57: ('[Cr]', 0),
+ 58: ('[Mn]', 0),
+ 59: ('[Fe]', 0),
+ 60: ('[CO]', 0),
+ 61: ('[Ni]', 0),
+ 62: ('[Cu]', 0),
+ 63: ('[Zn]', 0),
+ 64: ('[Ga]', 0),
+ 65: ('[Ge]', 0),
+ 66: ('[As]', 0),
+ 67: ('[Se]', 0),
+ 68: ('[Kr]', 0),
+ 69: ('[Rb]', 0),
+ 70: ('[Sr]', 0),
+ 71: ('[Y]', 0),
+ 72: ('[Zr]', 0),
+ 73: ('[Nb]', 0),
+ 74: ('[Mo]', 0),
+ 75: ('[Ru]', 0),
+ 76: ('[Rh]', 0),
+ 77: ('[Pd]', 0),
+ 78: ('[Ag]', 0),
+ 79: ('[Cd]', 0),
+ 80: ('[In]', 0),
+ 81: ('[Sn]', 0),
+ 82: ('[Sb]', 0),
+ 83: ('[Te]', 0),
+ 84: ('[Xe]', 0),
+ 85: ('[Cs]', 0),
+ 86: ('[Ba]', 0),
+ 87: ('[Lu]', 0),
+ 88: ('[Hf]', 0),
+ 89: ('[Ta]', 0),
+ 90: ('[W]', 0),
+ 91: ('[Re]', 0),
+ 92: ('[Os]', 0),
+ 93: ('[Ir]', 0),
+ 94: ('[Pt]', 0),
+ 95: ('[Au]', 0),
+ 96: ('[Hg]', 0),
+ 97: ('[Tl]', 0),
+ 98: ('[Pb]', 0),
+ 99: ('[Bi]', 0),
+ 100: ('[La]', 0),
+ 101: ('[Ce]', 0),
+ 102: ('[Pr]', 0),
+ 103: ('[Nd]', 0),
+ 104: ('[Pm]', 0),
+ 105: ('[Sm]', 0),
+ 106: ('[Eu]', 0),
+ 107: ('[Gd]', 0),
+ 108: ('[Tb]', 0),
+ 109: ('[Dy]', 0),
+ 110: ('[Ho]', 0),
+ 111: ('[Er]', 0),
+ 112: ('[Tm]', 0),
+ 113: ('[Yb]', 0),
+ 114: ('[Tc]', 0),
+ 115: ('[U]', 0),
+ 116: ('[Li&!H0]', 0), # 264-881
+ 117: ('[Li]~[Li]', 0),
+ 118: ('[Li]~[#5]', 0),
+ 119: ('[Li]~[#6]', 0),
+ 120: ('[Li]~[#8]', 0),
+ 121: ('[Li]~[F]', 0),
+ 122: ('[Li]~[#15]', 0),
+ 123: ('[Li]~[#16]', 0),
+ 124: ('[Li]~[Cl]', 0),
+ 125: ('[#5&!H0]', 0),
+ 126: ('[#5]~[#5]', 0),
+ 127: ('[#5]~[#6]', 0),
+ 128: ('[#5]~[#7]', 0),
+ 129: ('[#5]~[#8]', 0),
+ 130: ('[#5]~[F]', 0),
+ 131: ('[#5]~[#14]', 0),
+ 132: ('[#5]~[#15]', 0),
+ 133: ('[#5]~[#16]', 0),
+ 134: ('[#5]~[Cl]', 0),
+ 135: ('[#5]~[Br]', 0),
+ 136: ('[#6&!H0]', 0),
+ 137: ('[#6]~[#6]', 0),
+ 138: ('[#6]~[#7]', 0),
+ 139: ('[#6]~[#8]', 0),
+ 140: ('[#6]~[F]', 0),
+ 141: ('[#6]~[Na]', 0),
+ 142: ('[#6]~[Mg]', 0),
+ 143: ('[#6]~[Al]', 0),
+ 144: ('[#6]~[#14]', 0),
+ 145: ('[#6]~[#15]', 0),
+ 146: ('[#6]~[#16]', 0),
+ 147: ('[#6]~[Cl]', 0),
+ 148: ('[#6]~[#33]', 0),
+ 149: ('[#6]~[#34]', 0),
+ 150: ('[#6]~[Br]', 0),
+ 151: ('[#6]~[I]', 0),
+ 152: ('[#7&!H0]', 0),
+ 153: ('[#7]~[#7]', 0),
+ 154: ('[#7]~[#8]', 0),
+ 155: ('[#7]~[F]', 0),
+ 156: ('[#7]~[#14]', 0),
+ 157: ('[#7]~[#15]', 0),
+ 158: ('[#7]~[#16]', 0),
+ 159: ('[#7]~[Cl]', 0),
+ 160: ('[#7]~[Br]', 0),
+ 161: ('[#8&!H0]', 0),
+ 162: ('[#8]~[#8]', 0),
+ 163: ('[#8]~[Mg]', 0),
+ 164: ('[#8]~[Na]', 0),
+ 165: ('[#8]~[Al]', 0),
+ 166: ('[#8]~[#14]', 0),
+ 167: ('[#8]~[#15]', 0),
+ 168: ('[#8]~[K]', 0),
+ 169: ('[F]~[#15]', 0),
+ 170: ('[F]~[#16]', 0),
+ 171: ('[Al&!H0]', 0),
+ 172: ('[Al]~[Cl]', 0),
+ 173: ('[#14&!H0]', 0),
+ 174: ('[#14]~[#14]', 0),
+ 175: ('[#14]~[Cl]', 0),
+ 176: ('[#15&!H0]', 0),
+ 177: ('[#15]~[#15]', 0),
+ 178: ('[#33&!H0]', 0),
+ 179: ('[#33]~[#33]', 0),
+ 180: ('[#6](~Br)(~[#6])', 0),
+ 181: ('[#6](~Br)(~[#6])(~[#6])', 0),
+ 182: ('[#6&!H0]~[Br]', 0),
+ 183: ('[#6](~[Br])(:[c])', 0),
+ 184: ('[#6](~[Br])(:[n])', 0),
+ 185: ('[#6](~[#6])(~[#6])', 0),
+ 186: ('[#6](~[#6])(~[#6])(~[#6])', 0),
+ 187: ('[#6](~[#6])(~[#6])(~[#6])(~[#6])', 0),
+ 188: ('[#6H1](~[#6])(~[#6])(~[#6])', 0),
+ 189: ('[#6](~[#6])(~[#6])(~[#6])(~[#7])', 0),
+ 190: ('[#6](~[#6])(~[#6])(~[#6])(~[#8])', 0),
+ 191: ('[#6H1](~[#6])(~[#6])(~[#7])', 0),
+ 192: ('[#6H1](~[#6])(~[#6])(~[#8])', 0),
+ 193: ('[#6](~[#6])(~[#6])(~[#7])', 0),
+ 194: ('[#6](~[#6])(~[#6])(~[#8])', 0),
+ 195: ('[#6](~[#6])(~[Cl])', 0),
+ 196: ('[#6&!H0](~[#6])(~[Cl])', 0),
+ 197: ('[#6H,#6H2,#6H3,#6H4]~[#6]', 0),
+ 198: ('[#6&!H0](~[#6])(~[#7])', 0),
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+ 504: ('[#8]-,:[#6]:[#6]:[#6]-,:[#6]', 0),
+ 505: ('[#8]-,:[#6]:[#6]:[#6]-,:[#7]', 0),
+ 506: ('[#8]-,:[#6]:[#6]:[#6]-,:[#8]', 0),
+ 507: ('[#7]-,:[#6]-,:[#7]-,:[#6]-,:[#6]', 0),
+ 508: ('[#8]-,:[#6]-,:[#6]-,:[#6]:c', 0),
+ 509: ('[#6]-,:[#6]-,:[#7]-,:[#6]-,:[#6]', 0),
+ 510: ('[#6]-,:[#7]-,:[#6]:[#6]-,:[#6]', 0),
+ 511: ('[#6]-,:[#6]-,:[#16]-,:[#6]-,:[#6]', 0),
+ 512: ('[#8]-,:[#6]-,:[#6]-,:[#7]-,:[#6]', 0),
+ 513: ('[#6]-,:[#6]=,:[#6]-,:[#6]-,:[#6]', 0),
+ 514: ('[#8]-,:[#6]-,:[#8]-,:[#6]-,:[#6]', 0),
+ 515: ('[#8]-,:[#6]-,:[#6]-,:[#8]-,:[#6]', 0),
+ 516: ('[#8]-,:[#6]-,:[#6]-,:[#8&!H0]', 0),
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+ 519: ('[#6]=,:[#6]-,:[#6]-,:[#8]-,:[#6]', 0),
+ 520: ('[#6]=,:[#6]-,:[#6]-,:[#8&!H0]', 0),
+ 521: ('[#6]-,:[#6]:[#6]-,:[#6]-,:[#6]', 0),
+ 522: ('[Cl]-,:[#6]:[#6]-,:[#6]=,:[#8]', 0),
+ 523: ('[Br]-,:[#6]:c:c-,:[#6]', 0),
+ 524: ('[#8]=,:[#6]-,:[#6]=,:[#6]-,:[#6]', 0),
+ 525: ('[#8]=,:[#6]-,:[#6]=,:[#6&!H0]', 0),
+ 526: ('[#8]=,:[#6]-,:[#6]=,:[#6]-,:[#7]', 0),
+ 527: ('[#7]-,:[#6]-,:[#7]-,:[#6]:c', 0),
+ 528: ('[Br]-,:[#6]-,:[#6]-,:[#6]:c', 0),
+ 529: ('[#7]#[#6]-,:[#6]-,:[#6]-,:[#6]', 0),
+ 530: ('[#6]-,:[#6]=,:[#6]-,:[#6]:c', 0),
+ 531: ('[#6]-,:[#6]-,:[#6]=,:[#6]-,:[#6]', 0),
+ 532: ('[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]', 0),
+ 533: ('[#8]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]', 0),
+ 534: ('[#8]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#8]', 0),
+ 535: ('[#8]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#7]', 0),
+ 536: ('[#7]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]', 0),
+ 537: ('[#8]=,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]', 0),
+ 538: ('[#8]=,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#7]', 0),
+ 539: ('[#8]=,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#8]', 0),
+ 540: ('[#8]=,:[#6]-,:[#6]-,:[#6]-,:[#6]=,:[#8]', 0),
+ 541: ('[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]', 0),
+ 542: ('[#8]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]', 0),
+ 543: ('[#8]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#8]', 0),
+ 544: ('[#8]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#7]', 0),
+ 545: ('[#8]=,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]', 0),
+ 546: ('[#8]=,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#8]', 0),
+ 547: ('[#8]=,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]=,:[#8]', 0),
+ 548: ('[#8]=,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#7]', 0),
+ 549: ('[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]', 0),
+ 550: ('[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6](-,:[#6])-,:[#6]', 0),
+ 551: ('[#8]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]', 0),
+ 552: ('[#8]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6](-,:[#6])-,:[#6]', 0),
+ 553: ('[#8]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#8]-,:[#6]', 0),
+ 554: ('[#8]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6](-,:[#8])-,:[#6]', 0),
+ 555: ('[#8]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#7]-,:[#6]', 0),
+ 556: ('[#8]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6](-,:[#7])-,:[#6]', 0),
+ 557: ('[#8]=,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6]', 0),
+ 558: ('[#8]=,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6](-,:[#8])-,:[#6]', 0),
+ 559: ('[#8]=,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6](=,:[#8])-,:[#6]', 0),
+ 560: ('[#8]=,:[#6]-,:[#6]-,:[#6]-,:[#6]-,:[#6](-,:[#7])-,:[#6]', 0),
+ 561: ('[#6]-,:[#6](-,:[#6])-,:[#6]-,:[#6]', 0),
+ 562: ('[#6]-,:[#6](-,:[#6])-,:[#6]-,:[#6]-,:[#6]', 0),
+ 563: ('[#6]-,:[#6]-,:[#6](-,:[#6])-,:[#6]-,:[#6]', 0),
+ 564: ('[#6]-,:[#6](-,:[#6])(-,:[#6])-,:[#6]-,:[#6]', 0),
+ 565: ('[#6]-,:[#6](-,:[#6])-,:[#6](-,:[#6])-,:[#6]', 0),
+ 566: ('[#6]c1ccc([#6])cc1', 0),
+ 567: ('[#6]c1ccc([#8])cc1', 0),
+ 568: ('[#6]c1ccc([#16])cc1', 0),
+ 569: ('[#6]c1ccc([#7])cc1', 0),
+ 570: ('[#6]c1ccc(Cl)cc1', 0),
+ 571: ('[#6]c1ccc(Br)cc1', 0),
+ 572: ('[#8]c1ccc([#8])cc1', 0),
+ 573: ('[#8]c1ccc([#16])cc1', 0),
+ 574: ('[#8]c1ccc([#7])cc1', 0),
+ 575: ('[#8]c1ccc(Cl)cc1', 0),
+ 576: ('[#8]c1ccc(Br)cc1', 0),
+ 577: ('[#16]c1ccc([#16])cc1', 0),
+ 578: ('[#16]c1ccc([#7])cc1', 0),
+ 579: ('[#16]c1ccc(Cl)cc1', 0),
+ 580: ('[#16]c1ccc(Br)cc1', 0),
+ 581: ('[#7]c1ccc([#7])cc1', 0),
+ 582: ('[#7]c1ccc(Cl)cc1', 0),
+ 583: ('[#7]c1ccc(Br)cc1', 0),
+ 584: ('Clc1ccc(Cl)cc1', 0),
+ 585: ('Clc1ccc(Br)cc1', 0),
+ 586: ('Brc1ccc(Br)cc1', 0),
+ 587: ('[#6]c1cc([#6])ccc1', 0),
+ 588: ('[#6]c1cc([#8])ccc1', 0),
+ 589: ('[#6]c1cc([#16])ccc1', 0),
+ 590: ('[#6]c1cc([#7])ccc1', 0),
+ 591: ('[#6]c1cc(Cl)ccc1', 0),
+ 592: ('[#6]c1cc(Br)ccc1', 0),
+ 593: ('[#8]c1cc([#8])ccc1', 0),
+ 594: ('[#8]c1cc([#16])ccc1', 0),
+ 595: ('[#8]c1cc([#7])ccc1', 0),
+ 596: ('[#8]c1cc(Cl)ccc1', 0),
+ 597: ('[#8]c1cc(Br)ccc1', 0),
+ 598: ('[#16]c1cc([#16])ccc1', 0),
+ 599: ('[#16]c1cc([#7])ccc1', 0),
+ 600: ('[#16]c1cc(Cl)ccc1', 0),
+ 601: ('[#16]c1cc(Br)ccc1', 0),
+ 602: ('[#7]c1cc([#7])ccc1', 0),
+ 603: ('[#7]c1cc(Cl)ccc1', 0),
+ 604: ('[#7]c1cc(Br)ccc1', 0),
+ 605: ('Clc1cc(Cl)ccc1', 0),
+ 606: ('Clc1cc(Br)ccc1', 0),
+ 607: ('Brc1cc(Br)ccc1', 0),
+ 608: ('[#6]c1c([#6])cccc1', 0),
+ 609: ('[#6]c1c([#8])cccc1', 0),
+ 610: ('[#6]c1c([#16])cccc1', 0),
+ 611: ('[#6]c1c([#7])cccc1', 0),
+ 612: ('[#6]c1c(Cl)cccc1', 0),
+ 613: ('[#6]c1c(Br)cccc1', 0),
+ 614: ('[#8]c1c([#8])cccc1', 0),
+ 615: ('[#8]c1c([#16])cccc1', 0),
+ 616: ('[#8]c1c([#7])cccc1', 0),
+ 617: ('[#8]c1c(Cl)cccc1', 0),
+ 618: ('[#8]c1c(Br)cccc1', 0),
+ 619: ('[#16]c1c([#16])cccc1', 0),
+ 620: ('[#16]c1c([#7])cccc1', 0),
+ 621: ('[#16]c1c(Cl)cccc1', 0),
+ 622: ('[#16]c1c(Br)cccc1', 0),
+ 623: ('[#7]c1c([#7])cccc1', 0),
+ 624: ('[#7]c1c(Cl)cccc1', 0),
+ 625: ('[#7]c1c(Br)cccc1', 0),
+ 626: ('Clc1c(Cl)cccc1', 0),
+ 627: ('Clc1c(Br)cccc1', 0),
+ 628: ('Brc1c(Br)cccc1', 0),
+ 629: ('[#6][#6]1[#6][#6][#6]([#6])[#6][#6]1', 0),
+ 630: ('[#6][#6]1[#6][#6][#6]([#8])[#6][#6]1', 0),
+ 631: ('[#6][#6]1[#6][#6][#6]([#16])[#6][#6]1', 0),
+ 632: ('[#6][#6]1[#6][#6][#6]([#7])[#6][#6]1', 0),
+ 633: ('[#6][#6]1[#6][#6][#6](Cl)[#6][#6]1', 0),
+ 634: ('[#6][#6]1[#6][#6][#6](Br)[#6][#6]1', 0),
+ 635: ('[#8][#6]1[#6][#6][#6]([#8])[#6][#6]1', 0),
+ 636: ('[#8][#6]1[#6][#6][#6]([#16])[#6][#6]1', 0),
+ 637: ('[#8][#6]1[#6][#6][#6]([#7])[#6][#6]1', 0),
+ 638: ('[#8][#6]1[#6][#6][#6](Cl)[#6][#6]1', 0),
+ 639: ('[#8][#6]1[#6][#6][#6](Br)[#6][#6]1', 0),
+ 640: ('[#16][#6]1[#6][#6][#6]([#16])[#6][#6]1', 0),
+ 641: ('[#16][#6]1[#6][#6][#6]([#7])[#6][#6]1', 0),
+ 642: ('[#16][#6]1[#6][#6][#6](Cl)[#6][#6]1', 0),
+ 643: ('[#16][#6]1[#6][#6][#6](Br)[#6][#6]1', 0),
+ 644: ('[#7][#6]1[#6][#6][#6]([#7])[#6][#6]1', 0),
+ 645: ('[#7][#6]1[#6][#6][#6](Cl)[#6][#6]1', 0),
+ 646: ('[#7][#6]1[#6][#6][#6](Br)[#6][#6]1', 0),
+ 647: ('Cl[#6]1[#6][#6][#6](Cl)[#6][#6]1', 0),
+ 648: ('Cl[#6]1[#6][#6][#6](Br)[#6][#6]1', 0),
+ 649: ('Br[#6]1[#6][#6][#6](Br)[#6][#6]1', 0),
+ 650: ('[#6][#6]1[#6][#6]([#6])[#6][#6][#6]1', 0),
+ 651: ('[#6][#6]1[#6][#6]([#8])[#6][#6][#6]1', 0),
+ 652: ('[#6][#6]1[#6][#6]([#16])[#6][#6][#6]1', 0),
+ 653: ('[#6][#6]1[#6][#6]([#7])[#6][#6][#6]1', 0),
+ 654: ('[#6][#6]1[#6][#6](Cl)[#6][#6][#6]1', 0),
+ 655: ('[#6][#6]1[#6][#6](Br)[#6][#6][#6]1', 0),
+ 656: ('[#8][#6]1[#6][#6]([#8])[#6][#6][#6]1', 0),
+ 657: ('[#8][#6]1[#6][#6]([#16])[#6][#6][#6]1', 0),
+ 658: ('[#8][#6]1[#6][#6]([#7])[#6][#6][#6]1', 0),
+ 659: ('[#8][#6]1[#6][#6](Cl)[#6][#6][#6]1', 0),
+ 660: ('[#8][#6]1[#6][#6](Br)[#6][#6][#6]1', 0),
+ 661: ('[#16][#6]1[#6][#6]([#16])[#6][#6][#6]1', 0),
+ 662: ('[#16][#6]1[#6][#6]([#7])[#6][#6][#6]1', 0),
+ 663: ('[#16][#6]1[#6][#6](Cl)[#6][#6][#6]1', 0),
+ 664: ('[#16][#6]1[#6][#6](Br)[#6][#6][#6]1', 0),
+ 665: ('[#7][#6]1[#6][#6]([#7])[#6][#6][#6]1', 0),
+ 666: ('[#7][#6]1[#6][#6](Cl)[#6][#6][#6]1', 0),
+ 667: ('[#7][#6]1[#6][#6](Br)[#6][#6][#6]1', 0),
+ 668: ('Cl[#6]1[#6][#6](Cl)[#6][#6][#6]1', 0),
+ 669: ('Cl[#6]1[#6][#6](Br)[#6][#6][#6]1', 0),
+ 670: ('Br[#6]1[#6][#6](Br)[#6][#6][#6]1', 0),
+ 671: ('[#6][#6]1[#6]([#6])[#6][#6][#6][#6]1', 0),
+ 672: ('[#6][#6]1[#6]([#8])[#6][#6][#6][#6]1', 0),
+ 673: ('[#6][#6]1[#6]([#16])[#6][#6][#6][#6]1', 0),
+ 674: ('[#6][#6]1[#6]([#7])[#6][#6][#6][#6]1', 0),
+ 675: ('[#6][#6]1[#6](Cl)[#6][#6][#6][#6]1', 0),
+ 676: ('[#6][#6]1[#6](Br)[#6][#6][#6][#6]1', 0),
+ 677: ('[#8][#6]1[#6]([#8])[#6][#6][#6][#6]1', 0),
+ 678: ('[#8][#6]1[#6]([#16])[#6][#6][#6][#6]1', 0),
+ 679: ('[#8][#6]1[#6]([#7])[#6][#6][#6][#6]1', 0),
+ 680: ('[#8][#6]1[#6](Cl)[#6][#6][#6][#6]1', 0),
+ 681: ('[#8][#6]1[#6](Br)[#6][#6][#6][#6]1', 0),
+ 682: ('[#16][#6]1[#6]([#16])[#6][#6][#6][#6]1', 0),
+ 683: ('[#16][#6]1[#6]([#7])[#6][#6][#6][#6]1', 0),
+ 684: ('[#16][#6]1[#6](Cl)[#6][#6][#6][#6]1', 0),
+ 685: ('[#16][#6]1[#6](Br)[#6][#6][#6][#6]1', 0),
+ 686: ('[#7][#6]1[#6]([#7])[#6][#6][#6][#6]1', 0),
+ 687: ('[#7][#6]1[#6](Cl)[#6][#6][#6][#6]1', 0),
+ 688: ('[#7][#6]1[#6](Br)[#6][#6][#6][#6]1', 0),
+ 689: ('Cl[#6]1[#6](Cl)[#6][#6][#6][#6]1', 0),
+ 690: ('Cl[#6]1[#6](Br)[#6][#6][#6][#6]1', 0),
+ 691: ('Br[#6]1[#6](Br)[#6][#6][#6][#6]1', 0),
+ 692: ('[#6][#6]1[#6][#6]([#6])[#6][#6]1', 0),
+ 693: ('[#6][#6]1[#6][#6]([#8])[#6][#6]1', 0),
+ 694: ('[#6][#6]1[#6][#6]([#16])[#6][#6]1', 0),
+ 695: ('[#6][#6]1[#6][#6]([#7])[#6][#6]1', 0),
+ 696: ('[#6][#6]1[#6][#6](Cl)[#6][#6]1', 0),
+ 697: ('[#6][#6]1[#6][#6](Br)[#6][#6]1', 0),
+ 698: ('[#8][#6]1[#6][#6]([#8])[#6][#6]1', 0),
+ 699: ('[#8][#6]1[#6][#6]([#16])[#6][#6]1', 0),
+ 700: ('[#8][#6]1[#6][#6]([#7])[#6][#6]1', 0),
+ 701: ('[#8][#6]1[#6][#6](Cl)[#6][#6]1', 0),
+ 702: ('[#8][#6]1[#6][#6](Br)[#6][#6]1', 0),
+ 703: ('[#16][#6]1[#6][#6]([#16])[#6][#6]1', 0),
+ 704: ('[#16][#6]1[#6][#6]([#7])[#6][#6]1', 0),
+ 705: ('[#16][#6]1[#6][#6](Cl)[#6][#6]1', 0),
+ 706: ('[#16][#6]1[#6][#6](Br)[#6][#6]1', 0),
+ 707: ('[#7][#6]1[#6][#6]([#7])[#6][#6]1', 0),
+ 708: ('[#7][#6]1[#6][#6](Cl)[#6][#6]1', 0),
+ 709: ('[#7][#6]1[#6][#6](Br)[#6][#6]1', 0),
+ 710: ('Cl[#6]1[#6][#6](Cl)[#6][#6]1', 0),
+ 711: ('Cl[#6]1[#6][#6](Br)[#6][#6]1', 0),
+ 712: ('Br[#6]1[#6][#6](Br)[#6][#6]1', 0),
+ 713: ('[#6][#6]1[#6]([#6])[#6][#6][#6]1', 0),
+ 714: ('[#6][#6]1[#6]([#8])[#6][#6][#6]1', 0),
+ 715: ('[#6][#6]1[#6]([#16])[#6][#6][#6]1', 0),
+ 716: ('[#6][#6]1[#6]([#7])[#6][#6][#6]1', 0),
+ 717: ('[#6][#6]1[#6](Cl)[#6][#6][#6]1', 0),
+ 718: ('[#6][#6]1[#6](Br)[#6][#6][#6]1', 0),
+ 719: ('[#8][#6]1[#6]([#8])[#6][#6][#6]1', 0),
+ 720: ('[#8][#6]1[#6]([#16])[#6][#6][#6]1', 0),
+ 721: ('[#8][#6]1[#6]([#7])[#6][#6][#6]1', 0),
+ 722: ('[#8][#6]1[#6](Cl)[#6][#6][#6]1', 0),
+ 723: ('[#8][#6]1[#6](Br)[#6][#6][#6]1', 0),
+ 724: ('[#16][#6]1[#6]([#16])[#6][#6][#6]1', 0),
+ 725: ('[#16][#6]1[#6]([#7])[#6][#6][#6]1', 0),
+ 726: ('[#16][#6]1[#6](Cl)[#6][#6][#6]1', 0),
+ 727: ('[#16][#6]1[#6](Br)[#6][#6][#6]1', 0),
+ 728: ('[#7][#6]1[#6]([#7])[#6][#6][#6]1', 0),
+ 729: ('[#7][#6]1[#6](Cl)[#6][#6]1', 0),
+ 730: ('[#7][#6]1[#6](Br)[#6][#6][#6]1', 0),
+ 731: ('Cl[#6]1[#6](Cl)[#6][#6][#6]1', 0),
+ 732: ('Cl[#6]1[#6](Br)[#6][#6][#6]1', 0),
+ 733: ('Br[#6]1[#6](Br)[#6][#6][#6]1', 0)}
+
+PubchemKeys = None
+
+
+def InitKeys(keyList, keyDict):
+ """ *Internal Use Only*
+ generates SMARTS patterns for the keys, run once
+ """
+ assert len(keyList) == len(keyDict.keys()), 'length mismatch'
+ for key in keyDict.keys():
+ patt, count = keyDict[key]
+ if patt != '?':
+ sma = Chem.MolFromSmarts(patt)
+ if not sma:
+ print('SMARTS parser error for key #%d: %s' % (key, patt))
+ else:
+ keyList[key - 1] = sma, count
+
+
+def calcPubChemFingerPart1(mol, **kwargs):
+ """ Calculate PubChem Fingerprints （1-115; 263-881)
+ **Arguments**
+ - mol: the molecule to be fingerprinted
+ - any extra keyword arguments are ignored
+ **Returns**
+ a _DataStructs.SparseBitVect_ containing the fingerprint.
+ >>> m = Chem.MolFromSmiles('CNO')
+ >>> bv = PubChemFingerPart1(m)
+ >>> tuple(bv.GetOnBits())
+ (24, 68, 69, 71, 93, 94, 102, 124, 131, 139, 151, 158, 160, 161, 164)
+ >>> bv = PubChemFingerPart1(Chem.MolFromSmiles('CCC'))
+ >>> tuple(bv.GetOnBits())
+ (74, 114, 149, 155, 160)
+ """
+ global PubchemKeys
+ if PubchemKeys is None:
+ PubchemKeys = [(None, 0)] * len(smartsPatts.keys())
+ InitKeys(PubchemKeys, smartsPatts)
+ ctor = kwargs.get('ctor', DataStructs.SparseBitVect)
+ res = ctor(len(PubchemKeys) + 1)
+ for i, (patt, count) in enumerate(PubchemKeys):
+ if patt is not None:
+ if count == 0:
+ res[i + 1] = mol.HasSubstructMatch(patt)
+ else:
+ matches = mol.GetSubstructMatches(patt)
+ if len(matches) > count:
+ res[i + 1] = 1
+ return res
+
+
+def func_1(mol, bits):
+ """ *Internal Use Only*
+ Calculate PubChem Fingerprints （116-263)
+ """
+ ringSize = []
+ temp = {3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0}
+ AllRingsAtom = mol.GetRingInfo().AtomRings()
+ for ring in AllRingsAtom:
+ ringSize.append(len(ring))
+ for k, v in temp.items():
+ if len(ring) == k:
+ temp[k] += 1
+ if temp[3] >= 2:
+ bits[0] = 1
+ bits[7] = 1
+ elif temp[3] == 1:
+ bits[0] = 1
+ else:
+ pass
+ if temp[4] >= 2:
+ bits[14] = 1
+ bits[21] = 1
+ elif temp[4] == 1:
+ bits[14] = 1
+ else:
+ pass
+ if temp[5] >= 5:
+ bits[28] = 1
+ bits[35] = 1
+ bits[42] = 1
+ bits[49] = 1
+ bits[56] = 1
+ elif temp[5] == 4:
+ bits[28] = 1
+ bits[35] = 1
+ bits[42] = 1
+ bits[49] = 1
+ elif temp[5] == 3:
+ bits[28] = 1
+ bits[35] = 1
+ bits[42] = 1
+ elif temp[5] == 2:
+ bits[28] = 1
+ bits[35] = 1
+ elif temp[5] == 1:
+ bits[28] = 1
+ else:
+ pass
+ if temp[6] >= 5:
+ bits[63] = 1
+ bits[70] = 1
+ bits[77] = 1
+ bits[84] = 1
+ bits[91] = 1
+ elif temp[6] == 4:
+ bits[63] = 1
+ bits[70] = 1
+ bits[77] = 1
+ bits[84] = 1
+ elif temp[6] == 3:
+ bits[63] = 1
+ bits[70] = 1
+ bits[77] = 1
+ elif temp[6] == 2:
+ bits[63] = 1
+ bits[70] = 1
+ elif temp[6] == 1:
+ bits[63] = 1
+ else:
+ pass
+ if temp[7] >= 2:
+ bits[98] = 1
+ bits[105] = 1
+ elif temp[7] == 1:
+ bits[98] = 1
+ else:
+ pass
+ if temp[8] >= 2:
+ bits[112] = 1
+ bits[119] = 1
+ elif temp[8] == 1:
+ bits[112] = 1
+ else:
+ pass
+ if temp[9] >= 1:
+ bits[126] = 1
+ else:
+ pass
+ if temp[10] >= 1:
+ bits[133] = 1
+ else:
+ pass
+
+ return ringSize, bits
+
+
+def func_2(mol, bits):
+ """ *Internal Use Only*
+ saturated or aromatic carbon-only ring
+ """
+ AllRingsBond = mol.GetRingInfo().BondRings()
+ ringSize = []
+ temp = {3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0}
+ for ring in AllRingsBond:
+ ######### saturated
+ nonsingle = False
+ for bondIdx in ring:
+ if mol.GetBondWithIdx(bondIdx).GetBondType().name != 'SINGLE':
+ nonsingle = True
+ break
+ if nonsingle == False:
+ ringSize.append(len(ring))
+ for k, v in temp.items():
+ if len(ring) == k:
+ temp[k] += 1
+ ######## aromatic carbon-only
+ aromatic = True
+ AllCarb = True
+ for bondIdx in ring:
+ if mol.GetBondWithIdx(bondIdx).GetBondType().name != 'AROMATIC':
+ aromatic = False
+ break
+ for bondIdx in ring:
+ BeginAtom = mol.GetBondWithIdx(bondIdx).GetBeginAtom()
+ EndAtom = mol.GetBondWithIdx(bondIdx).GetEndAtom()
+ if BeginAtom.GetAtomicNum() != 6 or EndAtom.GetAtomicNum() != 6:
+ AllCarb = False
+ break
+ if aromatic == True and AllCarb == True:
+ ringSize.append(len(ring))
+ for k, v in temp.items():
+ if len(ring) == k:
+ temp[k] += 1
+ if temp[3] >= 2:
+ bits[1] = 1
+ bits[8] = 1
+ elif temp[3] == 1:
+ bits[1] = 1
+ else:
+ pass
+ if temp[4] >= 2:
+ bits[15] = 1
+ bits[22] = 1
+ elif temp[4] == 1:
+ bits[15] = 1
+ else:
+ pass
+ if temp[5] >= 5:
+ bits[29] = 1
+ bits[36] = 1
+ bits[43] = 1
+ bits[50] = 1
+ bits[57] = 1
+ elif temp[5] == 4:
+ bits[29] = 1
+ bits[36] = 1
+ bits[43] = 1
+ bits[50] = 1
+ elif temp[5] == 3:
+ bits[29] = 1
+ bits[36] = 1
+ bits[43] = 1
+ elif temp[5] == 2:
+ bits[29] = 1
+ bits[36] = 1
+ elif temp[5] == 1:
+ bits[29] = 1
+ else:
+ pass
+ if temp[6] >= 5:
+ bits[64] = 1
+ bits[71] = 1
+ bits[78] = 1
+ bits[85] = 1
+ bits[92] = 1
+ elif temp[6] == 4:
+ bits[64] = 1
+ bits[71] = 1
+ bits[78] = 1
+ bits[85] = 1
+ elif temp[6] == 3:
+ bits[64] = 1
+ bits[71] = 1
+ bits[78] = 1
+ elif temp[6] == 2:
+ bits[64] = 1
+ bits[71] = 1
+ elif temp[6] == 1:
+ bits[64] = 1
+ else:
+ pass
+ if temp[7] >= 2:
+ bits[99] = 1
+ bits[106] = 1
+ elif temp[7] == 1:
+ bits[99] = 1
+ else:
+ pass
+ if temp[8] >= 2:
+ bits[113] = 1
+ bits[120] = 1
+ elif temp[8] == 1:
+ bits[113] = 1
+ else:
+ pass
+ if temp[9] >= 1:
+ bits[127] = 1
+ else:
+ pass
+ if temp[10] >= 1:
+ bits[134] = 1
+ else:
+ pass
+ return ringSize, bits
+
+
+def func_3(mol, bits):
+ """ *Internal Use Only*
+ saturated or aromatic nitrogen-containing
+ """
+ AllRingsBond = mol.GetRingInfo().BondRings()
+ ringSize = []
+ temp = {3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0}
+ for ring in AllRingsBond:
+ ######### saturated
+ nonsingle = False
+ for bondIdx in ring:
+ if mol.GetBondWithIdx(bondIdx).GetBondType().name != 'SINGLE':
+ nonsingle = True
+ break
+ if nonsingle == False:
+ ringSize.append(len(ring))
+ for k, v in temp.items():
+ if len(ring) == k:
+ temp[k] += 1
+ ######## aromatic nitrogen-containing
+ aromatic = True
+ ContainNitro = False
+ for bondIdx in ring:
+ if mol.GetBondWithIdx(bondIdx).GetBondType().name != 'AROMATIC':
+ aromatic = False
+ break
+ for bondIdx in ring:
+ BeginAtom = mol.GetBondWithIdx(bondIdx).GetBeginAtom()
+ EndAtom = mol.GetBondWithIdx(bondIdx).GetEndAtom()
+ if BeginAtom.GetAtomicNum() == 7 or EndAtom.GetAtomicNum() == 7:
+ ContainNitro = True
+ break
+ if aromatic == True and ContainNitro == True:
+ ringSize.append(len(ring))
+ for k, v in temp.items():
+ if len(ring) == k:
+ temp[k] += 1
+ if temp[3] >= 2:
+ bits[2] = 1
+ bits[9] = 1
+ elif temp[3] == 1:
+ bits[2] = 1
+ else:
+ pass
+ if temp[4] >= 2:
+ bits[16] = 1
+ bits[23] = 1
+ elif temp[4] == 1:
+ bits[16] = 1
+ else:
+ pass
+ if temp[5] >= 5:
+ bits[30] = 1
+ bits[37] = 1
+ bits[44] = 1
+ bits[51] = 1
+ bits[58] = 1
+ elif temp[5] == 4:
+ bits[30] = 1
+ bits[37] = 1
+ bits[44] = 1
+ bits[51] = 1
+ elif temp[5] == 3:
+ bits[30] = 1
+ bits[37] = 1
+ bits[44] = 1
+ elif temp[5] == 2:
+ bits[30] = 1
+ bits[37] = 1
+ elif temp[5] == 1:
+ bits[30] = 1
+ else:
+ pass
+ if temp[6] >= 5:
+ bits[65] = 1
+ bits[72] = 1
+ bits[79] = 1
+ bits[86] = 1
+ bits[93] = 1
+ elif temp[6] == 4:
+ bits[65] = 1
+ bits[72] = 1
+ bits[79] = 1
+ bits[86] = 1
+ elif temp[6] == 3:
+ bits[65] = 1
+ bits[72] = 1
+ bits[79] = 1
+ elif temp[6] == 2:
+ bits[65] = 1
+ bits[72] = 1
+ elif temp[6] == 1:
+ bits[65] = 1
+ else:
+ pass
+ if temp[7] >= 2:
+ bits[100] = 1
+ bits[107] = 1
+ elif temp[7] == 1:
+ bits[100] = 1
+ else:
+ pass
+ if temp[8] >= 2:
+ bits[114] = 1
+ bits[121] = 1
+ elif temp[8] == 1:
+ bits[114] = 1
+ else:
+ pass
+ if temp[9] >= 1:
+ bits[128] = 1
+ else:
+ pass
+ if temp[10] >= 1:
+ bits[135] = 1
+ else:
+ pass
+ return ringSize, bits
+
+
+def func_4(mol, bits):
+ """ *Internal Use Only*
+ saturated or aromatic heteroatom-containing
+ """
+ AllRingsBond = mol.GetRingInfo().BondRings()
+ ringSize = []
+ temp = {3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0}
+ for ring in AllRingsBond:
+ ######### saturated
+ nonsingle = False
+ for bondIdx in ring:
+ if mol.GetBondWithIdx(bondIdx).GetBondType().name != 'SINGLE':
+ nonsingle = True
+ break
+ if nonsingle == False:
+ ringSize.append(len(ring))
+ for k, v in temp.items():
+ if len(ring) == k:
+ temp[k] += 1
+ ######## aromatic heteroatom-containing
+ aromatic = True
+ heteroatom = False
+ for bondIdx in ring:
+ if mol.GetBondWithIdx(bondIdx).GetBondType().name != 'AROMATIC':
+ aromatic = False
+ break
+ for bondIdx in ring:
+ BeginAtom = mol.GetBondWithIdx(bondIdx).GetBeginAtom()
+ EndAtom = mol.GetBondWithIdx(bondIdx).GetEndAtom()
+ if BeginAtom.GetAtomicNum() not in [1, 6] or EndAtom.GetAtomicNum() not in [1, 6]:
+ heteroatom = True
+ break
+ if aromatic == True and heteroatom == True:
+ ringSize.append(len(ring))
+ for k, v in temp.items():
+ if len(ring) == k:
+ temp[k] += 1
+ if temp[3] >= 2:
+ bits[3] = 1
+ bits[10] = 1
+ elif temp[3] == 1:
+ bits[3] = 1
+ else:
+ pass
+ if temp[4] >= 2:
+ bits[17] = 1
+ bits[24] = 1
+ elif temp[4] == 1:
+ bits[17] = 1
+ else:
+ pass
+ if temp[5] >= 5:
+ bits[31] = 1
+ bits[38] = 1
+ bits[45] = 1
+ bits[52] = 1
+ bits[59] = 1
+ elif temp[5] == 4:
+ bits[31] = 1
+ bits[38] = 1
+ bits[45] = 1
+ bits[52] = 1
+ elif temp[5] == 3:
+ bits[31] = 1
+ bits[38] = 1
+ bits[45] = 1
+ elif temp[5] == 2:
+ bits[31] = 1
+ bits[38] = 1
+ elif temp[5] == 1:
+ bits[31] = 1
+ else:
+ pass
+ if temp[6] >= 5:
+ bits[66] = 1
+ bits[73] = 1
+ bits[80] = 1
+ bits[87] = 1
+ bits[94] = 1
+ elif temp[6] == 4:
+ bits[66] = 1
+ bits[73] = 1
+ bits[80] = 1
+ bits[87] = 1
+ elif temp[6] == 3:
+ bits[66] = 1
+ bits[73] = 1
+ bits[80] = 1
+ elif temp[6] == 2:
+ bits[66] = 1
+ bits[73] = 1
+ elif temp[6] == 1:
+ bits[66] = 1
+ else:
+ pass
+ if temp[7] >= 2:
+ bits[101] = 1
+ bits[108] = 1
+ elif temp[7] == 1:
+ bits[101] = 1
+ else:
+ pass
+ if temp[8] >= 2:
+ bits[115] = 1
+ bits[122] = 1
+ elif temp[8] == 1:
+ bits[115] = 1
+ else:
+ pass
+ if temp[9] >= 1:
+ bits[129] = 1
+ else:
+ pass
+ if temp[10] >= 1:
+ bits[136] = 1
+ else:
+ pass
+ return ringSize, bits
+
+
+def func_5(mol, bits):
+ """ *Internal Use Only*
+ unsaturated non-aromatic carbon-only
+ """
+ ringSize = []
+ AllRingsBond = mol.GetRingInfo().BondRings()
+ temp = {3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0}
+ for ring in AllRingsBond:
+ unsaturated = False
+ nonaromatic = True
+ Allcarb = True
+ ######### unsaturated
+ for bondIdx in ring:
+ if mol.GetBondWithIdx(bondIdx).GetBondType().name != 'SINGLE':
+ unsaturated = True
+ break
+ ######## non-aromatic
+ for bondIdx in ring:
+ if mol.GetBondWithIdx(bondIdx).GetBondType().name == 'AROMATIC':
+ nonaromatic = False
+ break
+ ######## allcarb
+ for bondIdx in ring:
+ BeginAtom = mol.GetBondWithIdx(bondIdx).GetBeginAtom()
+ EndAtom = mol.GetBondWithIdx(bondIdx).GetEndAtom()
+ if BeginAtom.GetAtomicNum() != 6 or EndAtom.GetAtomicNum() != 6:
+ Allcarb = False
+ break
+ if unsaturated == True and nonaromatic == True and Allcarb == True:
+ ringSize.append(len(ring))
+ for k, v in temp.items():
+ if len(ring) == k:
+ temp[k] += 1
+ if temp[3] >= 2:
+ bits[4] = 1
+ bits[11] = 1
+ elif temp[3] == 1:
+ bits[4] = 1
+ else:
+ pass
+ if temp[4] >= 2:
+ bits[18] = 1
+ bits[25] = 1
+ elif temp[4] == 1:
+ bits[18] = 1
+ else:
+ pass
+ if temp[5] >= 5:
+ bits[32] = 1
+ bits[39] = 1
+ bits[46] = 1
+ bits[53] = 1
+ bits[60] = 1
+ elif temp[5] == 4:
+ bits[32] = 1
+ bits[39] = 1
+ bits[46] = 1
+ bits[53] = 1
+ elif temp[5] == 3:
+ bits[32] = 1
+ bits[39] = 1
+ bits[46] = 1
+ elif temp[5] == 2:
+ bits[32] = 1
+ bits[39] = 1
+ elif temp[5] == 1:
+ bits[32] = 1
+ else:
+ pass
+ if temp[6] >= 5:
+ bits[67] = 1
+ bits[74] = 1
+ bits[81] = 1
+ bits[88] = 1
+ bits[95] = 1
+ elif temp[6] == 4:
+ bits[67] = 1
+ bits[74] = 1
+ bits[81] = 1
+ bits[88] = 1
+ elif temp[6] == 3:
+ bits[67] = 1
+ bits[74] = 1
+ bits[81] = 1
+ elif temp[6] == 2:
+ bits[67] = 1
+ bits[74] = 1
+ elif temp[6] == 1:
+ bits[67] = 1
+ else:
+ pass
+ if temp[7] >= 2:
+ bits[102] = 1
+ bits[109] = 1
+ elif temp[7] == 1:
+ bits[102] = 1
+ else:
+ pass
+ if temp[8] >= 2:
+ bits[116] = 1
+ bits[123] = 1
+ elif temp[8] == 1:
+ bits[116] = 1
+ else:
+ pass
+ if temp[9] >= 1:
+ bits[130] = 1
+ else:
+ pass
+ if temp[10] >= 1:
+ bits[137] = 1
+ else:
+ pass
+ return ringSize, bits
+
+
+def func_6(mol, bits):
+ """ *Internal Use Only*
+ unsaturated non-aromatic nitrogen-containing
+ """
+ ringSize = []
+ AllRingsBond = mol.GetRingInfo().BondRings()
+ temp = {3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0}
+ for ring in AllRingsBond:
+ unsaturated = False
+ nonaromatic = True
+ ContainNitro = False
+ ######### unsaturated
+ for bondIdx in ring:
+ if mol.GetBondWithIdx(bondIdx).GetBondType().name != 'SINGLE':
+ unsaturated = True
+ break
+ ######## non-aromatic
+ for bondIdx in ring:
+ if mol.GetBondWithIdx(bondIdx).GetBondType().name == 'AROMATIC':
+ nonaromatic = False
+ break
+ ######## nitrogen-containing
+ for bondIdx in ring:
+ BeginAtom = mol.GetBondWithIdx(bondIdx).GetBeginAtom()
+ EndAtom = mol.GetBondWithIdx(bondIdx).GetEndAtom()
+ if BeginAtom.GetAtomicNum() == 7 or EndAtom.GetAtomicNum() == 7:
+ ContainNitro = True
+ break
+ if unsaturated == True and nonaromatic == True and ContainNitro == True:
+ ringSize.append(len(ring))
+ for k, v in temp.items():
+ if len(ring) == k:
+ temp[k] += 1
+ if temp[3] >= 2:
+ bits[5] = 1
+ bits[12] = 1
+ elif temp[3] == 1:
+ bits[5] = 1
+ else:
+ pass
+ if temp[4] >= 2:
+ bits[19] = 1
+ bits[26] = 1
+ elif temp[4] == 1:
+ bits[19] = 1
+ else:
+ pass
+ if temp[5] >= 5:
+ bits[33] = 1
+ bits[40] = 1
+ bits[47] = 1
+ bits[54] = 1
+ bits[61] = 1
+ elif temp[5] == 4:
+ bits[33] = 1
+ bits[40] = 1
+ bits[47] = 1
+ bits[54] = 1
+ elif temp[5] == 3:
+ bits[33] = 1
+ bits[40] = 1
+ bits[47] = 1
+ elif temp[5] == 2:
+ bits[33] = 1
+ bits[40] = 1
+ elif temp[5] == 1:
+ bits[33] = 1
+ else:
+ pass
+ if temp[6] >= 5:
+ bits[68] = 1
+ bits[75] = 1
+ bits[82] = 1
+ bits[89] = 1
+ bits[96] = 1
+ elif temp[6] == 4:
+ bits[68] = 1
+ bits[75] = 1
+ bits[82] = 1
+ bits[89] = 1
+ elif temp[6] == 3:
+ bits[68] = 1
+ bits[75] = 1
+ bits[82] = 1
+ elif temp[6] == 2:
+ bits[68] = 1
+ bits[75] = 1
+ elif temp[6] == 1:
+ bits[68] = 1
+ else:
+ pass
+ if temp[7] >= 2:
+ bits[103] = 1
+ bits[110] = 1
+ elif temp[7] == 1:
+ bits[103] = 1
+ else:
+ pass
+ if temp[8] >= 2:
+ bits[117] = 1
+ bits[124] = 1
+ elif temp[8] == 1:
+ bits[117] = 1
+ else:
+ pass
+ if temp[9] >= 1:
+ bits[131] = 1
+ else:
+ pass
+ if temp[10] >= 1:
+ bits[138] = 1
+ else:
+ pass
+ return ringSize, bits
+
+
+def func_7(mol, bits):
+ """ *Internal Use Only*
+ unsaturated non-aromatic heteroatom-containing
+ """
+ ringSize = []
+ AllRingsBond = mol.GetRingInfo().BondRings()
+ temp = {3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0}
+ for ring in AllRingsBond:
+ unsaturated = False
+ nonaromatic = True
+ heteroatom = False
+ ######### unsaturated
+ for bondIdx in ring:
+ if mol.GetBondWithIdx(bondIdx).GetBondType().name != 'SINGLE':
+ unsaturated = True
+ break
+ ######## non-aromatic
+ for bondIdx in ring:
+ if mol.GetBondWithIdx(bondIdx).GetBondType().name == 'AROMATIC':
+ nonaromatic = False
+ break
+ ######## heteroatom-containing
+ for bondIdx in ring:
+ BeginAtom = mol.GetBondWithIdx(bondIdx).GetBeginAtom()
+ EndAtom = mol.GetBondWithIdx(bondIdx).GetEndAtom()
+ if BeginAtom.GetAtomicNum() not in [1, 6] or EndAtom.GetAtomicNum() not in [1, 6]:
+ heteroatom = True
+ break
+ if unsaturated == True and nonaromatic == True and heteroatom == True:
+ ringSize.append(len(ring))
+ for k, v in temp.items():
+ if len(ring) == k:
+ temp[k] += 1
+ if temp[3] >= 2:
+ bits[6] = 1
+ bits[13] = 1
+ elif temp[3] == 1:
+ bits[6] = 1
+ else:
+ pass
+ if temp[4] >= 2:
+ bits[20] = 1
+ bits[27] = 1
+ elif temp[4] == 1:
+ bits[20] = 1
+ else:
+ pass
+ if temp[5] >= 5:
+ bits[34] = 1
+ bits[41] = 1
+ bits[48] = 1
+ bits[55] = 1
+ bits[62] = 1
+ elif temp[5] == 4:
+ bits[34] = 1
+ bits[41] = 1
+ bits[48] = 1
+ bits[55] = 1
+ elif temp[5] == 3:
+ bits[34] = 1
+ bits[41] = 1
+ bits[48] = 1
+ elif temp[5] == 2:
+ bits[34] = 1
+ bits[41] = 1
+ elif temp[5] == 1:
+ bits[34] = 1
+ else:
+ pass
+ if temp[6] >= 5:
+ bits[69] = 1
+ bits[76] = 1
+ bits[83] = 1
+ bits[90] = 1
+ bits[97] = 1
+ elif temp[6] == 4:
+ bits[69] = 1
+ bits[76] = 1
+ bits[83] = 1
+ bits[90] = 1
+ elif temp[6] == 3:
+ bits[69] = 1
+ bits[76] = 1
+ bits[83] = 1
+ elif temp[6] == 2:
+ bits[69] = 1
+ bits[76] = 1
+ elif temp[6] == 1:
+ bits[69] = 1
+ else:
+ pass
+ if temp[7] >= 2:
+ bits[104] = 1
+ bits[111] = 1
+ elif temp[7] == 1:
+ bits[104] = 1
+ else:
+ pass
+ if temp[8] >= 2:
+ bits[118] = 1
+ bits[125] = 1
+ elif temp[8] == 1:
+ bits[118] = 1
+ else:
+ pass
+ if temp[9] >= 1:
+ bits[132] = 1
+ else:
+ pass
+ if temp[10] >= 1:
+ bits[139] = 1
+ else:
+ pass
+ return ringSize, bits
+
+
+def func_8(mol, bits):
+ """ *Internal Use Only*
+ aromatic rings or hetero-aromatic rings
+ """
+ AllRingsBond = mol.GetRingInfo().BondRings()
+ temp = {'aromatic': 0, 'heteroatom': 0}
+ for ring in AllRingsBond:
+ aromatic = True
+ heteroatom = False
+ for bondIdx in ring:
+ if mol.GetBondWithIdx(bondIdx).GetBondType().name != 'AROMATIC':
+ aromatic = False
+ break
+ if aromatic == True:
+ temp['aromatic'] += 1
+ for bondIdx in ring:
+ BeginAtom = mol.GetBondWithIdx(bondIdx).GetBeginAtom()
+ EndAtom = mol.GetBondWithIdx(bondIdx).GetEndAtom()
+ if BeginAtom.GetAtomicNum() not in [1, 6] or EndAtom.GetAtomicNum() not in [1, 6]:
+ heteroatom = True
+ break
+ if heteroatom == True:
+ temp['heteroatom'] += 1
+ if temp['aromatic'] >= 4:
+ bits[140] = 1
+ bits[142] = 1
+ bits[144] = 1
+ bits[146] = 1
+ elif temp['aromatic'] == 3:
+ bits[140] = 1
+ bits[142] = 1
+ bits[144] = 1
+ elif temp['aromatic'] == 2:
+ bits[140] = 1
+ bits[142] = 1
+ elif temp['aromatic'] == 1:
+ bits[140] = 1
+ else:
+ pass
+ if temp['aromatic'] >= 4 and temp['heteroatom'] >= 4:
+ bits[141] = 1
+ bits[143] = 1
+ bits[145] = 1
+ bits[147] = 1
+ elif temp['aromatic'] == 3 and temp['heteroatom'] == 3:
+ bits[141] = 1
+ bits[143] = 1
+ bits[145] = 1
+ elif temp['aromatic'] == 2 and temp['heteroatom'] == 2:
+ bits[141] = 1
+ bits[143] = 1
+ elif temp['aromatic'] == 1 and temp['heteroatom'] == 1:
+ bits[141] = 1
+ else:
+ pass
+ return bits
+
+
+def calcPubChemFingerPart2(mol): # 116-263
+ """ *Internal Use Only*
+ Calculate PubChem Fingerprints （116-263)
+ """
+ bits = [0] * 148
+ bits = func_1(mol, bits)[1]
+ bits = func_2(mol, bits)[1]
+ bits = func_3(mol, bits)[1]
+ bits = func_4(mol, bits)[1]
+ bits = func_5(mol, bits)[1]
+ bits = func_6(mol, bits)[1]
+ bits = func_7(mol, bits)[1]
+ bits = func_8(mol, bits)
+
+ return bits
+
+
+def GetPubChemFPs(mol):
+ """*Internal Use Only*
+ Calculate PubChem Fingerprints
+ """
+ mol = Chem.AddHs(mol)
+ AllBits = [0] * 881
+ res1 = list(calcPubChemFingerPart1(mol).ToBitString())
+ for index, item in enumerate(res1[1:116]):
+ if item == '1':
+ AllBits[index] = 1
+ for index2, item2 in enumerate(res1[116:734]):
+ if item2 == '1':
+ AllBits[index2 + 115 + 148] = 1
+ res2 = calcPubChemFingerPart2(mol)
+ for index3, item3 in enumerate(res2):
+ if item3 == 1:
+ AllBits[index3 + 115] = 1
+ AllBits = np.array(AllBits, dtype=np.bool_)
+
+ return AllBits
+
+
+# ------------------------------------
+
+
+file_path = os.path.dirname(__file__)
+
+
+def GetPubChemFPInfos():
+ return pd.read_excel(os.path.join(file_path, 'pubchemfp.xlsx'))
+
+
+if __name__ == '__main__':
+ print('-' * 10 + 'START' + '-' * 10)
+ SMILES = 'C1=NC2NC3=CNCC3=CC2CC1'
+ mol = Chem.MolFromSmiles(SMILES)
+ mol2 = Chem.AddHs(mol)
+ result = GetPubChemFPs(mol2)
+ print('Molecule: %s' % SMILES)
+ print('-' * 25)
+ print('Results: %s' % result)
+ print('-' * 10 + 'END' + '-' * 10)
diff --git a/deepscreen/data/featurizers/fingerprint/pubchemfp.xlsx b/deepscreen/data/featurizers/fingerprint/pubchemfp.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..464009090fdbbd5cc156076a3a9abc973f0320e9
Binary files /dev/null and b/deepscreen/data/featurizers/fingerprint/pubchemfp.xlsx differ
diff --git a/deepscreen/data/featurizers/fingerprint/rdkitfp.py b/deepscreen/data/featurizers/fingerprint/rdkitfp.py
new file mode 100644
index 0000000000000000000000000000000000000000..a56fd8e267ba57cdc911edb9bf65f1ff030ff574
--- /dev/null
+++ b/deepscreen/data/featurizers/fingerprint/rdkitfp.py
@@ -0,0 +1,42 @@
+"""
+topological fingerprint
+
+"""
+
+import numpy as np
+from rdkit.Chem.rdmolops import RDKFingerprint
+from rdkit.Chem import DataStructs
+
+_type = 'topological-based'
+
+
+def GetRDkitFPs(mol, nBits=2048, return_bitInfo=False):
+ """
+ #################################################################
+ Calculate Daylight-like fingerprint or topological fingerprint
+
+ (1024 bits).
+
+ Usage:
+
+ result=CalculateDaylightFingerprint(mol)
+
+ Input: mol is a molecule object.
+
+ Output: result is a tuple form. The first is the number of
+
+ fingerprints. The second is a dict form whose keys are the
+
+ position which this molecule has some substructure. The third
+
+ is the DataStructs which is used for calculating the similarity.
+ #################################################################
+ """
+
+ bitInfo = {}
+ fp = RDKFingerprint(mol, fpSize=nBits, bitInfo=bitInfo)
+ arr = np.zeros((0,), dtype=np.bool_)
+ DataStructs.ConvertToNumpyArray(fp, arr)
+ if return_bitInfo:
+ return arr, return_bitInfo
+ return arr
diff --git a/deepscreen/data/featurizers/fingerprint/smarts_maccskey.py b/deepscreen/data/featurizers/fingerprint/smarts_maccskey.py
new file mode 100644
index 0000000000000000000000000000000000000000..4546568d20e31efbe47f04b51d20e895c014327f
--- /dev/null
+++ b/deepscreen/data/featurizers/fingerprint/smarts_maccskey.py
@@ -0,0 +1,178 @@
+smartsPatts = {
+ 'MACCSFP0': (None, 0),
+ # ignore, Bit 0 is a placeholder and should be ignored: https://github.com/rdkit/rdkit/issues/1726
+ 'MACCSFP1': ('?', 0),
+ 'MACCSFP2': ('[#104]', 0),
+ 'MACCSFP3': ('[#32,#33,#34,#50,#51,#52,#82,#83,#84]', 0),
+ 'MACCSFP4': ('[Ac,Th,Pa,U,Np,Pu,Am,Cm,Bk,Cf,Es,Fm,Md,No,Lr]', 0),
+ 'MACCSFP5': ('[Sc,Ti,Y,Zr,Hf]', 0),
+ 'MACCSFP6': ('[La,Ce,Pr,Nd,Pm,Sm,Eu,Gd,Tb,Dy,Ho,Er,Tm,Yb,Lu]', 0),
+ 'MACCSFP7': ('[V,Cr,Mn,Nb,Mo,Tc,Ta,W,Re]', 0),
+ 'MACCSFP8': ('[!#6;!#1]1~*~*~*~1', 0),
+ 'MACCSFP9': ('[Fe,Co,Ni,Ru,Rh,Pd,Os,Ir,Pt]', 0),
+ 'MACCSFP10': ('[Be,Mg,Ca,Sr,Ba,Ra]', 0),
+ 'MACCSFP11': ('*1~*~*~*~1', 0),
+ 'MACCSFP12': ('[Cu,Zn,Ag,Cd,Au,Hg]', 0),
+ 'MACCSFP13': ('[#8]~[#7](~[#6])~[#6]', 0),
+ 'MACCSFP14': ('[#16]-[#16]', 0),
+ 'MACCSFP15': ('[#8]~[#6](~[#8])~[#8]', 0),
+ 'MACCSFP16': ('[!#6;!#1]1~*~*~1', 0),
+ 'MACCSFP17': ('[#6]#[#6]', 0),
+ 'MACCSFP18': ('[#5,#13,#31,#49,#81]', 0),
+ 'MACCSFP19': ('*1~*~*~*~*~*~*~1', 0),
+ 'MACCSFP20': ('[#14]', 0),
+ 'MACCSFP21': ('[#6]=[#6](~[!#6;!#1])~[!#6;!#1]', 0),
+ 'MACCSFP22': ('*1~*~*~1', 0),
+ 'MACCSFP23': ('[#7]~[#6](~[#8])~[#8]', 0),
+ 'MACCSFP24': ('[#7]-[#8]', 0),
+ 'MACCSFP25': ('[#7]~[#6](~[#7])~[#7]', 0),
+ 'MACCSFP26': ('[#6]=;@[#6](@*)@*', 0),
+ 'MACCSFP27': ('[I]', 0),
+ 'MACCSFP28': ('[!#6;!#1]~[CH2]~[!#6;!#1]', 0),
+ 'MACCSFP29': ('[#15]', 0),
+ 'MACCSFP30': ('[#6]~[!#6;!#1](~[#6])(~[#6])~*', 0),
+ 'MACCSFP31': ('[!#6;!#1]~[F,Cl,Br,I]', 0),
+ 'MACCSFP32': ('[#6]~[#16]~[#7]', 0),
+ 'MACCSFP33': ('[#7]~[#16]', 0),
+ 'MACCSFP34': ('[CH2]=*', 0),
+ 'MACCSFP35': ('[Li,Na,K,Rb,Cs,Fr]', 0),
+ 'MACCSFP36': ('[#16R]', 0),
+ 'MACCSFP37': ('[#7]~[#6](~[#8])~[#7]', 0),
+ 'MACCSFP38': ('[#7]~[#6](~[#6])~[#7]', 0),
+ 'MACCSFP39': ('[#8]~[#16](~[#8])~[#8]', 0),
+ 'MACCSFP40': ('[#16]-[#8]', 0),
+ 'MACCSFP41': ('[#6]#[#7]', 0),
+ 'MACCSFP42': ('F', 0),
+ 'MACCSFP43': ('[!#6;!#1;!H0]~*~[!#6;!#1;!H0]', 0),
+ 'MACCSFP44': ('?', 0),
+ 'MACCSFP45': ('[#6]=[#6]~[#7]', 0),
+ 'MACCSFP46': ('Br', 0),
+ 'MACCSFP47': ('[#16]~*~[#7]', 0),
+ 'MACCSFP48': ('[#8]~[!#6;!#1](~[#8])(~[#8])', 0),
+ 'MACCSFP49': ('[!+0]', 0),
+ 'MACCSFP50': ('[#6]=[#6](~[#6])~[#6]', 0),
+ 'MACCSFP51': ('[#6]~[#16]~[#8]', 0),
+ 'MACCSFP52': ('[#7]~[#7]', 0),
+ 'MACCSFP53': ('[!#6;!#1;!H0]~*~*~*~[!#6;!#1;!H0]', 0),
+ 'MACCSFP54': ('[!#6;!#1;!H0]~*~*~[!#6;!#1;!H0]', 0),
+ 'MACCSFP55': ('[#8]~[#16]~[#8]', 0),
+ 'MACCSFP56': ('[#8]~[#7](~[#8])~[#6]', 0),
+ 'MACCSFP57': ('[#8R]', 0),
+ 'MACCSFP58': ('[!#6;!#1]~[#16]~[!#6;!#1]', 0),
+ 'MACCSFP59': ('[#16]!:*:*', 0),
+ 'MACCSFP60': ('[#16]=[#8]', 0),
+ 'MACCSFP61': ('*~[#16](~*)~*', 0),
+ 'MACCSFP62': ('*@*!@*@*', 0),
+ 'MACCSFP63': ('[#7]=[#8]', 0),
+ 'MACCSFP64': ('*@*!@[#16]', 0),
+ 'MACCSFP65': ('c:n', 0),
+ 'MACCSFP66': ('[#6]~[#6](~[#6])(~[#6])~*', 0),
+ 'MACCSFP67': ('[!#6;!#1]~[#16]', 0),
+ 'MACCSFP68': ('[!#6;!#1;!H0]~[!#6;!#1;!H0]', 0),
+ 'MACCSFP69': ('[!#6;!#1]~[!#6;!#1;!H0]', 0),
+ 'MACCSFP70': ('[!#6;!#1]~[#7]~[!#6;!#1]', 0),
+ 'MACCSFP71': ('[#7]~[#8]', 0),
+ 'MACCSFP72': ('[#8]~*~*~[#8]', 0),
+ 'MACCSFP73': ('[#16]=*', 0),
+ 'MACCSFP74': ('[CH3]~*~[CH3]', 0),
+ 'MACCSFP75': ('*!@[#7]@*', 0),
+ 'MACCSFP76': ('[#6]=[#6](~*)~*', 0),
+ 'MACCSFP77': ('[#7]~*~[#7]', 0),
+ 'MACCSFP78': ('[#6]=[#7]', 0),
+ 'MACCSFP79': ('[#7]~*~*~[#7]', 0),
+ 'MACCSFP80': ('[#7]~*~*~*~[#7]', 0),
+ 'MACCSFP81': ('[#16]~*(~*)~*', 0),
+ 'MACCSFP82': ('*~[CH2]~[!#6;!#1;!H0]', 0),
+ 'MACCSFP83': ('[!#6;!#1]1~*~*~*~*~1', 0),
+ 'MACCSFP84': ('[NH2]', 0),
+ 'MACCSFP85': ('[#6]~[#7](~[#6])~[#6]', 0),
+ 'MACCSFP86': ('[C;H2,H3][!#6;!#1][C;H2,H3]', 0),
+ 'MACCSFP87': ('[F,Cl,Br,I]!@*@*', 0),
+ 'MACCSFP88': ('[#16]', 0),
+ 'MACCSFP89': ('[#8]~*~*~*~[#8]', 0),
+ 'MACCSFP90': (
+ '[$([!#6;!#1;!H0]~*~*~[CH2]~*),$([!#6;!#1;!H0;R]1@[R]@[R]@[CH2;R]1),$([!#6;!#1;!H0]~[R]1@[R]@[CH2;R]1)]',
+ 0),
+ 'MACCSFP91': (
+ '[$([!#6;!#1;!H0]~*~*~*~[CH2]~*),$([!#6;!#1;!H0;R]1@[R]@[R]@[R]@[CH2;R]1),$([!#6;!#1;!H0]~[R]1@[R]@[R]@[CH2;R]1),$([!#6;!#1;!H0]~*~[R]1@[R]@[CH2;R]1)]',
+ 0),
+ 'MACCSFP92': ('[#8]~[#6](~[#7])~[#6]', 0),
+ 'MACCSFP93': ('[!#6;!#1]~[CH3]', 0),
+ 'MACCSFP94': ('[!#6;!#1]~[#7]', 0),
+ 'MACCSFP95': ('[#7]~*~*~[#8]', 0),
+ 'MACCSFP96': ('*1~*~*~*~*~1', 0),
+ 'MACCSFP97': ('[#7]~*~*~*~[#8]', 0),
+ 'MACCSFP98': ('[!#6;!#1]1~*~*~*~*~*~1', 0),
+ 'MACCSFP99': ('[#6]=[#6]', 0),
+ 'MACCSFP100': ('*~[CH2]~[#7]', 0),
+ 'MACCSFP101': (
+ '[$([R]@1@[R]@[R]@[R]@[R]@[R]@[R]@[R]1),$([R]@1@[R]@[R]@[R]@[R]@[R]@[R]@[R]@[R]1),$([R]@1@[R]@[R]@[R]@[R]@[R]@[R]@[R]@[R]@[R]1),$([R]@1@[R]@[R]@[R]@[R]@[R]@[R]@[R]@[R]@[R]@[R]1),$([R]@1@[R]@[R]@[R]@[R]@[R]@[R]@[R]@[R]@[R]@[R]@[R]1),$([R]@1@[R]@[R]@[R]@[R]@[R]@[R]@[R]@[R]@[R]@[R]@[R]@[R]1),$([R]@1@[R]@[R]@[R]@[R]@[R]@[R]@[R]@[R]@[R]@[R]@[R]@[R]@[R]1)]',
+ 0),
+ 'MACCSFP102': ('[!#6;!#1]~[#8]', 0),
+ 'MACCSFP103': ('Cl', 0),
+ 'MACCSFP104': ('[!#6;!#1;!H0]~*~[CH2]~*', 0),
+ 'MACCSFP105': ('*@*(@*)@*', 0),
+ 'MACCSFP106': ('[!#6;!#1]~*(~[!#6;!#1])~[!#6;!#1]', 0),
+ 'MACCSFP107': ('[F,Cl,Br,I]~*(~*)~*', 0),
+ 'MACCSFP108': ('[CH3]~*~*~*~[CH2]~*', 0),
+ 'MACCSFP109': ('*~[CH2]~[#8]', 0),
+ 'MACCSFP110': ('[#7]~[#6]~[#8]', 0),
+ 'MACCSFP111': ('[#7]~*~[CH2]~*', 0),
+ 'MACCSFP112': ('*~*(~*)(~*)~*', 0),
+ 'MACCSFP113': ('[#8]!:*:*', 0),
+ 'MACCSFP114': ('[CH3]~[CH2]~*', 0),
+ 'MACCSFP115': ('[CH3]~*~[CH2]~*', 0),
+ 'MACCSFP116': ('[$([CH3]~*~*~[CH2]~*),$([CH3]~*1~*~[CH2]1)]', 0),
+ 'MACCSFP117': ('[#7]~*~[#8]', 0),
+ 'MACCSFP118': ('[$(*~[CH2]~[CH2]~*),$(*1~[CH2]~[CH2]1)]', 1),
+ 'MACCSFP119': ('[#7]=*', 0),
+ 'MACCSFP120': ('[!#6;R]', 1),
+ 'MACCSFP121': ('[#7;R]', 0),
+ 'MACCSFP122': ('*~[#7](~*)~*', 0),
+ 'MACCSFP123': ('[#8]~[#6]~[#8]', 0),
+ 'MACCSFP124': ('[!#6;!#1]~[!#6;!#1]', 0),
+ 'MACCSFP125': ('?', 0),
+ 'MACCSFP126': ('*!@[#8]!@*', 0),
+ 'MACCSFP127': ('*@*!@[#8]', 1),
+ 'MACCSFP128': (
+ '[$(*~[CH2]~*~*~*~[CH2]~*),$([R]1@[CH2;R]@[R]@[R]@[R]@[CH2;R]1),$(*~[CH2]~[R]1@[R]@[R]@[CH2;R]1),$(*~[CH2]~*~[R]1@[R]@[CH2;R]1)]',
+ 0),
+ 'MACCSFP129': ('[$(*~[CH2]~*~*~[CH2]~*),$([R]1@[CH2]@[R]@[R]@[CH2;R]1),$(*~[CH2]~[R]1@[R]@[CH2;R]1)]',
+ 0),
+ 'MACCSFP130': ('[!#6;!#1]~[!#6;!#1]', 1),
+ 'MACCSFP131': ('[!#6;!#1;!H0]', 1),
+ 'MACCSFP132': ('[#8]~*~[CH2]~*', 0),
+ 'MACCSFP133': ('*@*!@[#7]', 0),
+ 'MACCSFP134': ('[F,Cl,Br,I]', 0),
+ 'MACCSFP135': ('[#7]!:*:*', 0),
+ 'MACCSFP136': ('[#8]=*', 1),
+ 'MACCSFP137': ('[!C;!c;R]', 0),
+ 'MACCSFP138': ('[!#6;!#1]~[CH2]~*', 1),
+ 'MACCSFP139': ('[O;!H0]', 0),
+ 'MACCSFP140': ('[#8]', 3),
+ 'MACCSFP141': ('[CH3]', 2),
+ 'MACCSFP142': ('[#7]', 1),
+ 'MACCSFP143': ('*@*!@[#8]', 0),
+ 'MACCSFP144': ('*!:*:*!:*', 0),
+ 'MACCSFP145': ('*1~*~*~*~*~*~1', 1),
+ 'MACCSFP146': ('[#8]', 2),
+ 'MACCSFP147': ('[$(*~[CH2]~[CH2]~*),$([R]1@[CH2;R]@[CH2;R]1)]', 0),
+ 'MACCSFP148': ('*~[!#6;!#1](~*)~*', 0),
+ 'MACCSFP149': ('[C;H3,H4]', 1),
+ 'MACCSFP150': ('*!@*@*!@*', 0),
+ 'MACCSFP151': ('[#7;!H0]', 0),
+ 'MACCSFP152': ('[#8]~[#6](~[#6])~[#6]', 0),
+ 'MACCSFP153': ('[!#6;!#1]~[CH2]~*', 0),
+ 'MACCSFP154': ('[#6]=[#8]', 0),
+ 'MACCSFP155': ('*!@[CH2]!@*', 0),
+ 'MACCSFP156': ('[#7]~*(~*)~*', 0),
+ 'MACCSFP157': ('[#6]-[#8]', 0),
+ 'MACCSFP158': ('[#6]-[#7]', 0),
+ 'MACCSFP159': ('[#8]', 1),
+ 'MACCSFP160': ('[C;H3,H4]', 0),
+ 'MACCSFP161': ('[#7]', 0),
+ 'MACCSFP162': ('a', 0),
+ 'MACCSFP163': ('*1~*~*~*~*~*~1', 0),
+ 'MACCSFP164': ('[#8]', 0),
+ 'MACCSFP165': ('[R]', 0),
+ 'MACCSFP166': ('?', 0)}
diff --git a/deepscreen/data/featurizers/fingerprint/smarts_pharmacophore.py b/deepscreen/data/featurizers/fingerprint/smarts_pharmacophore.py
new file mode 100644
index 0000000000000000000000000000000000000000..51a0a687890ca008811d7c1181e0b1f11735c582
--- /dev/null
+++ b/deepscreen/data/featurizers/fingerprint/smarts_pharmacophore.py
@@ -0,0 +1,21 @@
+Donor = ["[N;!H0;v3,v4&+1]", "[O,S;H1;+0]", "[n&H1&+0]"]
+
+Acceptor = ["[O,S;H1;v2;!$(*-*=[O,N,P,S])]", "[O;H0;v2]", "[O,S;v1;-]",
+ "[N;v3;!$(N-*=[O,N,P,S])]", "[n&H0&+0]", "[o;+0;!$([o]:n);!$([o]:c:n)]"]
+
+Positive = ["[#7;+]", "[N;H2&+0][$([C,a]);!$([C,a](=O))]",
+ "[N;H1&+0]([$([C,a]);!$([C,a](=O))])[$([C,a]);!$([C,a](=O))]",
+ "[N;H0&+0]([C;!$(C(=O))])([C;!$(C(=O))])[C;!$(C(=O))]"]
+
+Negative = ["[C,S](=[O,S,P])-[O;H1,-1]"]
+
+Hydrophobic = ["[C;D3,D4](-[CH3])-[CH3]", "[S;D2](-C)-C"]
+
+Aromatic = ["a"]
+
+pharmacophore_smarts = {"Donor": Donor,
+ "Acceptor": Acceptor,
+ "Positive": Positive,
+ "Negative": Negative,
+ "Hydrophobic": Hydrophobic,
+ "Aromatic": Aromatic}
diff --git a/deepscreen/data/featurizers/fingerprint/smarts_pubchem.py b/deepscreen/data/featurizers/fingerprint/smarts_pubchem.py
new file mode 100644
index 0000000000000000000000000000000000000000..64393ffc9f633d50f363108022099b33981a301d
--- /dev/null
+++ b/deepscreen/data/featurizers/fingerprint/smarts_pubchem.py
@@ -0,0 +1,734 @@
+smartsPatts = {
+ 'PubChemFP0': ('[H]', 3),
+ 'PubChemFP1': ('[H]', 7),
+ 'PubChemFP2': ('[H]', 15),
+ 'PubChemFP3': ('[H]', 31),
+ 'PubChemFP4': ('[Li]', 0),
+ 'PubChemFP5': ('[Li]', 1),
+ 'PubChemFP6': ('[B]', 0),
+ 'PubChemFP7': ('[B]', 1),
+ 'PubChemFP8': ('[B]', 3),
+ 'PubChemFP9': ('[C]', 1),
+ 'PubChemFP10': ('[C]', 3),
+ 'PubChemFP11': ('[C]', 7),
+ 'PubChemFP12': ('[C]', 15),
+ 'PubChemFP13': ('[C]', 31),
+ 'PubChemFP14': ('[N]', 0),
+ 'PubChemFP15': ('[N]', 1),
+ 'PubChemFP16': ('[N]', 3),
+ 'PubChemFP17': ('[N]', 7),
+ 'PubChemFP18': ('[O]', 0),
+ 'PubChemFP19': ('[O]', 1),
+ 'PubChemFP20': ('[O]', 3),
+ 'PubChemFP21': ('[O]', 7),
+ 'PubChemFP22': ('[O]', 15),
+ 'PubChemFP23': ('[F]', 0),
+ 'PubChemFP24': ('[F]', 1),
+ 'PubChemFP25': ('[F]', 3),
+ 'PubChemFP26': ('[Na]', 0),
+ 'PubChemFP27': ('[Na]', 1),
+ 'PubChemFP28': ('[Si]', 0),
+ 'PubChemFP29': ('[Si]', 1),
+ 'PubChemFP30': ('[P]', 0),
+ 'PubChemFP31': ('[P]', 1),
+ 'PubChemFP32': ('[P]', 3),
+ 'PubChemFP33': ('[S]', 0),
+ 'PubChemFP34': ('[S]', 1),
+ 'PubChemFP35': ('[S]', 3),
+ 'PubChemFP36': ('[S]', 7),
+ 'PubChemFP37': ('[Cl]', 0),
+ 'PubChemFP38': ('[Cl]', 1),
+ 'PubChemFP39': ('[Cl]', 3),
+ 'PubChemFP40': ('[Cl]', 7),
+ 'PubChemFP41': ('[K]', 0),
+ 'PubChemFP42': ('[K]', 1),
+ 'PubChemFP43': ('[Br]', 0),
+ 'PubChemFP44': ('[Br]', 1),
+ 'PubChemFP45': ('[Br]', 3),
+ 'PubChemFP46': ('[I]', 0),
+ 'PubChemFP47': ('[I]', 1),
+ 'PubChemFP48': ('[I]', 3),
+ 'PubChemFP49': ('[Be]', 0),
+ 'PubChemFP50': ('[Mg]', 0),
+ 'PubChemFP51': ('[Al]', 0),
+ 'PubChemFP52': ('[Ca]', 0),
+ 'PubChemFP53': ('[Sc]', 0),
+ 'PubChemFP54': ('[Ti]', 0),
+ 'PubChemFP55': ('[V]', 0),
+ 'PubChemFP56': ('[Cr]', 0),
+ 'PubChemFP57': ('[Mn]', 0),
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+ 'PubChemFP767': ('[#16]c1c([#7])cccc1', 0),
+ 'PubChemFP768': ('[#16]c1c(Cl)cccc1', 0),
+ 'PubChemFP769': ('[#16]c1c(Br)cccc1', 0),
+ 'PubChemFP770': ('[#7]c1c([#7])cccc1', 0),
+ 'PubChemFP771': ('[#7]c1c(Cl)cccc1', 0),
+ 'PubChemFP772': ('[#7]c1c(Br)cccc1', 0),
+ 'PubChemFP773': ('Clc1c(Cl)cccc1', 0),
+ 'PubChemFP774': ('Clc1c(Br)cccc1', 0),
+ 'PubChemFP775': ('Brc1c(Br)cccc1', 0),
+ 'PubChemFP776': ('[#6][#6]1[#6][#6][#6]([#6])[#6][#6]1', 0),
+ 'PubChemFP777': ('[#6][#6]1[#6][#6][#6]([#8])[#6][#6]1', 0),
+ 'PubChemFP778': ('[#6][#6]1[#6][#6][#6]([#16])[#6][#6]1', 0),
+ 'PubChemFP779': ('[#6][#6]1[#6][#6][#6]([#7])[#6][#6]1', 0),
+ 'PubChemFP780': ('[#6][#6]1[#6][#6][#6](Cl)[#6][#6]1', 0),
+ 'PubChemFP781': ('[#6][#6]1[#6][#6][#6](Br)[#6][#6]1', 0),
+ 'PubChemFP782': ('[#8][#6]1[#6][#6][#6]([#8])[#6][#6]1', 0),
+ 'PubChemFP783': ('[#8][#6]1[#6][#6][#6]([#16])[#6][#6]1', 0),
+ 'PubChemFP784': ('[#8][#6]1[#6][#6][#6]([#7])[#6][#6]1', 0),
+ 'PubChemFP785': ('[#8][#6]1[#6][#6][#6](Cl)[#6][#6]1', 0),
+ 'PubChemFP786': ('[#8][#6]1[#6][#6][#6](Br)[#6][#6]1', 0),
+ 'PubChemFP787': ('[#16][#6]1[#6][#6][#6]([#16])[#6][#6]1', 0),
+ 'PubChemFP788': ('[#16][#6]1[#6][#6][#6]([#7])[#6][#6]1', 0),
+ 'PubChemFP789': ('[#16][#6]1[#6][#6][#6](Cl)[#6][#6]1', 0),
+ 'PubChemFP790': ('[#16][#6]1[#6][#6][#6](Br)[#6][#6]1', 0),
+ 'PubChemFP791': ('[#7][#6]1[#6][#6][#6]([#7])[#6][#6]1', 0),
+ 'PubChemFP792': ('[#7][#6]1[#6][#6][#6](Cl)[#6][#6]1', 0),
+ 'PubChemFP793': ('[#7][#6]1[#6][#6][#6](Br)[#6][#6]1', 0),
+ 'PubChemFP794': ('Cl[#6]1[#6][#6][#6](Cl)[#6][#6]1', 0),
+ 'PubChemFP795': ('Cl[#6]1[#6][#6][#6](Br)[#6][#6]1', 0),
+ 'PubChemFP796': ('Br[#6]1[#6][#6][#6](Br)[#6][#6]1', 0),
+ 'PubChemFP797': ('[#6][#6]1[#6][#6]([#6])[#6][#6][#6]1', 0),
+ 'PubChemFP798': ('[#6][#6]1[#6][#6]([#8])[#6][#6][#6]1', 0),
+ 'PubChemFP799': ('[#6][#6]1[#6][#6]([#16])[#6][#6][#6]1', 0),
+ 'PubChemFP800': ('[#6][#6]1[#6][#6]([#7])[#6][#6][#6]1', 0),
+ 'PubChemFP801': ('[#6][#6]1[#6][#6](Cl)[#6][#6][#6]1', 0),
+ 'PubChemFP802': ('[#6][#6]1[#6][#6](Br)[#6][#6][#6]1', 0),
+ 'PubChemFP803': ('[#8][#6]1[#6][#6]([#8])[#6][#6][#6]1', 0),
+ 'PubChemFP804': ('[#8][#6]1[#6][#6]([#16])[#6][#6][#6]1', 0),
+ 'PubChemFP805': ('[#8][#6]1[#6][#6]([#7])[#6][#6][#6]1', 0),
+ 'PubChemFP806': ('[#8][#6]1[#6][#6](Cl)[#6][#6][#6]1', 0),
+ 'PubChemFP807': ('[#8][#6]1[#6][#6](Br)[#6][#6][#6]1', 0),
+ 'PubChemFP808': ('[#16][#6]1[#6][#6]([#16])[#6][#6][#6]1', 0),
+ 'PubChemFP809': ('[#16][#6]1[#6][#6]([#7])[#6][#6][#6]1', 0),
+ 'PubChemFP810': ('[#16][#6]1[#6][#6](Cl)[#6][#6][#6]1', 0),
+ 'PubChemFP811': ('[#16][#6]1[#6][#6](Br)[#6][#6][#6]1', 0),
+ 'PubChemFP812': ('[#7][#6]1[#6][#6]([#7])[#6][#6][#6]1', 0),
+ 'PubChemFP813': ('[#7][#6]1[#6][#6](Cl)[#6][#6][#6]1', 0),
+ 'PubChemFP814': ('[#7][#6]1[#6][#6](Br)[#6][#6][#6]1', 0),
+ 'PubChemFP815': ('Cl[#6]1[#6][#6](Cl)[#6][#6][#6]1', 0),
+ 'PubChemFP816': ('Cl[#6]1[#6][#6](Br)[#6][#6][#6]1', 0),
+ 'PubChemFP817': ('Br[#6]1[#6][#6](Br)[#6][#6][#6]1', 0),
+ 'PubChemFP818': ('[#6][#6]1[#6]([#6])[#6][#6][#6][#6]1', 0),
+ 'PubChemFP819': ('[#6][#6]1[#6]([#8])[#6][#6][#6][#6]1', 0),
+ 'PubChemFP820': ('[#6][#6]1[#6]([#16])[#6][#6][#6][#6]1', 0),
+ 'PubChemFP821': ('[#6][#6]1[#6]([#7])[#6][#6][#6][#6]1', 0),
+ 'PubChemFP822': ('[#6][#6]1[#6](Cl)[#6][#6][#6][#6]1', 0),
+ 'PubChemFP823': ('[#6][#6]1[#6](Br)[#6][#6][#6][#6]1', 0),
+ 'PubChemFP824': ('[#8][#6]1[#6]([#8])[#6][#6][#6][#6]1', 0),
+ 'PubChemFP825': ('[#8][#6]1[#6]([#16])[#6][#6][#6][#6]1', 0),
+ 'PubChemFP826': ('[#8][#6]1[#6]([#7])[#6][#6][#6][#6]1', 0),
+ 'PubChemFP827': ('[#8][#6]1[#6](Cl)[#6][#6][#6][#6]1', 0),
+ 'PubChemFP828': ('[#8][#6]1[#6](Br)[#6][#6][#6][#6]1', 0),
+ 'PubChemFP829': ('[#16][#6]1[#6]([#16])[#6][#6][#6][#6]1', 0),
+ 'PubChemFP830': ('[#16][#6]1[#6]([#7])[#6][#6][#6][#6]1', 0),
+ 'PubChemFP831': ('[#16][#6]1[#6](Cl)[#6][#6][#6][#6]1', 0),
+ 'PubChemFP832': ('[#16][#6]1[#6](Br)[#6][#6][#6][#6]1', 0),
+ 'PubChemFP833': ('[#7][#6]1[#6]([#7])[#6][#6][#6][#6]1', 0),
+ 'PubChemFP834': ('[#7][#6]1[#6](Cl)[#6][#6][#6][#6]1', 0),
+ 'PubChemFP835': ('[#7][#6]1[#6](Br)[#6][#6][#6][#6]1', 0),
+ 'PubChemFP836': ('Cl[#6]1[#6](Cl)[#6][#6][#6][#6]1', 0),
+ 'PubChemFP837': ('Cl[#6]1[#6](Br)[#6][#6][#6][#6]1', 0),
+ 'PubChemFP838': ('Br[#6]1[#6](Br)[#6][#6][#6][#6]1', 0),
+ 'PubChemFP839': ('[#6][#6]1[#6][#6]([#6])[#6][#6]1', 0),
+ 'PubChemFP840': ('[#6][#6]1[#6][#6]([#8])[#6][#6]1', 0),
+ 'PubChemFP841': ('[#6][#6]1[#6][#6]([#16])[#6][#6]1', 0),
+ 'PubChemFP842': ('[#6][#6]1[#6][#6]([#7])[#6][#6]1', 0),
+ 'PubChemFP843': ('[#6][#6]1[#6][#6](Cl)[#6][#6]1', 0),
+ 'PubChemFP844': ('[#6][#6]1[#6][#6](Br)[#6][#6]1', 0),
+ 'PubChemFP845': ('[#8][#6]1[#6][#6]([#8])[#6][#6]1', 0),
+ 'PubChemFP846': ('[#8][#6]1[#6][#6]([#16])[#6][#6]1', 0),
+ 'PubChemFP847': ('[#8][#6]1[#6][#6]([#7])[#6][#6]1', 0),
+ 'PubChemFP848': ('[#8][#6]1[#6][#6](Cl)[#6][#6]1', 0),
+ 'PubChemFP849': ('[#8][#6]1[#6][#6](Br)[#6][#6]1', 0),
+ 'PubChemFP850': ('[#16][#6]1[#6][#6]([#16])[#6][#6]1', 0),
+ 'PubChemFP851': ('[#16][#6]1[#6][#6]([#7])[#6][#6]1', 0),
+ 'PubChemFP852': ('[#16][#6]1[#6][#6](Cl)[#6][#6]1', 0),
+ 'PubChemFP853': ('[#16][#6]1[#6][#6](Br)[#6][#6]1', 0),
+ 'PubChemFP854': ('[#7][#6]1[#6][#6]([#7])[#6][#6]1', 0),
+ 'PubChemFP855': ('[#7][#6]1[#6][#6](Cl)[#6][#6]1', 0),
+ 'PubChemFP856': ('[#7][#6]1[#6][#6](Br)[#6][#6]1', 0),
+ 'PubChemFP857': ('Cl[#6]1[#6][#6](Cl)[#6][#6]1', 0),
+ 'PubChemFP858': ('Cl[#6]1[#6][#6](Br)[#6][#6]1', 0),
+ 'PubChemFP859': ('Br[#6]1[#6][#6](Br)[#6][#6]1', 0),
+ 'PubChemFP860': ('[#6][#6]1[#6]([#6])[#6][#6][#6]1', 0),
+ 'PubChemFP861': ('[#6][#6]1[#6]([#8])[#6][#6][#6]1', 0),
+ 'PubChemFP862': ('[#6][#6]1[#6]([#16])[#6][#6][#6]1', 0),
+ 'PubChemFP863': ('[#6][#6]1[#6]([#7])[#6][#6][#6]1', 0),
+ 'PubChemFP864': ('[#6][#6]1[#6](Cl)[#6][#6][#6]1', 0),
+ 'PubChemFP865': ('[#6][#6]1[#6](Br)[#6][#6][#6]1', 0),
+ 'PubChemFP866': ('[#8][#6]1[#6]([#8])[#6][#6][#6]1', 0),
+ 'PubChemFP867': ('[#8][#6]1[#6]([#16])[#6][#6][#6]1', 0),
+ 'PubChemFP868': ('[#8][#6]1[#6]([#7])[#6][#6][#6]1', 0),
+ 'PubChemFP869': ('[#8][#6]1[#6](Cl)[#6][#6][#6]1', 0),
+ 'PubChemFP870': ('[#8][#6]1[#6](Br)[#6][#6][#6]1', 0),
+ 'PubChemFP871': ('[#16][#6]1[#6]([#16])[#6][#6][#6]1', 0),
+ 'PubChemFP872': ('[#16][#6]1[#6]([#7])[#6][#6][#6]1', 0),
+ 'PubChemFP873': ('[#16][#6]1[#6](Cl)[#6][#6][#6]1', 0),
+ 'PubChemFP874': ('[#16][#6]1[#6](Br)[#6][#6][#6]1', 0),
+ 'PubChemFP875': ('[#7][#6]1[#6]([#7])[#6][#6][#6]1', 0),
+ 'PubChemFP876': ('[#7][#6]1[#6](Cl)[#6][#6]1', 0),
+ 'PubChemFP877': ('[#7][#6]1[#6](Br)[#6][#6][#6]1', 0),
+ 'PubChemFP878': ('Cl[#6]1[#6](Cl)[#6][#6][#6]1', 0),
+ 'PubChemFP879': ('Cl[#6]1[#6](Br)[#6][#6][#6]1', 0),
+ 'PubChemFP880': ('Br[#6]1[#6](Br)[#6][#6][#6]1', 0)}
diff --git a/deepscreen/data/featurizers/fingerprint/torsions.py b/deepscreen/data/featurizers/fingerprint/torsions.py
new file mode 100644
index 0000000000000000000000000000000000000000..d594832f48b3cc172b7b48a348f4a4e680db8eeb
--- /dev/null
+++ b/deepscreen/data/featurizers/fingerprint/torsions.py
@@ -0,0 +1,18 @@
+from rdkit.Chem.AtomPairs import Torsions
+from rdkit.Chem import DataStructs
+import numpy as np
+
+_type = 'topological-based'
+
+
+def GetTorsionFPs(mol, nBits=2048, binary=True):
+ '''
+ atompairs fingerprints
+ '''
+ fp = Torsions.GetHashedTopologicalTorsionFingerprint(mol, nBits=nBits)
+ if binary:
+ arr = np.zeros((0,), dtype=np.bool_)
+ else:
+ arr = np.zeros((0,), dtype=np.int8)
+ DataStructs.ConvertToNumpyArray(fp, arr)
+ return arr
diff --git a/deepscreen/data/featurizers/graph.py b/deepscreen/data/featurizers/graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..97ac6ffb9fdf06c276e0297d6bebeb2d7ae47260
--- /dev/null
+++ b/deepscreen/data/featurizers/graph.py
@@ -0,0 +1,133 @@
+import networkx as nx
+import numpy as np
+import torch
+from rdkit import Chem
+from torch_geometric.utils import from_smiles
+from torch_geometric.data import Data
+
+from deepscreen.data.featurizers.categorical import one_of_k_encoding_unk, one_of_k_encoding
+from deepscreen.utils import get_logger
+
+log = get_logger(__name__)
+
+
+def atom_features(atom, explicit_H=False, use_chirality=True):
+ """
+ Adapted from TransformerCPI 2.0
+ """
+ symbol = ['C', 'N', 'O', 'F', 'P', 'S', 'Cl', 'Br', 'I', 'other'] # 10-dim
+ degree = [0, 1, 2, 3, 4, 5, 6] # 7-dim
+ hybridization_type = [Chem.rdchem.HybridizationType.SP,
+ Chem.rdchem.HybridizationType.SP2,
+ Chem.rdchem.HybridizationType.SP3,
+ Chem.rdchem.HybridizationType.SP3D,
+ Chem.rdchem.HybridizationType.SP3D2,
+ 'other'] # 6-dim
+
+ # 10+7+2+6+1=26
+ results = one_of_k_encoding_unk(atom.GetSymbol(), symbol) + \
+ one_of_k_encoding(atom.GetDegree(), degree) + \
+ [atom.GetFormalCharge(), atom.GetNumRadicalElectrons()] + \
+ one_of_k_encoding_unk(atom.GetHybridization(), hybridization_type) + [atom.GetIsAromatic()]
+
+ # In case of explicit hydrogen(QM8, QM9), avoid calling `GetTotalNumHs`
+ # 26+5=31
+ if not explicit_H:
+ results = results + one_of_k_encoding_unk(atom.GetTotalNumHs(),
+ [0, 1, 2, 3, 4])
+ # 31+3=34
+ if use_chirality:
+ try:
+ results = results + one_of_k_encoding_unk(
+ atom.GetProp('_CIPCode'),
+ ['R', 'S']) + [atom.HasProp('_ChiralityPossible')]
+ except:
+ results = results + [False, False] + [atom.HasProp('_ChiralityPossible')]
+
+ return np.array(results)
+
+
+def bond_features(bond):
+ bt = bond.GetBondType()
+ return np.array(
+ [bt == Chem.rdchem.BondType.SINGLE, bt == Chem.rdchem.BondType.DOUBLE, bt == Chem.rdchem.BondType.TRIPLE,
+ bt == Chem.rdchem.BondType.AROMATIC, bond.GetIsConjugated(), bond.IsInRing()])
+
+
+def smiles_to_graph_pyg(smiles):
+ """
+ Convert SMILES to graph with the default method defined by PyTorch Geometric
+ """
+ try:
+ return from_smiles(smiles)
+ except Exception as e:
+ log.warning(f"Failed to featurize the following SMILES to graph: {smiles} due to {str(e)}")
+ return None
+
+
+def smiles_to_graph(smiles, atom_features: callable = atom_features):
+ """
+ Convert SMILES to graph with custom atom_features
+ """
+ try:
+ mol = Chem.MolFromSmiles(smiles)
+
+ features = []
+ for atom in mol.GetAtoms():
+ feature = atom_features(atom)
+ features.append(feature / sum(feature))
+ features = np.array(features)
+
+ edges = []
+ for bond in mol.GetBonds():
+ edges.append([bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()])
+ g = nx.Graph(edges).to_directed()
+
+ if len(edges) == 0:
+ edge_index = [[0, 0]]
+ else:
+ edge_index = []
+ for e1, e2 in g.edges:
+ edge_index.append([e1, e2])
+
+ return Data(x=torch.Tensor(features),
+ edge_index=torch.LongTensor(edge_index).transpose(0, 1))
+
+ except Exception as e:
+ log.warning(f"Failed to convert SMILES ({smiles}) to graph due to {str(e)}")
+ return None
+ # features = []
+ # for atom in mol.GetAtoms():
+ # feature = atom_features(atom)
+ # features.append(feature / sum(feature))
+ #
+ # edge_indices = []
+ # for bond in mol.GetBonds():
+ # i = bond.GetBeginAtomIdx()
+ # j = bond.GetEndAtomIdx()
+ # edge_indices += [[i, j], [j, i]]
+ #
+ # edge_index = torch.tensor(edge_indices)
+ # edge_index = edge_index.t().to(torch.long).view(2, -1)
+ #
+ # if edge_index.numel() > 0: # Sort indices.
+ # perm = (edge_index[0] * x.size(0) + edge_index[1]).argsort()
+ # edge_index = edge_index[:, perm]
+ #
+
+
+def smiles_to_mol_features(smiles, num_atom_feat: callable):
+ try:
+ mol = Chem.MolFromSmiles(smiles)
+ num_atom_feat = len(atom_features(mol.GetAtoms()[0]))
+ atom_feat = np.zeros((mol.GetNumAtoms(), num_atom_feat))
+ for atom in mol.GetAtoms():
+ atom_feat[atom.GetIdx(), :] = atom_features(atom)
+ adj = Chem.GetAdjacencyMatrix(mol)
+ adj_mat = np.array(adj)
+
+ return atom_feat, adj_mat
+
+ except Exception as e:
+ log.warning(f"Failed to featurize the following SMILES to molecular features: {smiles} due to {str(e)}")
+ return None
\ No newline at end of file
diff --git a/deepscreen/data/featurizers/monn.py b/deepscreen/data/featurizers/monn.py
new file mode 100644
index 0000000000000000000000000000000000000000..81a9cce604c701b6f8099c773ef7ff236eab810a
--- /dev/null
+++ b/deepscreen/data/featurizers/monn.py
@@ -0,0 +1,106 @@
+import numpy as np
+from rdkit.Chem import MolFromSmiles
+
+from deepscreen.data.featurizers.categorical import FASTA_CHARSET, fasta_to_label
+from deepscreen.data.featurizers.graph import atom_features, bond_features
+
+
+def get_mask(arr):
+ a = np.zeros(1, len(arr))
+ a[1, :arr.shape[0]] = 1
+ return a
+
+
+def add_index(input_array, ebd_size):
+ batch_size, n_vertex, n_nbs = np.shape(input_array)
+ add_idx = np.array(range(0, ebd_size * batch_size, ebd_size) * (n_nbs * n_vertex))
+ add_idx = np.transpose(add_idx.reshape(-1, batch_size))
+ add_idx = add_idx.reshape(-1)
+ new_array = input_array.reshape(-1) + add_idx
+ return new_array
+
+
+# TODO fix padding and masking
+def drug_featurizer(smiles, max_neighbors=6):
+ mol = MolFromSmiles(smiles)
+
+ # convert molecule to GNN input
+ n_atoms = mol.GetNumAtoms()
+ assert mol.GetNumBonds() >= 0
+
+ n_bonds = max(mol.GetNumBonds(), 1)
+ feat_atoms = np.zeros((n_atoms,)) # atom feature ID
+ feat_bonds = np.zeros((n_bonds,)) # bond feature ID
+ atom_adj = np.zeros((n_atoms, max_neighbors))
+ bond_adj = np.zeros((n_atoms, max_neighbors))
+ n_neighbors = np.zeros((n_atoms,))
+ neighbor_mask = np.zeros((n_atoms, max_neighbors))
+
+ for atom in mol.GetAtoms():
+ idx = atom.GetIdx()
+ feat_atoms[idx] = atom_features(atom)
+
+ for bond in mol.GetBonds():
+ a1 = bond.GetBeginAtom().GetIdx()
+ a2 = bond.GetEndAtom().GetIdx()
+ idx = bond.GetIdx()
+ feat_bonds[idx] = bond_features(bond)
+ try:
+ atom_adj[a1, n_neighbors[a1]] = a2
+ atom_adj[a2, n_neighbors[a2]] = a1
+ except:
+ return [], [], [], [], []
+ bond_adj[a1, n_neighbors[a1]] = idx
+ bond_adj[a2, n_neighbors[a2]] = idx
+ n_neighbors[a1] += 1
+ n_neighbors[a2] += 1
+
+ for i in range(len(n_neighbors)):
+ neighbor_mask[i, :n_neighbors[i]] = 1
+
+ vertex_mask = get_mask(feat_atoms)
+ # vertex = pack_1d(feat_atoms)
+ # edge = pack_1d(feat_bonds)
+ # atom_adj = pack_2d(atom_adj)
+ # bond_adj = pack_2d(bond_adj)
+ # nbs_mask = pack_2d(n_neighbors_mat)
+
+ atom_adj = add_index(atom_adj, np.shape(atom_adj)[1])
+ bond_adj = add_index(bond_adj, np.shape(feat_bonds)[1])
+
+ return vertex_mask, feat_atoms, feat_bonds, atom_adj, bond_adj, neighbor_mask
+
+
+# TODO WIP the pairwise_label matrix probably should be generated beforehand and stored as an extra label in the dataset
+def get_pairwise_label(pdbid, interaction_dict, mol):
+ if pdbid in interaction_dict:
+ sdf_element = np.array([atom.GetSymbol().upper() for atom in mol.GetAtoms()])
+ atom_element = np.array(interaction_dict[pdbid]['atom_element'], dtype=str)
+ atom_name_list = np.array(interaction_dict[pdbid]['atom_name'], dtype=str)
+ atom_interact = np.array(interaction_dict[pdbid]['atom_interact'], dtype=int)
+ nonH_position = np.where(atom_element != 'H')[0]
+ assert sum(atom_element[nonH_position] != sdf_element) == 0
+
+ atom_name_list = atom_name_list[nonH_position].tolist()
+ pairwise_mat = np.zeros((len(nonH_position), len(interaction_dict[pdbid]['uniprot_seq'])), dtype=np.int32)
+ for atom_name, bond_type in interaction_dict[pdbid]['atom_bond_type']:
+ atom_idx = atom_name_list.index(str(atom_name))
+ assert atom_idx < len(nonH_position)
+
+ seq_idx_list = []
+ for seq_idx, bond_type_seq in interaction_dict[pdbid]['residue_bond_type']:
+ if bond_type == bond_type_seq:
+ seq_idx_list.append(seq_idx)
+ pairwise_mat[atom_idx, seq_idx] = 1
+ if len(np.where(pairwise_mat != 0)[0]) != 0:
+ pairwise_mask = True
+ return True, pairwise_mat
+ return False, np.zeros((1, 1))
+
+
+def protein_featurizer(fasta):
+ sequence = fasta_to_label(fasta)
+ # pad proteins and make masks
+ seq_mask = get_mask(sequence)
+
+ return seq_mask, sequence
diff --git a/deepscreen/data/featurizers/token.py b/deepscreen/data/featurizers/token.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7a62778a33a811ab31aec0122d389486aca30f0
--- /dev/null
+++ b/deepscreen/data/featurizers/token.py
@@ -0,0 +1,295 @@
+import collections
+from importlib import resources
+import os
+import re
+from typing import Optional, List
+
+import numpy as np
+from transformers import BertTokenizer
+
+SMI_REGEX_PATTERN = r"""(
+ \[[^\]]+\] # match anything inside square brackets
+ |Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p # match elements
+ |\(|\) # match parentheses
+ |\.|=|#|-|\+|\\|\/|:|~|@|\?|>>?|\*|\$|\%[0-9]{2} # match various symbols
+ |[0-9] # match digits
+)"""
+
+
+def sequence_to_kmers(sequence, k=3):
+ """ Divide a string into a list of kmers strings.
+
+ Parameters:
+ sequence (string)
+ k (int), default 3
+ Returns:
+ List containing a list of kmers.
+ """
+ return [sequence[i:i + k] for i in range(len(sequence) - k + 1)]
+
+
+def sequence_to_word_embedding(sequence, model):
+ """Get protein embedding, infer a list of 3-mers to (num_word, 100) matrix"""
+ vec = np.zeros((len(sequence), 100))
+ i = 0
+ for word in sequence:
+ vec[i,] = model.wv[word]
+ i += 1
+ return vec
+
+
+def sequence_to_token_ids(sequence, tokenizer):
+ token_ids = tokenizer.encode(sequence)
+ return token_ids
+
+
+# def sequence_to_token_ids(sequence, tokenizer, max_length: int):
+# token_ids = tokenizer.encode(sequence)
+# length = min(max_length, len(token_ids))
+#
+# token_ids_padded = np.zeros(max_length, dtype='int')
+# token_ids_padded[:length] = token_ids[:length]
+#
+# return token_ids_padded
+
+
+class SmilesTokenizer(BertTokenizer):
+ """
+ Adapted from https://github.com/deepchem/deepchem/.
+
+ Creates the SmilesTokenizer class. The tokenizer heavily inherits from the BertTokenizer
+ implementation found in Huggingface's transformers library. It runs a WordPiece tokenization
+ algorithm over SMILES strings using the tokenisation SMILES regex developed by Schwaller et. al.
+
+ Please see https://github.com/huggingface/transformers
+ and https://github.com/rxn4chemistry/rxnfp for more details.
+
+ Examples
+ --------
+ >>> tokenizer = SmilesTokenizer(vocab_path)
+ >>> print(tokenizer.encode("CC(=O)OC1=CC=CC=C1C(=O)O"))
+ [12, 16, 16, 17, 22, 19, 18, 19, 16, 20, 22, 16, 16, 22, 16, 16, 22, 16, 20, 16, 17, 22, 19, 18, 19, 13]
+
+
+ References
+ ----------
+ .. [1] Schwaller, Philippe; Probst, Daniel; Vaucher, Alain C.; Nair, Vishnu H; Kreutter, David;
+ Laino, Teodoro; et al. (2019): Mapping the Space of Chemical Reactions using Attention-Based Neural
+ Networks. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.9897365.v3
+
+ Note
+ ----
+ This class requires huggingface's transformers and tokenizers libraries to be installed.
+ """
+ def __init__(
+ self,
+ vocab_file: str = '',
+ regex_pattern: str = SMI_REGEX_PATTERN,
+ # unk_token="[UNK]",
+ # sep_token="[SEP]",
+ # pad_token="[PAD]",
+ # cls_token="[CLS]",
+ # mask_token="[MASK]",
+ **kwargs):
+ """Constructs a SmilesTokenizer.
+
+ Parameters
+ ----------
+ vocab_file: str
+ Path to a SMILES character per line vocabulary file.
+ Default vocab file is found in deepchem/feat/tests/data/vocab.txt
+ """
+
+ super().__init__(vocab_file, **kwargs)
+
+ if not os.path.isfile(vocab_file):
+ raise ValueError(
+ "Can't find a vocab file at path '{}'.".format(vocab_file))
+ self.vocab = load_vocab(vocab_file)
+ unused_indexes = [i for i, v in enumerate(self.vocab.keys()) if v.startswith("[unused")]
+ self.highest_unused_index = 0 if len(unused_indexes) == 0 else max(unused_indexes)
+ self.ids_to_tokens = collections.OrderedDict([
+ (ids, tok) for tok, ids in self.vocab.items()
+ ])
+ self.basic_tokenizer = BasicSmilesTokenizer(regex_pattern=regex_pattern)
+
+ @property
+ def vocab_size(self):
+ return len(self.vocab)
+
+ @property
+ def vocab_list(self):
+ return list(self.vocab.keys())
+
+ def _tokenize(self, text: str, max_seq_length: int = 512, **kwargs):
+ """Tokenize a string into a list of tokens.
+
+ Parameters
+ ----------
+ text: str
+ Input string sequence to be tokenized.
+ """
+
+ max_len_single_sentence = max_seq_length - 2
+ split_tokens = [
+ token for token in self.basic_tokenizer.tokenize(text)
+ [:max_len_single_sentence]
+ ]
+ return split_tokens
+
+ def _convert_token_to_id(self, token: str):
+ """Converts a token (str/unicode) in an id using the vocab.
+
+ Parameters
+ ----------
+ token: str
+ String token from a larger sequence to be converted to a numerical id.
+ """
+
+ return self.vocab.get(token, self.vocab.get(self.unk_token))
+
+ def _convert_id_to_token(self, index: int):
+ """Converts an index (integer) in a token (string/unicode) using the vocab.
+
+ Parameters
+ ----------
+ index: int
+ Integer index to be converted back to a string-based token as part of a larger sequence.
+ """
+
+ return self.ids_to_tokens.get(index, self.unk_token)
+
+ def convert_tokens_to_string(self, tokens: List[str]):
+ """Converts a sequence of tokens (string) in a single string.
+
+ Parameters
+ ----------
+ tokens: List[str]
+ List of tokens for a given string sequence.
+
+ Returns
+ -------
+ out_string: str
+ Single string from combined tokens.
+ """
+
+ out_string: str = " ".join(tokens).replace(" ##", "").strip()
+ return out_string
+
+ def add_special_tokens_ids_single_sequence(self,
+ token_ids: List[Optional[int]]):
+ """Adds special tokens to a sequence for sequence classification tasks.
+
+ A BERT sequence has the following format: [CLS] X [SEP]
+
+ Parameters
+ ----------
+ token_ids: list[int]
+ list of tokenized input ids. Can be obtained using the encode or encode_plus methods.
+ """
+
+ return [self.cls_token_id] + token_ids + [self.sep_token_id]
+
+ def add_special_tokens_single_sequence(self, tokens: List[str]):
+ """Adds special tokens to the a sequence for sequence classification tasks.
+ A BERT sequence has the following format: [CLS] X [SEP]
+
+ Parameters
+ ----------
+ tokens: List[str]
+ List of tokens for a given string sequence.
+ """
+ return [self.cls_token] + tokens + [self.sep_token]
+
+ def add_special_tokens_ids_sequence_pair(
+ self, token_ids_0: List[Optional[int]],
+ token_ids_1: List[Optional[int]]) -> List[Optional[int]]:
+ """Adds special tokens to a sequence pair for sequence classification tasks.
+ A BERT sequence pair has the following format: [CLS] A [SEP] B [SEP]
+
+ Parameters
+ ----------
+ token_ids_0: List[int]
+ List of ids for the first string sequence in the sequence pair (A).
+ token_ids_1: List[int]
+ List of tokens for the second string sequence in the sequence pair (B).
+ """
+
+ sep = [self.sep_token_id]
+ cls = [self.cls_token_id]
+
+ return cls + token_ids_0 + sep + token_ids_1 + sep
+
+ def add_padding_tokens(self,
+ token_ids: List[Optional[int]],
+ length: int,
+ right: bool = True) -> List[Optional[int]]:
+ """Adds padding tokens to return a sequence of length max_length.
+ By default padding tokens are added to the right of the sequence.
+
+ Parameters
+ ----------
+ token_ids: list[optional[int]]
+ list of tokenized input ids. Can be obtained using the encode or encode_plus methods.
+ length: int
+ right: bool, default True
+
+ Returns
+ -------
+ List[int]
+ """
+ padding = [self.pad_token_id] * (length - len(token_ids))
+
+ if right:
+ return token_ids + padding
+ else:
+ return padding + token_ids
+
+
+class BasicSmilesTokenizer(object):
+ """
+ Adapted from https://github.com/deepchem/deepchem/.
+ Run basic SMILES tokenization using a regex pattern developed by Schwaller et. al.
+ This tokenizer is to be used when a tokenizer that does not require the transformers library by HuggingFace is required.
+
+ Examples
+ --------
+ >>> tokenizer = BasicSmilesTokenizer()
+ >>> print(tokenizer.tokenize("CC(=O)OC1=CC=CC=C1C(=O)O"))
+ ['C', 'C', '(', '=', 'O', ')', 'O', 'C', '1', '=', 'C', 'C', '=', 'C', 'C', '=', 'C', '1', 'C', '(', '=', 'O', ')', 'O']
+
+
+ References
+ ----------
+ .. [1] Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A. Hunter, Costas Bekas, and Alpha A. Lee
+ ACS Central Science 2019 5 (9): Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction
+ 1572-1583 DOI: 10.1021/acscentsci.9b00576
+ """
+
+ def __init__(self, regex_pattern: str = SMI_REGEX_PATTERN):
+ """Constructs a BasicSMILESTokenizer.
+
+ Parameters
+ ----------
+ regex: string
+ SMILES token regex
+ """
+ self.regex_pattern = regex_pattern
+ self.regex = re.compile(self.regex_pattern)
+
+ def tokenize(self, text):
+ """Basic Tokenization of a SMILES.
+ """
+ tokens = [token for token in self.regex.findall(text)]
+ return tokens
+
+
+def load_vocab(vocab_file):
+ """Loads a vocabulary file into a dictionary."""
+ vocab = collections.OrderedDict()
+ with open(vocab_file, "r", encoding="utf-8") as reader:
+ tokens = reader.readlines()
+ for index, token in enumerate(tokens):
+ token = token.rstrip("\n")
+ vocab[token] = index
+ return vocab
diff --git a/deepscreen/data/single_entity.py b/deepscreen/data/single_entity.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef4195fdd988b44f9712b3a426b2bb4b2857a6d0
--- /dev/null
+++ b/deepscreen/data/single_entity.py
@@ -0,0 +1,195 @@
+# from itertools import product
+from numbers import Number
+from pathlib import Path
+from typing import Any, Dict, Optional, Sequence, Union, Literal
+
+# import numpy as np
+import pandas as pd
+from lightning import LightningDataModule
+from sklearn.base import TransformerMixin
+from torch.utils.data import Dataset, DataLoader, random_split
+
+from deepscreen.data.utils.dataset import SingleEntitySingleTargetDataset, BaseEntityDataset
+from deepscreen.data.utils.label import label_transform
+from deepscreen.data.utils.collator import collate_fn
+from deepscreen.data.utils.sampler import SafeBatchSampler
+
+
+class EntityDataModule(LightningDataModule):
+ """
+ DTI DataModule
+
+ A DataModule implements 5 key methods:
+
+ def prepare_data(self):
+ # things to do on 1 GPU/TPU (not on every GPU/TPU in DDP)
+ # download data, pre-process, split, save to disk, etc.
+ def setup(self, stage):
+ # things to do on every process in DDP
+ # load data, set variables, etc.
+ def train_dataloader(self):
+ # return train dataloader
+ def val_dataloader(self):
+ # return validation dataloader
+ def test_dataloader(self):
+ # return test dataloader
+ def teardown(self):
+ # called on every process in DDP
+ # clean up after fit or test
+
+ This allows you to share a full dataset without explaining how to download,
+ split, transform and process the data.
+
+ Read the docs:
+ https://pytorch-lightning.readthedocs.io/en/latest/extensions/datamodules.html
+ """
+
+ def __init__(
+ self,
+ dataset: type[BaseEntityDataset],
+ task: Literal['regression', 'binary', 'multiclass'],
+ n_classes: Optional[int],
+ train: bool,
+ batch_size: int,
+ num_workers: int = 0,
+ thresholds: Optional[Union[Number, Sequence[Number]]] = None,
+ pin_memory: bool = False,
+ data_dir: str = "data/",
+ data_file: Optional[str] = None,
+ train_val_test_split: Optional[Sequence[Number], Sequence[str]] = None,
+ split: Optional[callable] = random_split,
+ ):
+ super().__init__()
+ data_path = Path(data_dir) / data_file
+ # this line allows to access init params with 'self.hparams' attribute
+ # also ensures init params will be stored in ckpt
+ self.save_hyperparameters(logger=False)
+
+ # data processing
+ self.split = split
+
+ if train:
+ if all([data_file, split]):
+ if all(isinstance(split, Number) for split in train_val_test_split):
+ pass
+ else:
+ raise ValueError('`train_val_test_split` must be a sequence of 3 numbers '
+ '(float for percentages and int for sample numbers) if '
+ '`data_file` and `split` have been specified.')
+ elif all(isinstance(split, str) for split in train_val_test_split) and not any([data_file, split]):
+ self.train_data = dataset(dataset_path=str(Path(data_dir) / train_val_test_split[0]))
+ self.val_data = dataset(dataset_path=str(Path(data_dir) / train_val_test_split[1]))
+ self.test_data = dataset(dataset_path=str(Path(data_dir) / train_val_test_split[2]))
+ else:
+ raise ValueError('For training (train=True), you must specify either '
+ '`dataset_name` and `split` with `train_val_test_split` of 3 numbers or '
+ 'solely `train_val_test_split` of 3 data file names.')
+ else:
+ if data_file and not any([split, train_val_test_split]):
+ self.test_data = self.predict_data = dataset(dataset_path=str(Path(data_dir) / data_file))
+ else:
+ raise ValueError("For testing/predicting (train=False), you must specify only `data_file` without "
+ "`train_val_test_split` or `split`")
+
+ def prepare_data(self):
+ """
+ Download data if needed.
+ Do not use it to assign state (e.g., self.x = x).
+ """
+
+ def setup(self, stage: Optional[str] = None, encoding: str = None):
+ """
+ Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`.
+ This method is called by lightning with both `trainer.fit()` and `trainer.test()`, so be
+ careful not to execute data splitting twice.
+ """
+ # load and split datasets only if not loaded in initialization
+ if not any([self.data_train, self.data_val, self.data_test, self.data_predict]):
+ dataset = SingleEntitySingleTargetDataset(
+ task=self.hparams.task,
+ n_classes=self.hparams.n_classes,
+ dataset_path=Path(self.hparams.data_dir) / self.hparams.dataset_name,
+ transformer=self.hparams.transformer,
+ featurizer=self.hparams.featurizer,
+ thresholds=self.hparams.thresholds,
+ )
+
+ if self.hparams.train:
+ self.data_train, self.data_val, self.data_test = self.split(
+ dataset=dataset,
+ lengths=self.hparams.train_val_test_split
+ )
+ else:
+ self.data_test = self.data_predict = dataset
+
+ def train_dataloader(self):
+ return DataLoader(
+ dataset=self.data_train,
+ batch_sampler=SafeBatchSampler(
+ data_source=self.data_train,
+ batch_size=self.hparams.batch_size,
+ shuffle=True),
+ # batch_size=self.hparams.batch_size,
+ # shuffle=True,
+ num_workers=self.hparams.num_workers,
+ pin_memory=self.hparams.pin_memory,
+ collate_fn=collate_fn,
+ persistent_workers=True if self.hparams.num_workers > 0 else False
+ )
+
+ def val_dataloader(self):
+ return DataLoader(
+ dataset=self.data_val,
+ batch_sampler=SafeBatchSampler(
+ data_source=self.data_val,
+ batch_size=self.hparams.batch_size,
+ shuffle=False),
+ # batch_size=self.hparams.batch_size,
+ # shuffle=False,
+ num_workers=self.hparams.num_workers,
+ pin_memory=self.hparams.pin_memory,
+ collate_fn=collate_fn,
+ persistent_workers=True if self.hparams.num_workers > 0 else False
+ )
+
+ def test_dataloader(self):
+ return DataLoader(
+ dataset=self.data_test,
+ batch_sampler=SafeBatchSampler(
+ data_source=self.data_test,
+ batch_size=self.hparams.batch_size,
+ shuffle=False),
+ # batch_size=self.hparams.batch_size,
+ # shuffle=False,
+ num_workers=self.hparams.num_workers,
+ pin_memory=self.hparams.pin_memory,
+ collate_fn=collate_fn,
+ persistent_workers=True if self.hparams.num_workers > 0 else False
+ )
+
+ def predict_dataloader(self):
+ return DataLoader(
+ dataset=self.data_predict,
+ batch_sampler=SafeBatchSampler(
+ data_source=self.data_predict,
+ batch_size=self.hparams.batch_size,
+ shuffle=False),
+ # batch_size=self.hparams.batch_size,
+ # shuffle=False,
+ num_workers=self.hparams.num_workers,
+ pin_memory=self.hparams.pin_memory,
+ collate_fn=collate_fn,
+ persistent_workers=True if self.hparams.num_workers > 0 else False
+ )
+
+ def teardown(self, stage: Optional[str] = None):
+ """Clean up after fit or test."""
+ pass
+
+ def state_dict(self):
+ """Extra things to save to checkpoint."""
+ return {}
+
+ def load_state_dict(self, state_dict: Dict[str, Any]):
+ """Things to do when loading checkpoint."""
+ pass
diff --git a/deepscreen/data/utils/__init__.py b/deepscreen/data/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..80edbecbbd91c4b36749186b2f72069c4918d949
--- /dev/null
+++ b/deepscreen/data/utils/__init__.py
@@ -0,0 +1,8 @@
+from typing import Dict, Sequence, TypeVar, Union
+
+from deepscreen.data.utils.collator import collate_fn
+from deepscreen.data.utils.label import label_transform
+from deepscreen.data.utils.sampler import SafeBatchSampler
+
+T = TypeVar('T')
+FlexibleIterable = Union[T, Sequence[T], Dict[str, T]]
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diff --git a/deepscreen/data/utils/collator.py b/deepscreen/data/utils/collator.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdc661250a63789dd17df092a7203c4655907c61
--- /dev/null
+++ b/deepscreen/data/utils/collator.py
@@ -0,0 +1,117 @@
+"""
+Define collate functions for new data types here
+"""
+from functools import partial
+from itertools import chain
+
+import dgl
+import torch
+from torch.nn.utils.rnn import pad_sequence
+from torch.utils.data._utils.collate import default_collate_fn_map, collate_tensor_fn, collate
+import torch_geometric
+
+
+def collate_pyg_fn(batch, collate_fn_map=None):
+ """
+ PyG graph collation
+ """
+ return torch_geometric.data.Batch.from_data_list(batch)
+
+
+def collate_dgl_fn(batch, collate_fn_map=None):
+ """
+ DGL graph collation
+ """
+ return dgl.batch(batch)
+
+
+def pad_collate_tensor_fn(batch, padding_value=0.0, collate_fn_map=None):
+ """
+ Similar to pad_packed_sequence(pack_sequence(batch, enforce_sorted=False), batch_first=True),
+ but additionally supports padding a list of square Tensors of size ``(L x L x ...)``.
+ :param batch:
+ :param padding_value:
+ :param collate_fn_map:
+ :return: padded_batch, lengths
+ """
+ lengths = [tensor.size(0) for tensor in batch]
+ if any(element != lengths[0] for element in lengths[1:]):
+ try:
+ # Tensors share at least one common dimension size, use pad_sequence
+ batch = pad_sequence(batch, batch_first=True, padding_value=padding_value)
+ except RuntimeError:
+ # Tensors do not share any common dimension size, find the max size of each dimension in the batch
+ max_sizes = [max([tensor.size(dim) for tensor in batch]) for dim in range(batch[0].dim())]
+ # Pad every dimension of all tensors in the batch to be the respective max size with the value
+ batch = collate_tensor_fn([
+ torch.nn.functional.pad(
+ tensor, tuple(chain.from_iterable(
+ [(0, max_sizes[dim] - tensor.size(dim)) for dim in range(tensor.dim())][::-1])
+ ), mode='constant', value=padding_value) for tensor in batch
+ ])
+ else:
+ batch = collate_tensor_fn(batch)
+
+ lengths = torch.as_tensor(lengths)
+ # Return the padded batch tensor and the lengths
+ return batch, lengths
+
+
+# Join custom collate functions with the default collation map of PyTorch
+COLLATE_FN_MAP = default_collate_fn_map | {
+ torch_geometric.data.data.BaseData: collate_pyg_fn,
+ dgl.DGLGraph: collate_dgl_fn,
+}
+
+
+def collate_fn(batch, automatic_padding=False, padding_value=0):
+ if automatic_padding:
+ COLLATE_FN_MAP.update({
+ torch.Tensor: partial(pad_collate_tensor_fn, padding_value=padding_value),
+ })
+ return collate(batch, collate_fn_map=COLLATE_FN_MAP)
+
+
+class VariableLengthSequence(torch.Tensor):
+ """
+ A custom PyTorch Tensor class that is similar to PackedSequence, except it can be directly used as a batch tensor,
+ and it has an attribute called lengths, which signifies the length of each original sequence in the batch.
+ """
+
+ def __new__(cls, data, lengths):
+ """
+ Creates a new VariableLengthSequence object from the given data and lengths.
+ Args:
+ data (torch.Tensor): The batch collated tensor of shape (batch_size, max_length, *).
+ lengths (torch.Tensor): The lengths of each original sequence in the batch of shape (batch_size,).
+ Returns:
+ VariableLengthSequence: A new VariableLengthSequence object.
+ """
+ # Check the validity of the inputs
+ assert isinstance(data, torch.Tensor), "data must be a torch.Tensor"
+ assert isinstance(lengths, torch.Tensor), "lengths must be a torch.Tensor"
+ assert data.dim() >= 2, "data must have at least two dimensions"
+ assert lengths.dim() == 1, "lengths must have one dimension"
+ assert data.size(0) == lengths.size(0), "data and lengths must have the same batch size"
+ assert lengths.min() > 0, "lengths must be positive"
+ assert lengths.max() <= data.size(1), "lengths must not exceed the max length of data"
+
+ # Create a new tensor object from data
+ obj = super().__new__(cls, data)
+
+ # Set the lengths attribute
+ obj.lengths = lengths
+
+ return obj
+
+ def __repr__(self, *, tensor_contents=None):
+ """
+ Returns a string representation of the VariableLengthSequence object.
+ """
+ return f"VariableLengthSequence(data={self.data}, lengths={self.lengths})"
+
+ def __reduce_ex__(self, proto):
+ """
+ Enables pickling of the VariableLengthSequence object.
+ """
+ return type(self), (self.data, self.lengths)
diff --git a/deepscreen/data/utils/dataset.py b/deepscreen/data/utils/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..12af30f77dfe9d9d21432d7f424895cd27bcc446
--- /dev/null
+++ b/deepscreen/data/utils/dataset.py
@@ -0,0 +1,216 @@
+from numbers import Number
+from typing import Literal, Union, Sequence
+
+import pandas as pd
+from sklearn.base import TransformerMixin
+from sklearn.exceptions import NotFittedError
+from sklearn.utils.validation import check_is_fitted
+from torch.utils.data import Dataset
+
+from deepscreen.data.utils import label_transform, FlexibleIterable
+
+
+class BaseEntityDataset(Dataset):
+ def __init__(
+ self,
+ dataset_path: str,
+ use_col_prefixes=('X', 'Y', 'ID', 'U')
+ ):
+
+ # Read the data table header row first to filter columns and create column dtype dict
+ df = pd.read_csv(
+ dataset_path,
+ header=0, nrows=0,
+ usecols=lambda col: col.startswith(use_col_prefixes)
+ )
+ # Read the whole data table
+ df = pd.read_csv(
+ dataset_path,
+ header=0,
+ usecols=df.columns,
+ dtype={col: 'float32' if col.startswith('Y') else 'string' for col in df.columns}
+ )
+
+ self.df = df
+ self.label_cols = [col for col in df.columns if col.startswith('Y')]
+ self.label_unit_cols = [col for col in df.columns if col.startswith('U')]
+ self.entity_id_cols = [col for col in df.columns if col.startswith('ID')]
+ self.entity_cols = [col for col in df.columns if col.startswith('X')]
+
+ def __len__(self):
+ return len(self.df.index)
+
+ def __getitem__(self, idx):
+ raise NotImplementedError
+
+
+# TODO test transform
+class SingleEntitySingleTargetDataset(BaseEntityDataset):
+ def __init__(
+ self,
+ dataset_path: str,
+ task: Literal['regression', 'binary', 'multiclass'],
+ n_classes: int,
+ featurizer: callable,
+ transformer: TransformerMixin = None,
+ thresholds: Union[Number, Sequence[Number]] = None,
+ discard_intermediate: bool = None,
+ forward_fill: bool = True
+ ):
+ super().__init__(dataset_path)
+
+ assert len(self.entity_cols) == 1, 'The dataset contains more than 1 entity column (starting with `X`).'
+ if len(self.label_cols) >= 0:
+ assert len(self.label_cols) == 1, 'The dataset contains more than 1 label column (starting with `Y`).'
+ # Remove trailing `1`s in column names for flexibility
+ self.df.columns = self.df.columns.str.rstrip('1')
+
+ # Forward-fill non-label columns
+ nonlabel_cols = self.label_unit_cols + self.entity_id_cols + self.entity_cols
+ if forward_fill:
+ self.df[nonlabel_cols] = self.df[nonlabel_cols].ffill(axis=0)
+
+ # Process target labels for training/testing if exist
+ if self.label_cols:
+ # Transform target labels
+ self.df[self.label_cols] = self.df[self.label_cols].apply(
+ label_transform,
+ units=self.df.get('U', None),
+ thresholds=thresholds,
+ discard_intermediate=discard_intermediate).astype('float32')
+
+ # Filter out rows with a NaN in Y (missing values); use inplace to save memory
+ self.df.dropna(subset=self.label_cols, inplace=True)
+
+ # Validate target labels
+ # TODO: check sklearn.utils.multiclass.check_classification_targets
+ match task:
+ case 'regression':
+ assert all(self.df['Y'].apply(lambda x: isinstance(x, Number))), \
+ f"Y for task `regression` must be numeric; got {set(self.df['Y'].apply(type))}."
+ case 'binary':
+ assert all(self.df['Y'].isin([0, 1])), \
+ f"Y for task `binary` (classification) must be 0 or 1, but Y got {pd.unique(self.df['Y'])}." \
+ "\nYou may set `thresholds` to discretize continuous labels."
+ case 'multiclass':
+ assert n_classes >= 3, f'n_classes for task `multiclass` (classification) must be at least 3.'
+ assert all(self.df['Y'].apply(lambda x: x.is_integer() and x >= 0)), \
+ f"``Y` for task `multiclass` (classification) must be non-negative integers, " \
+ f"but `Y` got {pd.unique(self.df['Y'])}." \
+ "\nYou may set `thresholds` to discretize continuous labels."
+ target_n_unique = self.df['Y'].nunique()
+ assert target_n_unique == n_classes, \
+ f"You have set n_classes for task `multiclass` (classification) task to {n_classes}, " \
+ f"but `Y` has {target_n_unique} unique labels."
+
+ if transformer:
+ self.df['X'] = self.df['X'].apply(featurizer)
+ try:
+ check_is_fitted(transformer)
+ self.df['X'] = list(transformer.transform(self.df['X']))
+ except NotFittedError:
+ self.df['X'] = list(transformer.fit_transform(self.df['X']))
+
+ # Skip sample-wise feature extraction because it has already been done dataset-wise
+ self.featurizer = lambda x: x
+
+ self.featurizer = featurizer
+ self.n_classes = n_classes
+ self.df['ID'] = self.df.get('ID', self.df['X'])
+
+ def __getitem__(self, idx):
+ sample = self.df.loc[idx]
+ return {
+ 'X': self.featurizer(sample['X']),
+ 'ID': sample['ID'],
+ 'Y': sample.get('Y')
+ }
+
+
+# TODO WIP
+class MultiEntityMultiTargetDataset(BaseEntityDataset):
+ def __init__(
+ self,
+ dataset_path: str,
+ task: FlexibleIterable[Literal['regression', 'binary', 'multiclass']],
+ n_class: FlexibleIterable[int],
+ featurizers: FlexibleIterable[callable],
+ thresholds: FlexibleIterable[Union[Number, Sequence[Number]]] = None,
+ discard_intermediate: FlexibleIterable[bool] = None,
+ ):
+ super().__init__(dataset_path)
+ label_col_prefix = tuple('Y')
+ nonlabel_col_prefixes = tuple(('X', 'ID', 'U'))
+ allowed_col_prefixes = label_col_prefix + nonlabel_col_prefixes
+
+ # Read the headers first to filter columns and create column dtype dict
+ df = pd.read_csv(
+ dataset_path,
+ header=0, nrows=0,
+ usecols=lambda col: col.startswith(allowed_col_prefixes)
+ )
+
+ # Read the whole table
+ df = pd.read_csv(
+ dataset_path,
+ header=0,
+ usecols=df.columns,
+ dtype={col: 'float32' if col.startswith('Y') else 'string' for col in df.columns}
+ )
+ label_cols = [col for col in df.columns if col.startswith(label_col_prefix)]
+ nonlabel_cols = [col for col in df.columns if col.startswith(nonlabel_col_prefixes)]
+ self.entity_cols = [col for col in nonlabel_cols if col.startswith('X')]
+
+ # Forward-fill all non-label columns
+ df[nonlabel_cols] = df[nonlabel_cols].ffill(axis=0)
+
+ # Process target labels for training/testing
+ if label_cols:
+ # Transform target labels
+ df[label_cols] = df[label_cols].apply(label_transform, units=df.get('U', None), thresholds=thresholds,
+ discard_intermediate=discard_intermediate).astype('float32')
+
+ # Filter out rows with a NaN in Y (missing values)
+ df.dropna(subset=label_cols, inplace=True)
+
+ # Validate target labels
+ # TODO: check sklearn.utils.multiclass.check_classification_targets
+ # WIP
+ match task:
+ case 'regression':
+ assert all(df['Y'].apply(lambda x: isinstance(x, Number))), \
+ f"Y for task `regression` must be numeric; got {set(df['Y'].apply(type))}."
+ case 'binary':
+ assert all(df['Y'].isin([0, 1])), \
+ f"Y for task `binary` must be 0 or 1, but Y got {pd.unique(df['Y'])}." \
+ "\nYou may set `thresholds` to discretize continuous labels."
+ case 'multiclass':
+ assert len(label_cols) == len(n_class), \
+ (f'Data table has {len(label_cols)} label columns (`Y*`) but you have specified '
+ f'n_class of length {len(n_class)} for task `multiclass`.')
+ for label, n in zip(df[label_cols], n_class):
+ assert n >= 3, f'n_class for task `multiclass` must be at least 3.'
+ assert all(label.apply(lambda x: x.is_integer() and x >= 0)), \
+ f"Y for task `multiclass` must be non-negative integers, " \
+ f"but Y got {pd.unique(label)}." \
+ "\nYou may set `thresholds` to discretize continuous labels."
+ target_n_unique = label.nunique()
+ assert target_n_unique == n, \
+ f"You have set n_classes for task `multiclass` task to {n}, " \
+ f"but Y has {target_n_unique} unique labels."
+
+ self.df = df
+ self.featurizers = featurizers
+ self.n_class = n_class
+
+ def __len__(self):
+ return len(self.df.index)
+
+ # WIP
+ def __getitem__(self, idx):
+ sample = self.df.loc[idx]
+ return {
+ 'X': [featurizer(x) for featurizer, x in zip(self.featurizers, sample[self.entity_cols])],
+ 'ID': sample.get('ID', sample['X']),
+ 'Y': sample.get('Y')
+ }
diff --git a/deepscreen/data/utils/label.py b/deepscreen/data/utils/label.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab97d2f6a607bc949008be5bb869fb0836614e54
--- /dev/null
+++ b/deepscreen/data/utils/label.py
@@ -0,0 +1,92 @@
+from numbers import Number
+from typing import Optional, Union
+
+import numpy as np
+
+from deepscreen.utils import get_logger
+
+log = get_logger(__name__)
+
+MOLARITY_TO_POTENCY = {
+ 'p': lambda x: x,
+ 'M': lambda x: -np.log10(x),
+ 'mM': lambda x: -np.log10(x) + 3,
+ 'μM': lambda x: -np.log10(x) + 6,
+ 'uM': lambda x: -np.log10(x) + 6, # in case someone doesn't know how to type micromolar lol
+ 'nM': lambda x: -np.log10(x) + 9,
+ 'pM': lambda x: -np.log10(x) + 12,
+ 'fM': lambda x: -np.log10(x) + 15,
+}
+
+
+def molar_to_p(labels, units):
+ assert units in MOLARITY_TO_POTENCY, f"Allowed units: {', '.join(MOLARITY_TO_POTENCY)}."
+
+ unit_converted_labels = []
+ for label, unit in (labels, units):
+ unit_converted_labels.append(MOLARITY_TO_POTENCY[unit](label))
+ labels = np.array(unit_converted_labels)
+
+ return labels
+
+
+def label_discretize(labels, thresholds):
+ # if isinstance(threshold, Number):
+ # labels = np.where(labels < threshold, 1, 0)
+ # else:
+ # labels = np.where(labels < threshold[0], 1, np.where(labels > threshold[1], 0, np.nan))
+ if isinstance(thresholds, Number):
+ labels = 1 - np.digitize(labels, [thresholds])
+ else:
+ labels = np.digitize(labels, np.sort(thresholds)[::-1])
+
+ return labels
+
+
+def label_transform(
+ labels,
+ units: Optional[list[str]],
+ thresholds: Optional[Union[float, list[Number]]],
+ discard_intermediate: Optional[bool]
+):
+ f"""Convert labels of all units to p scale (-log10[M]) and binarize them if specified.
+ :param labels: a sequence of labels, continuous or binary values
+ :type labels: array_like
+ :param units: a sequence of label units in {', '.join(MOLARITY_TO_POTENCY)}
+ :type units: array_like, optional
+ :param thresholds: discretization threshold(s) for affinity labels, in p scale (-log10[M]).
+ A single number maps affinities below it to 1 and otherwise to 0.
+ A tuple of two or more thresholds maps affinities to multiple discrete levels descendingly, assigning values
+ values below the lowest threshold to the highest level (e.g. 2) and values above the greatest threshold to 0
+ :type thresholds: list, float, optional
+ :param discard_intermediate: whether to discard the intermediate (indeterminate) level if provided an odd
+ number of thresholds (>=3)
+ :type discard_intermediate: bool
+ :return: a numpy array of affinity labels in p scale (-log10[M]) or discrete labels
+ """
+ # # Check if labels are already discrete (ignoring NAs).
+ # discrete = labels.dropna().isin([0, 1]).all()
+ #
+ # if discrete:
+ # assert discretize, "Cannot train a regression model with discrete labels."
+ # if thresholds:
+ # warn("Ignoring 'threshold' because 'Y' (labels) in the data table is already binary.")
+ # if units:
+ # warn("Ignoring 'units' because 'Y' (labels) in the data table is already binary.")
+ # labels = labels
+ if units:
+ labels = molar_to_p(labels, units)
+
+ if thresholds:
+ labels = label_discretize(labels, thresholds)
+ if discard_intermediate:
+ assert len(thresholds) % 2 == 1 and len(thresholds) >= 3, \
+ "Must give an odd number of (at least 3) thresholds to discard the intermediate level."
+ intermediate_level = len(thresholds) // 2
+ # Make the intermediate-level labels NaN (which will be filtered out later)
+ labels[labels == intermediate_level] = np.nan
+ # Reduce all levels above the intermediate level by 1
+ labels[labels > intermediate_level] -= 1
+
+ return labels
+
diff --git a/deepscreen/data/utils/sampler.py b/deepscreen/data/utils/sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..24e8a8da68297ac7ee9ec6633fb52ff0533044a7
--- /dev/null
+++ b/deepscreen/data/utils/sampler.py
@@ -0,0 +1,88 @@
+from typing import Mapping, Iterable
+
+from torch.utils.data import BatchSampler, RandomSampler, SequentialSampler
+
+
+class SafeBatchSampler(BatchSampler):
+ """
+ A safe `batch_sampler` that skips samples with `None` values, supports shuffling, and keep a fixed batch size.
+
+ Args:
+ data_source (Dataset): The dataset to sample from.
+ batch_size (int): The size of each batch.
+ drop_last (bool): Whether to drop the last batch if its size is smaller than `batch_size`. Defaults to `False`.
+ shuffle (bool, optional): Whether to shuffle the data before sampling. Defaults to `True`.
+
+ Example:
+ >>> dataloader = DataLoader(dataset, batch_sampler=SafeBatchSampler(dataset, batch_size, drop_last, shuffle))
+ """
+ def __init__(self, data_source, batch_size: int, drop_last: bool, shuffle: bool, sampler=None):
+ if not isinstance(batch_size, int) or isinstance(batch_size, bool) or \
+ batch_size <= 0:
+ raise ValueError(f"batch_size should be a positive integer value, but got batch_size={batch_size}")
+ if not isinstance(drop_last, bool):
+ raise ValueError(f"drop_last should be a boolean value, but got drop_last={drop_last}")
+ if sampler:
+ pass
+ elif shuffle:
+ sampler = RandomSampler(data_source) # type: ignore[arg-type]
+ else:
+ sampler = SequentialSampler(data_source) # type: ignore[arg-type]
+
+ super().__init__(sampler, batch_size, drop_last)
+ self.data_source = data_source
+
+ # def __iter__(self):
+ # batch = []
+ # for idx in self.sampler:
+ # sample = self.data_source[idx]
+ # # if isinstance(sample, list | tuple):
+ # # pass
+ # # elif isinstance(sample, dict):
+ # # sample = sample.values()
+ # # elif isinstance(sample, Series):
+ # # sample = sample.values
+ # # else:
+ # # sample = [sample]
+ # if isinstance(sample, (Iterable, Mapping)) and not isinstance(sample, str):
+ # if isinstance(sample, Mapping):
+ # sample = list(sample.values())
+ # else:
+ # sample = [sample]
+ #
+ # if all(v is not None for v in sample):
+ # batch.append(idx)
+ # if len(batch) == self.batch_size:
+ # yield batch
+ # batch = []
+ #
+ # if len(batch) > 0 and not self.drop_last:
+ # yield batch
+ #
+ # if not batch:
+ # raise StopIteration
+
+ def __iter__(self):
+ batch = [0] * self.batch_size
+ idx_in_batch = 0
+ for idx in self.sampler:
+ sample = self.data_source[idx]
+ if isinstance(sample, (Iterable, Mapping)) and not isinstance(sample, str):
+ if isinstance(sample, Mapping):
+ sample = sample.values()
+ else:
+ sample = [sample]
+
+ if all(v is not None for v in sample):
+ batch[idx_in_batch] = idx
+ idx_in_batch += 1
+ if idx_in_batch == self.batch_size:
+ yield batch
+ idx_in_batch = 0
+ batch = [0] * self.batch_size
+
+ if idx_in_batch > 0 and not self.drop_last:
+ yield batch[:idx_in_batch]
+
+ if not any(batch):
+ raise StopIteration
diff --git a/deepscreen/data/utils/split.py b/deepscreen/data/utils/split.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8f6d643831af267d306035dff5e45b9930b23c9
--- /dev/null
+++ b/deepscreen/data/utils/split.py
@@ -0,0 +1,72 @@
+import torch
+from typing import List, Union
+from torch.utils import data
+
+# FIXME Test and fix these split functions later
+
+
+def random_split(dataset: data.Dataset, lengths, seed):
+ return data.random_split(dataset, lengths, generator=torch.Generator().manual_seed(seed))
+
+
+def cold_start(dataset: data.Dataset, frac: List[float], entities: Union[str, List[str]]):
+ """Create cold-start splits for PyTorch datasets.
+
+ Args:
+ dataset (Dataset): PyTorch dataset object.
+ frac (list): A list of train/valid/test fractions.
+ entities (Union[str, List[str]]): Either a single "cold" entity or a list of "cold" entities
+ on which the split is done.
+
+ Returns:
+ dict: A dictionary of splitted datasets, where keys are 'train', 'valid', and 'test',
+ and values correspond to each dataset.
+ """
+ if isinstance(entities, str):
+ entities = [entities]
+
+ train_frac, val_frac, test_frac = frac
+
+ # Collect unique instances for each entity
+ entity_instances = {}
+ for entity in entities:
+ entity_instances[entity] = list(set([getattr(sample, entity) for sample in dataset]))
+
+ # Sample instances belonging to the test datasets
+ test_entity_instances = [
+ torch.randperm(len(entity_instances[entity]))[:int(len(entity_instances[entity]) * test_frac)]
+ for entity in entities
+ ]
+
+ # Select samples where all entities are in the test set
+ test_indices = []
+ for i, sample in enumerate(dataset):
+ if all([getattr(sample, entity) in entity_instances[entity][test_entity_instances[j]] for j, entity in enumerate(entities)]):
+ test_indices.append(i)
+
+ if len(test_indices) == 0:
+ raise ValueError('No test samples found. Try increasing the test frac or a less stringent splitting strategy.')
+
+ # Proceed with validation data
+ train_val_indices = list(set(range(len(dataset))) - set(test_indices))
+
+ val_entity_instances = [
+ torch.randperm(len(entity_instances[entity]))[:int(len(entity_instances[entity]) * val_frac / (1 - test_frac))]
+ for entity in entities
+ ]
+
+ val_indices = []
+ for i in train_val_indices:
+ if all([getattr(dataset[i], entity) in entity_instances[entity][val_entity_instances[j]] for j, entity in enumerate(entities)]):
+ val_indices.append(i)
+
+ if len(val_indices) == 0:
+ raise ValueError('No validation samples found. Try increasing the test frac or a less stringent splitting strategy.')
+
+ train_indices = list(set(train_val_indices) - set(val_indices))
+
+ train_dataset = torch.utils.data.Subset(dataset, train_indices)
+ val_dataset = torch.utils.data.Subset(dataset, val_indices)
+ test_dataset = torch.utils.data.Subset(dataset, test_indices)
+
+ return {'train': train_dataset, 'valid': val_dataset, 'test': test_dataset}
diff --git a/deepscreen/data/utils/transform.py b/deepscreen/data/utils/transform.py
new file mode 100644
index 0000000000000000000000000000000000000000..e146b880da2a30cd1abcc17490ec4f778506ed24
--- /dev/null
+++ b/deepscreen/data/utils/transform.py
@@ -0,0 +1,8 @@
+from sklearn.preprocessing import StandardScaler, MinMaxScaler, MaxAbsScaler, RobustScaler
+
+
+def scale_transform(features, scaler: type[StandardScaler, MinMaxScaler, MaxAbsScaler, RobustScaler]):
+ scaler = scaler()
+ features = scaler.fit_transform(features)
+ features = scaler.transform(features)
+ return features
diff --git a/deepscreen/gui/__init__.py b/deepscreen/gui/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/deepscreen/gui/app.py b/deepscreen/gui/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..53050185095e47900a639cae36a9a9ac1bb93589
--- /dev/null
+++ b/deepscreen/gui/app.py
@@ -0,0 +1,145 @@
+from io import BytesIO
+import os
+from pathlib import Path
+import sys
+
+import gradio as gr
+import pandas as pd
+from rdkit import Chem
+from rdkit.Chem import RDConfig, Descriptors, Lipinski, Crippen
+
+sys.path.append(os.path.join(RDConfig.RDContribDir, 'SA_Score'))
+import sascorer
+
+
+root = Path.cwd()
+task_list = [f.stem for f in root.parent.joinpath("configs/task").iterdir() if f.suffix == ".yaml"]
+preset_list = [f.stem for f in root.parent.joinpath("configs/preset").iterdir() if f.suffix == ".yaml"]
+predictor_list = [f.stem for f in root.parent.joinpath("configs/model/predictor").iterdir() if f.suffix == ".yaml"]
+drug_encoder_list = [f.stem for f in root.parent.joinpath("configs/model/predictor/drug_encoder").iterdir() if
+ f.suffix == ".yaml"]
+drug_featurizer_list = [f.stem for f in root.parent.joinpath("configs/data/drug_featurizer").iterdir() if
+ f.suffix == ".yaml"]
+protein_encoder_list = [f.stem for f in root.parent.joinpath("configs/model/predictor/protein_encoder").iterdir() if
+ f.suffix == ".yaml"]
+protein_featurizer_list = [f.stem for f in root.parent.joinpath("configs/data/protein_featurizer").iterdir() if
+ f.suffix == ".yaml"]
+classifier_list = [f.stem for f in root.parent.joinpath("configs/model/predictor/decoder").iterdir() if
+ f.suffix == ".yaml"]
+
+
+def load_csv(file):
+ return pd.read_csv(BytesIO(file))
+
+
+def drug_target_predict(
+ predict_data,
+ task,
+ preset,
+):
+ try:
+ return load_csv(predict_data)
+ except Exception as e:
+ raise gr.Error(str(e))
+
+
+# Define a function that takes a CSV output and a list of analytical utility functions as inputs
+def analyze_csv(df_predict, score_list, filter_list):
+ df_report = df_predict.copy()
+ try:
+ # Loop through the list of functions and apply them to the dataframe
+ for filter_name in filter_list:
+ gr.Info(f'Applying {filter_name}...')
+
+ for score_name in score_list:
+ gr.Info(f'Calculating {score_name}...')
+ # Apply the function to the dataframe and assign the result to a new column
+ df_report[score_name] = df_report.apply(score_map[score_name], axis=1)
+ # Return the dataframe as a table
+ return df_report
+ except Exception as e:
+ raise gr.Error(str(e))
+
+
+def sa_score(row):
+ return sascorer.calculateScore(Chem.MolFromSmiles(row['X1']))
+
+
+def mw(row):
+ return Chem.Descriptors.MolWt(Chem.MolFromSmiles(row['X1']))
+
+
+def hbd(row):
+ return Lipinski.NumHDonors(Chem.MolFromSmiles(row['X1']))
+
+
+def hba(row):
+ return Lipinski.NumHAcceptors(Chem.MolFromSmiles(row['X1']))
+
+
+def logp(row):
+ return Crippen.MolLogP(Chem.MolFromSmiles(row['X1']))
+
+
+score_map = {
+ 'SAscore': sa_score,
+ 'RAscore': None, # https://github.com/reymond-group/RAscore
+ 'SCScore': None, # https://pubs.acs.org/doi/10.1021/acs.jcim.7b00622
+ 'LogP': logp, # https://www.rdkit.org/docs/source/rdkit.Chem.Crippen.html
+ 'MW': mw, # https://www.rdkit.org/docs/source/rdkit.Chem.Descriptors.html
+ 'HBD': hbd, # https://www.rdkit.org/docs/source/rdkit.Chem.Lipinski.html
+ 'HBA': hba, # https://www.rdkit.org/docs/source/rdkit.Chem.Lipinski.html
+ 'TopoPSA': None, # http://mordred-descriptor.github.io/documentation/master/api/mordred.TopoPSA.html
+}
+
+filter_map = {
+ 'PAINS filter': None,
+ "Lipinski's rule of five": None, # https://gist.github.com/strets123/fdc4db6d450b66345f46
+ 'ADMET filter': None,
+ 'TCL filter': None
+}
+
+# cbg = gr.CheckboxGroup(choices=list(df.columns), label="Select columns")
+#
+# df_report = gr.Dataframe(type="pandas", interactive=False)
+
+df_predict = gr.Dataframe(type="pandas", interactive=False, height=500, visible=False)
+
+with gr.Blocks(theme=gr.themes.Soft(spacing_size="sm", text_size='lg'), title='DeepScreen') as demo:
+ with gr.Tab(label='Inference') as inference:
+ gr.Markdown('''
+ # DeepScreen Inference Service
+
+ DeepScreen for predicting drug-target interaction.
+ ''')
+ # Upload a prediction dataset, choose a prediction task
+ # (`binary` for interaction, and `regression` for affinity), and select a model preset to submit a job.
+ with gr.Row():
+ predict_data = gr.File(file_count="single", label='Prediction dataset file', type='binary')
+
+ # predict_data.change(fn=load_csv, inputs=predict_data, outputs=df_predict)
+ with gr.Row():
+ task = gr.Dropdown(task_list, label='Task')
+ preset = gr.Dropdown(preset_list + [None], label='Preset')
+
+ with gr.Row():
+ gr.ClearButton()
+ predict_btn = gr.Button("Predict", variant="primary")
+
+ predict_btn.click(fn=drug_target_predict, inputs=[predict_data, task, preset], outputs=df_predict)
+
+ with gr.Tab(label='Report') as report:
+ gr.Markdown('''
+ # DeepScreen Virtual Screening Report
+
+ Analytic report for virtual screening predictions.
+ ''')
+ with gr.Row():
+ score_menu = gr.CheckboxGroup(score_map.keys(), label='Scores')
+ filter_menu = gr.CheckboxGroup(filter_map.keys(), label='Filters')
+
+ df_report = gr.Dataframe(type="pandas", interactive=False, visible=True, height=500)
+
+ with gr.Row():
+ gr.ClearButton()
+ analyze_btn = gr.Button("Analyze", variant="primary")
diff --git a/deepscreen/gui/gradio.py b/deepscreen/gui/gradio.py
new file mode 100644
index 0000000000000000000000000000000000000000..39003516a29659cc37f7f97f09133e66f0711093
--- /dev/null
+++ b/deepscreen/gui/gradio.py
@@ -0,0 +1,114 @@
+from pathlib import Path
+
+import gradio as gr
+
+# Use this in a notebook
+root = Path.cwd()
+
+
+drug_encoder_list = [f.stem for f in root.parent.joinpath("configs/model/drug_encoder").iterdir() if f.suffix == ".yaml"]
+
+drug_featurizer_list = [f.stem for f in root.parent.joinpath("configs/model/drug_featurizer").iterdir() if f.suffix == ".yaml"]
+
+protein_encoder_list = [f.stem for f in root.parent.joinpath("configs/model/protein_encoder").iterdir() if f.suffix == ".yaml"]
+
+protein_featurizer_list = [f.stem for f in root.parent.joinpath("configs/model/protein_featurizer").iterdir() if f.suffix == ".yaml"]
+
+classifier_list = [f.stem for f in root.parent.joinpath("configs/model/classifier").iterdir() if f.suffix == ".yaml"]
+
+preset_list = [f.stem for f in root.parent.joinpath("configs/model/preset").iterdir() if f.suffix == ".yaml"]
+
+
+from typing import Optional
+
+def drug_target_interaction(
+ binary: bool,
+ drug_encoder,
+ drug_featurizer,
+ protein_encoder,
+ protein_featurizer,
+ classifier,
+ preset,) -> Optional[float]:
+
+
+ return 1
+
+def drug_encoder(
+ binary: bool,
+ drug_encoder,
+ drug_featurizer,
+ protein_encoder,
+ protein_featurizer,
+ classifier,
+ preset,):
+
+ return
+
+def protein_encoder(
+ binary: bool,
+ drug_encoder,
+ drug_featurizer,
+ protein_encoder,
+ protein_featurizer,
+ classifier,
+ preset,):
+
+ return
+
+# demo = gr.Interface(
+# fn=drug_target_interaction,
+# inputs=[
+# gr.Radio(["True", "False"]),
+# gr.Dropdown(drug_encoder_list),
+# gr.Dropdown(drug_featurizer_list),
+# gr.Dropdown(protein_encoder_list),
+# gr.Dropdown(protein_featurizer_list),
+# gr.Dropdown(classifier_list),
+# gr.Dropdown(preset_list),
+# ],
+# outputs=["number"],
+# show_error=True,
+#
+# )
+#
+# demo.launch()
+
+
+from omegaconf import DictConfig, OmegaConf
+
+type_to_component_map = {list: gr.Text, int: gr.Number, float: gr.Number}
+
+
+def get_config_choices(config_path: str):
+ return [f.stem for f in Path("../../configs/", config_path).iterdir() if f.suffix == ".yaml"]
+
+
+def create_blocks_from_config(cfg: DictConfig):
+ with gr.Blocks() as blocks:
+ for key, value in cfg.items():
+ if type(value) in [int, float]:
+ component = gr.Number(value=value, label=key, interactive=True)
+ if type(value) in [dict, DictConfig]:
+ with gr.Tab(label=key):
+ component = create_blocks_from_config(value)
+ else:
+ component = gr.Text(value=value, label=key, interactive=True)
+ return blocks
+
+
+def create_interface_from_config(fn: callable, cfg: DictConfig):
+ inputs = []
+
+ for key, value in OmegaConf.to_object(cfg).items():
+ component = type_to_component_map.get(type(value), gr.Text)
+ inputs.append(component(value=value, label=key, interactive=True))
+
+ interface = gr.Interface(fn=fn, inputs=inputs, outputs="label")
+
+ return interface
+
+
+import hydra
+
+with hydra.initialize(version_base=None, config_path="../../configs/"):
+ cfg = hydra.compose("train")
\ No newline at end of file
diff --git a/deepscreen/models/__init__.py b/deepscreen/models/__init__.py
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diff --git a/deepscreen/models/components/__init__.py b/deepscreen/models/components/__init__.py
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diff --git a/deepscreen/models/components/__pycache__/gat.cpython-311.pyc b/deepscreen/models/components/__pycache__/gat.cpython-311.pyc
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diff --git a/deepscreen/models/components/cnn.py b/deepscreen/models/components/cnn.py
new file mode 100644
index 0000000000000000000000000000000000000000..dcaba9b0f18902b2d6503c4ec1e08170cbd2a1de
--- /dev/null
+++ b/deepscreen/models/components/cnn.py
@@ -0,0 +1,42 @@
+from torch import nn, rand
+from torch.autograd import Variable
+
+
+class CNN(nn.Sequential):
+ def __init__(
+ self,
+ filters: list[int],
+ kernels: list[int],
+ max_sequence_length: int,
+ in_channels: int,
+ out_channels: int
+ ):
+ super().__init__()
+ num_layer = len(filters)
+ channels = [in_channels] + filters
+ self.conv = nn.ModuleList([nn.Conv1d(in_channels=channels[i],
+ out_channels=channels[i+1],
+ kernel_size=kernels[i])
+ for i in range(num_layer)])
+ n_size = self._get_conv_output((in_channels, max_sequence_length))
+ self.fc1 = nn.Linear(n_size, out_channels)
+
+ def _forward_features(self, x):
+ for layer in self.conv:
+ x = nn.functional.relu(layer(x))
+ x = nn.functional.adaptive_max_pool1d(x, output_size=1)
+ return x
+
+ def _get_conv_output(self, shape):
+ bs = 1
+ input_feat = Variable(rand(bs, *shape))
+ output_feat = self._forward_features(input_feat)
+ n_size = output_feat.data.view(bs, -1).size(1)
+ return n_size
+
+ def forward(self, v):
+ v = self._forward_features(v.float())
+ v = v.view(v.size(0), -1)
+ v = self.fc1(v)
+ return v
+
diff --git a/deepscreen/models/components/concat_mlp.py b/deepscreen/models/components/concat_mlp.py
new file mode 100644
index 0000000000000000000000000000000000000000..481d09dd8dc05210138f91cf07484fdba1c8e1da
--- /dev/null
+++ b/deepscreen/models/components/concat_mlp.py
@@ -0,0 +1,11 @@
+from torch import cat
+
+from deepscreen.models.components.mlp import MLP
+
+
+class ConcatMLP(MLP):
+ def forward(self, *inputs):
+ x = cat([*inputs], 1)
+ for module in self:
+ x = module(x)
+ return x
diff --git a/deepscreen/models/components/gat.py b/deepscreen/models/components/gat.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa8b439601178f280e44f6914b881f56879bd128
--- /dev/null
+++ b/deepscreen/models/components/gat.py
@@ -0,0 +1,39 @@
+from torch import nn
+import torch.nn.functional as F
+from torch_geometric.nn import GATConv
+from torch_geometric.nn import global_max_pool as gmp
+
+
+class GAT(nn.Module):
+ r"""
+ From `GraphDTA `_ (Nguyen et al., 2020),
+ based on `Graph Attention Network `_ (Veličković et al., 2018).
+ """
+ def __init__(
+ self,
+ num_features: int,
+ out_channels: int,
+ dropout: float
+ ):
+ super().__init__()
+
+ self.dropout = dropout
+ self.gcn1 = GATConv(num_features, num_features, heads=10, dropout=dropout)
+ self.gcn2 = GATConv(num_features * 10, out_channels, dropout=dropout)
+ self.fc_g1 = nn.Linear(out_channels, out_channels)
+ self.relu = nn.ReLU()
+
+ def forward(self, data):
+ # graph input feed-forward
+ x, edge_index, batch = data.x, data.edge_index, data.batch
+
+ x = F.dropout(x, p=self.dropout, training=self.training)
+ x = F.elu(self.gcn1(x, edge_index))
+ x = F.dropout(x, p=self.dropout, training=self.training)
+ x = self.gcn2(x, edge_index)
+ x = self.relu(x)
+ x = gmp(x, batch) # global max pooling
+ x = self.fc_g1(x)
+ x = self.relu(x)
+
+ return x
diff --git a/deepscreen/models/components/gat_gcn.py b/deepscreen/models/components/gat_gcn.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c11bd1f0c0830a40f040eb71d0764557fdc13cc
--- /dev/null
+++ b/deepscreen/models/components/gat_gcn.py
@@ -0,0 +1,40 @@
+from torch import cat, nn
+from torch_geometric.nn import GCNConv, GATConv
+from torch_geometric.nn import global_mean_pool as gap, global_max_pool as gmp
+
+
+class GATGCN(nn.Module):
+ r"""
+ From `GraphDTA `_ (Nguyen et al., 2020),
+ based on `Graph Attention Network `_ (Veličković et al., 2018)
+ and `Graph Convolutional Network `_ (Kipf and Welling, 2017).
+ """
+ def __init__(
+ self,
+ num_features: int,
+ out_channels: int,
+ dropout: float
+ ):
+ super().__init__()
+
+ self.conv1 = GATConv(num_features, num_features, heads=10)
+ self.conv2 = GCNConv(num_features*10, num_features*10)
+ self.fc_g1 = nn.Linear(num_features*10*2, 1500)
+ self.fc_g2 = nn.Linear(1500, out_channels)
+ self.relu = nn.ReLU()
+ self.dropout = nn.Dropout(dropout)
+
+ def forward(self, data):
+ x, edge_index, batch = data.x, data.edge_index, data.batch
+ # print('x shape = ', x.shape)
+ x = self.conv1(x, edge_index)
+ x = self.relu(x)
+ x = self.conv2(x, edge_index)
+ x = self.relu(x)
+ # apply global max pooling (gmp) and global mean pooling (gap)
+ x = cat([gmp(x, batch), gap(x, batch)], dim=1)
+ x = self.relu(self.fc_g1(x))
+ x = self.dropout(x)
+ x = self.fc_g2(x)
+
+ return x
diff --git a/deepscreen/models/components/gcn.py b/deepscreen/models/components/gcn.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec7b497179edae3c838a96489cb5db8355c35df0
--- /dev/null
+++ b/deepscreen/models/components/gcn.py
@@ -0,0 +1,47 @@
+from torch import nn
+from torch_geometric.nn import GCNConv, global_max_pool
+
+
+class GCN(nn.Module):
+ """
+ From `GraphDTA `_ (Nguyen et al., 2020),
+ based on `Graph Convolutional Network `_ (Kipf and Welling, 2017).
+ """
+ def __init__(
+ self,
+ num_features: int,
+ out_channels: int,
+ dropout: float
+ ):
+ super().__init__()
+
+ self.conv1 = GCNConv(num_features, num_features)
+ self.conv2 = GCNConv(num_features, num_features*2)
+ self.conv3 = GCNConv(num_features*2, num_features * 4)
+ self.fc_g1 = nn.Linear(num_features*4, 1024)
+ self.fc_g2 = nn.Linear(1024, out_channels)
+ self.relu = nn.ReLU()
+ self.dropout = nn.Dropout(dropout)
+
+ def forward(self, data):
+ # get graph input
+ x, edge_index, batch = data.x, data.edge_index, data.batch
+
+ x = self.conv1(x, edge_index)
+ x = self.relu(x)
+
+ x = self.conv2(x, edge_index)
+ x = self.relu(x)
+
+ x = self.conv3(x, edge_index)
+ x = self.relu(x)
+ x = global_max_pool(x, batch) # global max pooling
+
+ # flatten
+ x = self.relu(self.fc_g1(x))
+ x = self.dropout(x)
+ x = self.fc_g2(x)
+ x = self.dropout(x)
+
+ return x
+
\ No newline at end of file
diff --git a/deepscreen/models/components/gcn_attn.py b/deepscreen/models/components/gcn_attn.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4e44b360b3a314a1e83412961b62d93f14ea1e7
--- /dev/null
+++ b/deepscreen/models/components/gcn_attn.py
@@ -0,0 +1,66 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+from torch_geometric.nn import GCNConv, global_max_pool as gmp
+
+
+class AttentionGCN(nn.Module):
+ """
+ From `GraphDTA `_ (Nguyen et al., 2020),
+ based on `Graph Convolutional Network `_ (Kipf and Welling, 2017).
+ """
+ def __init__(
+ self,
+ num_features: int,
+ out_channels: int,
+ dropout: float
+ ):
+ super().__init__()
+
+ self.conv1 = GCNConv(num_features, num_features)
+ self.conv2 = GCNConv(num_features, num_features*2)
+ self.conv3 = GCNConv(num_features*2, num_features * 4)
+ self.fc_g1 = nn.Linear(num_features*4, 1024)
+ self.fc_g2 = nn.Linear(1024, out_channels)
+ self.relu = nn.ReLU()
+ self.dropout = nn.Dropout(dropout)
+
+ def forward(self, data):
+ # get graph input
+ x, edge_index, batch = data.x, data.edge_index, data.batch
+
+ x = self.conv1(x, edge_index)
+ x = self.relu(x)
+
+ x = self.conv2(x, edge_index)
+ x = self.relu(x)
+
+ x = self.conv3(x, edge_index)
+ x = self.relu(x)
+ x = gmp(x, batch) # global max pooling
+
+ # flatten
+ x = self.relu(self.fc_g1(x))
+ x = self.dropout(x)
+ x = self.fc_g2(x)
+ x = self.dropout(x)
+
+ return x
+
+
+class Pocket_BCELoss(nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.criterion = nn.BCELoss(reduce=False)
+
+ def forward(self, pred, label, seq_mask):
+ loss_all = self.criterion(pred, label)
+ loss = torch.sum(torch.masked_select(loss_all, seq_mask))
+ return loss
+
+ def protein_pred_module(self, prot_feature, seq_mask):
+ protein_emb = nn.Linear(self.hidden_size1, self.hidden_size1)
+ p_feature = F.leaky_relu(protein_emb(prot_feature), 0.1)
+ pocket_pred = torch.sigmoid(torch.masked_select(p_feature, seq_mask))
+
+ return pocket_pred
diff --git a/deepscreen/models/components/gin.py b/deepscreen/models/components/gin.py
new file mode 100644
index 0000000000000000000000000000000000000000..16754a60295e37e28ebadcdc7b7afa1f9884e930
--- /dev/null
+++ b/deepscreen/models/components/gin.py
@@ -0,0 +1,63 @@
+from torch import cat, nn
+import torch.nn.functional as F
+from torch.nn import Sequential, Linear, ReLU
+from torch_geometric.nn import GINConv, global_add_pool
+
+
+class GIN(nn.Module):
+ r"""
+ From `GraphDTA `_ (Nguyen et al., 2020),
+ based on `Graph Isomorphism Network `_ (Xu et al., 2019)
+ """
+ def __init__(
+ self,
+ num_features: int,
+ out_channels: int,
+ dropout: float
+ ):
+ super().__init__()
+
+ dim = 32
+ self.dropout = dropout
+ self.relu = nn.ReLU()
+
+ nn1 = Sequential(Linear(num_features, dim), ReLU(), Linear(dim, dim))
+ self.conv1 = GINConv(nn1)
+ self.bn1 = nn.BatchNorm1d(dim)
+
+ nn2 = Sequential(Linear(dim, dim), ReLU(), Linear(dim, dim))
+ self.conv2 = GINConv(nn2)
+ self.bn2 = nn.BatchNorm1d(dim)
+
+ nn3 = Sequential(Linear(dim, dim), ReLU(), Linear(dim, dim))
+ self.conv3 = GINConv(nn3)
+ self.bn3 = nn.BatchNorm1d(dim)
+
+ nn4 = Sequential(Linear(dim, dim), ReLU(), Linear(dim, dim))
+ self.conv4 = GINConv(nn4)
+ self.bn4 = nn.BatchNorm1d(dim)
+
+ nn5 = Sequential(Linear(dim, dim), ReLU(), Linear(dim, dim))
+ self.conv5 = GINConv(nn5)
+ self.bn5 = nn.BatchNorm1d(dim)
+
+ self.fc1_xd = Linear(dim, out_channels)
+
+ def forward(self, data):
+ x, edge_index, batch = data.x, data.edge_index, data.batch
+
+ x = F.relu(self.conv1(x, edge_index))
+ x = self.bn1(x)
+ x = F.relu(self.conv2(x, edge_index))
+ x = self.bn2(x)
+ x = F.relu(self.conv3(x, edge_index))
+ x = self.bn3(x)
+ x = F.relu(self.conv4(x, edge_index))
+ x = self.bn4(x)
+ x = F.relu(self.conv5(x, edge_index))
+ x = self.bn5(x)
+ x = global_add_pool(x, batch)
+ x = F.relu(self.fc1_xd(x))
+ x = F.dropout(x, p=self.dropout, training=self.training)
+
+ return x
diff --git a/deepscreen/models/components/lstm.py b/deepscreen/models/components/lstm.py
new file mode 100644
index 0000000000000000000000000000000000000000..149307438aab4780df718c2ee8b683415562c0a0
--- /dev/null
+++ b/deepscreen/models/components/lstm.py
@@ -0,0 +1,40 @@
+from torch import nn, zeros, cat
+
+
+class LSTM(nn.Module):
+ def __init__(
+ self,
+ n_samples: int,
+ hidden_layers: int = 64):
+ super().__init__()
+ self.hidden_layers = hidden_layers
+ # lstm1, lstm2, linear are all layers in the network
+ self.lstm1 = nn.LSTMCell(1, self.hidden_layers)
+ self.lstm2 = nn.LSTMCell(self.hidden_layers, self.hidden_layers)
+ self.linear = nn.Linear(self.hidden_layers, 1)
+ self.n_samples = n_samples
+
+ def forward(self, y, future_preds=0):
+ outputs, num_samples = [], y.size(0)
+ h_t = zeros(self.n_samples, self.hidden_layers)
+ c_t = zeros(self.n_samples, self.hidden_layers)
+ h_t2 = zeros(self.n_samples, self.hidden_layers)
+ c_t2 = zeros(self.n_samples, self.hidden_layers)
+
+ for time_step in y.split(1, dim=1):
+ # N, 1
+ h_t, c_t = self.lstm1(input_t, (h_t, c_t)) # initial hidden and cell states
+ h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2)) # new hidden and cell states
+ output = self.linear(h_t2) # output from the last FC layer
+ outputs.append(output)
+
+ for i in range(future_preds):
+ # this only generates future predictions if we pass in future_preds>0
+ # mirrors the code above, using last output/prediction as input
+ h_t, c_t = self.lstm1(output, (h_t, c_t))
+ h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2))
+ output = self.linear(h_t2)
+ outputs.append(output)
+ # transform list to tensor
+ outputs = cat(outputs, dim=1)
+ return outputs
diff --git a/deepscreen/models/components/mlp.py b/deepscreen/models/components/mlp.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea3dcb2b502104a75525763e418475f627866f65
--- /dev/null
+++ b/deepscreen/models/components/mlp.py
@@ -0,0 +1,77 @@
+from torch import nn, cat
+
+
+class MLP1(nn.Sequential):
+ def __init__(self,
+ input_channels,
+ hidden_channels: list[int],
+ out_channels: int,
+ activation: type[nn.Module] = nn.ReLU,
+ dropout: float = 0.0):
+ layers = []
+ num_layers = len(hidden_channels) + 1
+ dims = [input_channels] + hidden_channels + [out_channels]
+ for i in range(num_layers):
+ if i != (num_layers - 1):
+ layers.append(nn.Linear(dims[i], dims[i+1]))
+ layers.append(nn.Dropout(dropout))
+ layers.append(activation())
+ else:
+ layers.append(nn.Linear(dims[i], dims[i+1]))
+
+ super().__init__(*layers)
+
+
+class MLP2(nn.Sequential):
+ def __init__(self,
+ input_channels,
+ hidden_channels: list[int],
+ out_channels: int,
+ dropout: float = 0.0):
+ super().__init__()
+ self.dropout = nn.Dropout(dropout)
+ num_layers = len(hidden_channels) + 1
+ dims = [input_channels] + hidden_channels + [out_channels]
+ self.layers = nn.ModuleList([nn.Linear(dims[i], dims[i+1]) for i in range(num_layers)])
+
+ def forward(self, x):
+ for i, layer in enumerate(self.layers):
+ if i == (len(self.layers) - 1):
+ x = layer(x)
+ else:
+ x = nn.functional.relu(self.dropout(layer(x)))
+ return x
+
+
+class LazyMLP(nn.Sequential):
+ def __init__(
+ self,
+ out_channels: int,
+ hidden_channels: list[int],
+ activation: type[nn.Module] = nn.ReLU,
+ dropout: float = 0.0
+ ):
+ layers = []
+ for hidden_dim in hidden_channels:
+ layers.append(nn.LazyLinear(out_features=hidden_dim))
+ layers.append(nn.Dropout(dropout))
+ layers.append(activation())
+ layers.append(nn.LazyLinear(out_features=out_channels))
+
+ super().__init__(*layers)
+
+
+class ConcatMLP(LazyMLP):
+ def forward(self, *inputs):
+ x = cat([*inputs], 1)
+ x = super().forward(x)
+ return x
+
+
+# class ConcatMLP(MLP1):
+# def forward(self, *inputs):
+# x = cat([*inputs], 1)
+# for module in self:
+# x = module(x)
+# return x
+
diff --git a/deepscreen/models/components/transformer.py b/deepscreen/models/components/transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..e335794b9f093d612e3991660d093f6386ce2234
--- /dev/null
+++ b/deepscreen/models/components/transformer.py
@@ -0,0 +1,211 @@
+import copy
+import math
+
+import torch
+from torch import nn
+
+
+class Transformer(nn.Module):
+ def __init__(self,
+ input_dim,
+ emb_size,
+ max_position_size,
+ dropout,
+ n_layer,
+ intermediate_size,
+ num_attention_heads,
+ attention_probs_dropout,
+ hidden_dropout,
+ ):
+ super().__init__()
+ self.emb = Embeddings(input_dim,
+ emb_size,
+ max_position_size,
+ dropout)
+ self.encoder = MultiLayeredEncoder(n_layer,
+ emb_size,
+ intermediate_size,
+ num_attention_heads,
+ attention_probs_dropout,
+ hidden_dropout)
+
+ def forward(self, v):
+ e = v[0].long()
+ e_mask = v[1].long()
+ ex_e_mask = e_mask.unsqueeze(1).unsqueeze(2)
+ ex_e_mask = (1.0 - ex_e_mask) * -10000.0
+
+ emb = self.emb(e)
+ encoded_layers = self.encoder(emb.float(), ex_e_mask.float())
+ return encoded_layers[:, 0]
+
+
+class LayerNorm(nn.Module):
+ def __init__(self, hidden_size, variance_epsilon=1e-12):
+ super(LayerNorm, self).__init__()
+ self.gamma = nn.Parameter(torch.ones(hidden_size))
+ self.beta = nn.Parameter(torch.zeros(hidden_size))
+ self.variance_epsilon = variance_epsilon
+
+ def forward(self, x):
+ u = x.mean(-1, keepdim=True)
+ s = (x - u).pow(2).mean(-1, keepdim=True)
+ x = (x - u) / torch.sqrt(s + self.variance_epsilon)
+ return self.gamma * x + self.beta
+
+
+class Embeddings(nn.Module):
+ """Construct the embeddings from protein/target, position embeddings.
+ """
+
+ def __init__(self, vocab_size, hidden_size, max_position_size, dropout_rate):
+ super(Embeddings, self).__init__()
+ self.word_embeddings = nn.Embedding(vocab_size, hidden_size)
+ self.position_embeddings = nn.Embedding(max_position_size, hidden_size)
+
+ self.LayerNorm = LayerNorm(hidden_size)
+ self.dropout = nn.Dropout(dropout_rate)
+
+ def forward(self, input_ids):
+ seq_length = input_ids.size(1)
+ position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
+ position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+
+ words_embeddings = self.word_embeddings(input_ids)
+ position_embeddings = self.position_embeddings(position_ids)
+
+ embeddings = words_embeddings + position_embeddings
+ embeddings = self.LayerNorm(embeddings)
+ embeddings = self.dropout(embeddings)
+ return embeddings
+
+
+class SelfAttention(nn.Module):
+ def __init__(self, hidden_size, num_attention_heads, attention_probs_dropout_prob):
+ super(SelfAttention, self).__init__()
+ if hidden_size % num_attention_heads != 0:
+ raise ValueError(
+ "The hidden size (%d) is not a multiple of the number of attention "
+ "heads (%d)" % (hidden_size, num_attention_heads))
+ self.num_attention_heads = num_attention_heads
+ self.attention_head_size = int(hidden_size / num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.query = nn.Linear(hidden_size, self.all_head_size)
+ self.key = nn.Linear(hidden_size, self.all_head_size)
+ self.value = nn.Linear(hidden_size, self.all_head_size)
+
+ self.dropout = nn.Dropout(attention_probs_dropout_prob)
+
+ def transpose_for_scores(self, x):
+ new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+ x = x.view(*new_x_shape)
+ return x.permute(0, 2, 1, 3)
+
+ def forward(self, hidden_states, attention_mask):
+ mixed_query_layer = self.query(hidden_states)
+ mixed_key_layer = self.key(hidden_states)
+ mixed_value_layer = self.value(hidden_states)
+
+ query_layer = self.transpose_for_scores(mixed_query_layer)
+ key_layer = self.transpose_for_scores(mixed_key_layer)
+ value_layer = self.transpose_for_scores(mixed_value_layer)
+
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+ attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+
+ attention_scores = attention_scores + attention_mask
+
+ # Normalize the attention scores to probabilities.
+ attention_probs = nn.Softmax(dim=-1)(attention_scores)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs = self.dropout(attention_probs)
+
+ context_layer = torch.matmul(attention_probs, value_layer)
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(*new_context_layer_shape)
+ return context_layer
+
+
+class SelfOutput(nn.Module):
+ def __init__(self, hidden_size, hidden_dropout_prob):
+ super(SelfOutput, self).__init__()
+ self.dense = nn.Linear(hidden_size, hidden_size)
+ self.LayerNorm = LayerNorm(hidden_size)
+ self.dropout = nn.Dropout(hidden_dropout_prob)
+
+ def forward(self, hidden_states, input_tensor):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class Attention(nn.Module):
+ def __init__(self, hidden_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob):
+ super(Attention, self).__init__()
+ self.self = SelfAttention(hidden_size, num_attention_heads, attention_probs_dropout_prob)
+ self.output = SelfOutput(hidden_size, hidden_dropout_prob)
+
+ def forward(self, input_tensor, attention_mask):
+ self_output = self.self(input_tensor, attention_mask)
+ attention_output = self.output(self_output, input_tensor)
+ return attention_output
+
+
+class Intermediate(nn.Module):
+ def __init__(self, hidden_size, intermediate_size):
+ super(Intermediate, self).__init__()
+ self.dense = nn.Linear(hidden_size, intermediate_size)
+
+ def forward(self, hidden_states):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = nn.functional.relu(hidden_states)
+ return hidden_states
+
+
+class Output(nn.Module):
+ def __init__(self, intermediate_size, hidden_size, hidden_dropout_prob):
+ super(Output, self).__init__()
+ self.dense = nn.Linear(intermediate_size, hidden_size)
+ self.LayerNorm = LayerNorm(hidden_size)
+ self.dropout = nn.Dropout(hidden_dropout_prob)
+
+ def forward(self, hidden_states, input_tensor):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class Encoder(nn.Module):
+ def __init__(self, hidden_size, intermediate_size, num_attention_heads, attention_probs_dropout_prob,
+ hidden_dropout_prob):
+ super(Encoder, self).__init__()
+ self.attention = Attention(hidden_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob)
+ self.intermediate = Intermediate(hidden_size, intermediate_size)
+ self.output = Output(intermediate_size, hidden_size, hidden_dropout_prob)
+
+ def forward(self, hidden_states, attention_mask):
+ attention_output = self.attention(hidden_states, attention_mask)
+ intermediate_output = self.intermediate(attention_output)
+ layer_output = self.output(intermediate_output, attention_output)
+ return layer_output
+
+
+class MultiLayeredEncoder(nn.Module):
+ def __init__(self, n_layer, hidden_size, intermediate_size, num_attention_heads, attention_probs_dropout_prob,
+ hidden_dropout_prob):
+ super(MultiLayeredEncoder, self).__init__()
+ layer = Encoder(hidden_size, intermediate_size, num_attention_heads, attention_probs_dropout_prob,
+ hidden_dropout_prob)
+ self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(n_layer)])
+
+ def forward(self, hidden_states, attention_mask):
+ for layer_module in self.layer:
+ hidden_states = layer_module(hidden_states, attention_mask)
+ return hidden_states
diff --git a/deepscreen/models/dti.py b/deepscreen/models/dti.py
new file mode 100644
index 0000000000000000000000000000000000000000..aabb809507ab0d50486a7ff6b38807ee5ef23956
--- /dev/null
+++ b/deepscreen/models/dti.py
@@ -0,0 +1,143 @@
+from functools import partial
+from typing import Optional, Sequence, Dict
+
+from torch import nn, optim, Tensor
+from lightning import LightningModule
+from torchmetrics import Metric, MetricCollection
+
+
+class DTILightningModule(LightningModule):
+ """
+ Drug Target Interaction Prediction
+
+ optimizer: a partially or fully initialized instance of class torch.optim.Optimizer
+ drug_encoder: a fully initialized instance of class torch.nn.Module
+ protein_encoder: a fully initialized instance of class torch.nn.Module
+ classifier: a fully initialized instance of class torch.nn.Module
+ model: a fully initialized instance of class torch.nn.Module
+ metrics: a list of fully initialized instances of class torchmetrics.Metric
+ """
+ def __init__(
+ self,
+ optimizer: optim.Optimizer,
+ scheduler: Optional[optim.lr_scheduler | Dict],
+ predictor: nn.Module,
+ metrics: Optional[Dict[str, Metric]] = (),
+ out: nn.Module = None,
+ loss: nn.Module = None,
+ activation: nn.Module = None,
+ ):
+ super().__init__()
+
+ self.predictor = predictor
+ self.out = out
+ self.loss = loss
+ self.activation = activation
+
+ # Automatically averaged over batches:
+ # Separate metric instances for train, val and test step to ensure a proper reduction over the epoch
+ metrics = MetricCollection(dict(metrics))
+ self.train_metrics = metrics.clone(prefix="train/")
+ self.val_metrics = metrics.clone(prefix="val/")
+ self.test_metrics = metrics.clone(prefix="test/")
+
+ # allows access to init params with 'self.hparams' attribute and ensures init params will be stored in ckpt
+ self.save_hyperparameters(logger=False,
+ ignore=['predictor', 'out', 'loss', 'activation', 'metrics'])
+
+ def setup(self, stage):
+ match stage:
+ case 'fit':
+ dataloader = self.trainer.datamodule.train_dataloader()
+ case 'validate':
+ dataloader = self.trainer.datamodule.val_dataloader()
+ case 'test':
+ dataloader = self.trainer.datamodule.test_dataloader()
+ case 'predict':
+ dataloader = self.trainer.datamodule.predict_dataloader()
+ dummy_batch = next(iter(dataloader))
+
+ # for key, value in dummy_batch.items():
+ # if isinstance(value, Tensor):
+ # dummy_batch[key] = value.to(self.device)
+
+ self.forward(dummy_batch)
+
+ def forward(self, batch):
+ output = self.predictor(batch['X1'], batch['X2'])
+ target = batch.get('Y')
+ indexes = batch.get('IDX')
+ preds = None
+ loss = None
+
+ if isinstance(output, Tensor):
+ output = self.out(output).squeeze(1)
+ preds = self.activation(output)
+
+ elif isinstance(output, Sequence):
+ output = list(output)
+ # If multi-objective, assume the zeroth element in `output` is main while the rest are auxiliary
+ output[0] = self.out(output[0]).squeeze(1)
+ # Downstream metrics evaluation only needs main-objective preds
+ preds = self.activation(output[0])
+
+ if target is not None:
+ loss = self.loss(output, target.float())
+
+ return preds, target, indexes, loss
+
+ def training_step(self, batch, batch_idx):
+ preds, target, indexes, loss = self.forward(batch)
+ self.log('train/loss', loss, on_step=False, on_epoch=True, prog_bar=True, sync_dist=True)
+ self.train_metrics(preds=preds, target=target, indexes=indexes.long())
+ self.log_dict(self.train_metrics, on_step=False, on_epoch=True, prog_bar=True, sync_dist=True)
+
+ return {'loss': loss, 'N': batch['N'], 'ID1': batch['ID1'], 'ID2': batch['ID2'], 'Y^': preds, 'Y': target}
+
+ def on_train_epoch_end(self):
+ pass
+
+ def validation_step(self, batch, batch_idx):
+ preds, target, indexes, loss = self.forward(batch)
+
+ self.log('val/loss', loss, on_step=False, on_epoch=True, prog_bar=True, sync_dist=True)
+ self.val_metrics(preds=preds, target=target, indexes=indexes.long())
+ self.log_dict(self.val_metrics, on_step=False, on_epoch=True, prog_bar=True, sync_dist=True)
+
+ def on_validation_epoch_end(self):
+ pass
+
+ def test_step(self, batch, batch_idx):
+ preds, target, indexes, loss = self.forward(batch)
+
+ self.log('test/loss', loss, on_step=False, on_epoch=True, prog_bar=True, sync_dist=True)
+ self.test_metrics(preds=preds, target=target, indexes=indexes.long())
+ self.log_dict(self.test_metrics, on_step=False, on_epoch=True, prog_bar=True, sync_dist=True)
+
+ # return a dictionary for callbacks like BasePredictionWriter
+ return {'N': batch['N'], 'ID1': batch['ID1'], 'ID2': batch['ID2'], 'Y^': preds, 'Y': target}
+
+ def on_test_epoch_end(self):
+ pass
+
+ def predict_step(self, batch, batch_idx, dataloader_idx=0):
+ preds, target, indexes = self.forward(batch)
+ # return a dictionary for callbacks like BasePredictionWriter
+ return {'N': batch['N'], 'ID1': batch['ID1'], 'ID2': batch['ID2'], 'Y^': preds}
+
+ def configure_optimizers(self):
+ optimizers_config = {'optimizer': self.hparams.optimizer(params=self.parameters())}
+ if self.hparams.get('scheduler'):
+ if isinstance(self.hparams.scheduler, partial):
+ optimizers_config['lr_scheduler'] = {
+ "scheduler": self.hparams.scheduler(optimizer=optimizers_config['optimizer']),
+ "monitor": "val/loss",
+ "interval": "epoch",
+ "frequency": 1,
+ }
+ else:
+ self.hparams.scheduler['scheduler'] = self.hparams.scheduler['scheduler'](
+ optimizer=optimizers_config['optimizer']
+ )
+ optimizers_config['lr_scheduler'] = dict(self.hparams.scheduler)
+ return optimizers_config
diff --git a/deepscreen/models/dti.py.bak b/deepscreen/models/dti.py.bak
new file mode 100644
index 0000000000000000000000000000000000000000..a2dd3a0e43d7b6956ad794a028b81efa7a7fa6a4
--- /dev/null
+++ b/deepscreen/models/dti.py.bak
@@ -0,0 +1,147 @@
+from typing import Optional, Sequence, Literal
+
+from torch import nn, optim
+from lightning import LightningModule
+from torchmetrics import Metric, MetricCollection, MeanMetric
+
+
+class DTILightningModule(LightningModule):
+ """
+ Drug Target Interaction Prediction
+
+ optimizer: a partially or fully initialized instance of class torch.optim.Optimizer
+ drug_encoder: a fully initialized instance of class torch.nn.Module
+ protein_encoder: a fully initialized instance of class torch.nn.Module
+ classifier: a fully initialized instance of class torch.nn.Module
+ model: a fully initialized instance of class torch.nn.Module
+ metrics: a list of fully initialized instances of class torchmetrics.Metric
+ """
+ def __init__(
+ self,
+ optimizer: optim.Optimizer,
+ scheduler: optim.lr_scheduler,
+ predictor: Optional[nn.Module],
+ metrics: Optional[dict[Metric]] = (),
+ out: nn.Module = None,
+ loss: nn.Module = None,
+ activation: nn.Module = None,
+ ):
+ super().__init__()
+
+ self.predictor = predictor
+ self.out = out
+ self.loss = loss
+ self.activation = activation
+
+ # averaging loss over batches
+ # use separate metric instances for train, val and test step to ensure a proper reduction over the epoch
+ self.train_loss = MeanMetric()
+ self.val_loss = MeanMetric()
+ self.test_loss = MeanMetric()
+
+ metrics = MetricCollection(dict(metrics))
+ self.train_metrics = metrics.clone(prefix="train/")
+ self.val_metrics = metrics.clone(prefix="val/")
+ self.test_metrics = metrics.clone(prefix="test/")
+
+ # allows access to init params with 'self.hparams' attribute and ensures init params will be stored in ckpt
+ self.save_hyperparameters(logger=False,
+ ignore=['predictor', 'out', 'loss', 'activation', 'metrics'])
+
+ def forward(self, enc_drug, enc_protein):
+ return self.predictor(enc_drug, enc_protein)
+
+ def model_step(self, batch):
+ # common step for training/validation/test
+ enc_drug = batch['X1']
+ enc_protein = batch['X2']
+ target = batch['Y']
+ indexes = batch['ID']
+
+ preds = self.forward(enc_drug, enc_protein)
+
+ if isinstance(preds, Sequence):
+ # the first preds is the main task preds, with the others being auxiliary
+ preds = list(preds)
+ # metrics calculation only needs main task preds
+ preds[0] = self.out(preds[0]).squeeze()
+ loss = self.loss(preds, target)
+ preds = self.activation(preds[0])
+ else:
+ preds = self.out(preds).squeeze()
+ loss = self.loss(preds, target)
+ preds = self.activation(preds)
+
+ return loss, preds, target, indexes
+
+ def training_step(self, batch, batch_idx):
+ loss, preds, target, indexes = self.model_step(batch)
+
+ self.train_loss(loss)
+ self.log("train/loss", self.train_loss, on_step=False, on_epoch=True, prog_bar=True)
+ self.train_metrics(preds=preds, target=target.long(), indexes=indexes.long())
+ self.log_dict(self.train_metrics, on_step=False, on_epoch=True, prog_bar=True)
+
+ return loss # {"loss": loss, "preds": preds, "target": target}
+
+ def on_train_epoch_end(self):
+ # `outputs` is a list of dicts returned from `training_step()`
+ pass
+
+ def validation_step(self, batch, batch_idx):
+ loss, preds, target, indexes = self.model_step(batch)
+
+ self.val_loss(loss)
+ self.log("val/loss", self.val_loss, on_step=False, on_epoch=True, prog_bar=True)
+ self.val_metrics(preds=preds, target=target.long(), indexes=indexes.long())
+ self.log_dict(self.val_metrics, on_step=False, on_epoch=True, prog_bar=True)
+
+ def on_validation_epoch_end(self):
+ pass
+
+ def test_step(self, batch, batch_idx):
+ loss, preds, target, indexes = self.model_step(batch)
+
+ self.test_loss(loss)
+ self.log("test/loss", self.test_loss, on_step=False, on_epoch=True, prog_bar=True)
+ self.test_metrics(preds=preds, target=target.long(), indexes=indexes.long())
+ self.log_dict(self.test_metrics, on_step=False, on_epoch=True, prog_bar=True)
+
+ # return a dictionary for callbacks like BasePredictionWriter
+ return {"Y_hat": preds, "Y": target, 'ID1': batch['ID1'], 'ID2': batch['ID2']}
+
+ def on_test_epoch_end(self):
+ pass
+
+ def predict_step(self, batch, batch_idx, dataloader_idx=0):
+ enc_drug = batch['X1']
+ enc_protein = batch['X2']
+
+ preds = self.forward(enc_drug, enc_protein)
+
+ if isinstance(preds, Sequence):
+ # the first prediction is the main task prediction, with the others being auxiliary
+ preds = self.out(preds[0]).squeeze()
+ else:
+ preds = self.out(preds).squeeze()
+
+ preds = self.activation(preds)
+ # preds = squeeze(preds, dim=1)
+
+ # return a dictionary for callbacks like BasePredictionWriter
+ return {"Y_hat": preds, 'ID1': batch['ID1'], 'ID2': batch['ID2']}
+
+ def configure_optimizers(self):
+ optimizer = self.hparams.optimizer(params=self.parameters())
+ if self.hparams.scheduler is not None:
+ scheduler = self.hparams.scheduler(optimizer=optimizer)
+ return {
+ "optimizer": optimizer,
+ "lr_scheduler": {
+ "scheduler": scheduler,
+ "monitor": "val/loss",
+ "interval": "epoch",
+ "frequency": 1,
+ },
+ }
+ return {"optimizer": optimizer}
diff --git a/deepscreen/models/loss/__init__.py b/deepscreen/models/loss/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
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diff --git a/deepscreen/models/loss/multitask_loss.py b/deepscreen/models/loss/multitask_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ef284e7c0758f71f88844838b47924050bb99f5
--- /dev/null
+++ b/deepscreen/models/loss/multitask_loss.py
@@ -0,0 +1,118 @@
+from itertools import zip_longest
+
+import torch
+
+
+class MultitaskLoss(torch.nn.Module):
+ """A generic multitask loss class that takes a tuple of loss functions as input"""
+ def __init__(self, loss_fns, reduction='sum'):
+ super().__init__()
+ self.n_tasks = len(loss_fns) # assuming the number of tasks is equal to the number of loss functions
+ self.loss_fns = loss_fns # store the tuple of loss functions
+ self.reduction = reduction
+
+ def forward(self, preds, target):
+ if isinstance(preds, torch.Tensor):
+ preds = (preds,)
+ if isinstance(target, torch.Tensor):
+ target = (target,)
+ # compute the weighted losses for each task by applying the corresponding loss function and weight
+ # losses = [weight * loss_fn(p, t)
+ # for weight, loss_fn, p, t in zip_longest(self.weights, self.loss_fns, preds, target)]
+ losses = []
+ for loss_fn, p, t in zip_longest(self.loss_fns, preds, target):
+ if t is not None:
+ loss = loss_fn(p, t)
+ else:
+ loss = loss_fn(p)
+ losses.append(loss)
+
+ reduced_loss = None
+ # apply reduction if specified
+ if self.reduction == 'sum':
+ reduced_loss = sum(losses)
+ elif self.reduction == 'mean':
+ reduced_loss = sum(losses) / self.n_tasks
+ # return the tuple of losses or the reduced value
+ return reduced_loss
+
+
+class MultitaskWeightedLoss(MultitaskLoss):
+ """A multitask loss class that takes a tuple of loss functions and weights as input"""
+
+ def __init__(self, loss_fns, weights, reduction='sum'):
+ super().__init__(loss_fns, reduction)
+ self.weights = weights # store the tuple of weights
+
+ def forward(self, preds, target):
+ if isinstance(preds, torch.Tensor):
+ preds = (preds,)
+ if isinstance(target, torch.Tensor):
+ target = (target,)
+ # compute the weighted losses for each task by applying the corresponding loss function and weight
+ # losses = [weight * loss_fn(p, t)
+ # for weight, loss_fn, p, t in zip_longest(self.weights, self.loss_fns, preds, target)]
+ losses = []
+ for weight, loss_fn, p, t in zip_longest(self.weights, self.loss_fns, preds, target):
+ if t is not None:
+ loss = weight * loss_fn(p, t)
+ else:
+ loss = weight * loss_fn(p)
+ losses.append(loss)
+
+ reduced_loss = None
+ # apply reduction if specified
+ if self.reduction == 'sum':
+ reduced_loss = sum(losses)
+ elif self.reduction == 'mean':
+ reduced_loss = sum(losses) / self.n_tasks
+ # return the tuple of losses or the reduced value
+ return reduced_loss
+
+
+class MultitaskUncertaintyLoss(MultitaskLoss):
+ """
+ Modified from https://arxiv.org/abs/1705.07115.
+ Removed task-specific scale factor for flexibility.
+ """
+
+ def __init__(self, loss_fns):
+ # for loss_fn in loss_fns:
+ # loss_fn.reduction = 'none'
+ super().__init__(loss_fns, reduction='none')
+ self.log_vars = torch.nn.Parameter(torch.zeros(self.n_tasks, requires_grad=True))
+
+ def forward(self, preds, targets, rescale=True):
+ losses = super().forward(preds, targets)
+ stds = torch.exp(self.log_vars / 2)
+ coeffs = 1 / (stds ** 2)
+ loss = coeffs * losses + torch.log(stds)
+
+ return loss
+
+
+class MultitaskAutomaticWeightedLoss(MultitaskLoss):
+ """Automatically weighted multitask loss
+
+ Params:
+ loss_fns: tuple of loss functions
+ num: int, the number of losses
+ x: multitask loss
+ Examples:
+ loss1 = 1
+ loss2 = 2
+ awl = AutomaticWeightedLoss(2)
+ loss_sum = awl(loss1, loss2)
+ """
+
+ def __init__(self, loss_fns):
+ super().__init__(loss_fns, reduction='none')
+ self.params = torch.nn.Parameter(torch.ones(self.n_tasks, requires_grad=True))
+
+ def forward(self, preds, target):
+ losses = super().forward(preds, target)
+ loss = sum(
+ 0.5 / (param ** 2) * loss + torch.log(1 + param ** 2)
+ for param, loss in zip(self.params, losses)
+ )
+ return loss
diff --git a/deepscreen/models/metrics/__init__.py b/deepscreen/models/metrics/__init__.py
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diff --git a/deepscreen/models/metrics/bedroc.py b/deepscreen/models/metrics/bedroc.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb87823db498c6c6d720f0b094b6f1b51b2ed25c
--- /dev/null
+++ b/deepscreen/models/metrics/bedroc.py
@@ -0,0 +1,42 @@
+import torch
+from torch import Tensor
+from torchmetrics.retrieval.base import RetrievalMetric
+from torchmetrics.utilities.checks import _check_retrieval_functional_inputs
+
+from deepscreen.models.metrics.rie import calc_rie
+
+
+class BEDROC(RetrievalMetric):
+ is_differentiable: bool = False
+ higher_is_better: bool = True
+ full_state_update: bool = False
+
+ def __init__(
+ self,
+ alpha: float = 80.5,
+ ):
+ super().__init__()
+ self.alpha = alpha
+
+ def _metric(self, preds: Tensor, target: Tensor) -> Tensor:
+ preds, target = _check_retrieval_functional_inputs(preds, target)
+
+ n_total = target.size(0)
+ n_actives = target.sum()
+
+ if n_actives == 0:
+ return torch.tensor(0.0, device=preds.device)
+ elif n_actives == n_total:
+ return torch.tensor(1.0, device=preds.device)
+
+ r_a = n_actives / n_total
+ exp_a = torch.exp(torch.tensor(self.alpha))
+
+ idx = torch.argsort(preds, descending=True, stable=True)
+ active_ranks = torch.take(target, idx).nonzero() + 1
+
+ rie = calc_rie(n_total, active_ranks, r_a, exp_a)
+ rie_min = (1 - exp_a ** r_a) / (r_a * (1 - exp_a))
+ rie_max = (1 - exp_a ** (-r_a)) / (r_a * (1 - exp_a ** (-1)))
+
+ return (rie - rie_min) / (rie_max - rie_min)
diff --git a/deepscreen/models/metrics/ef.py b/deepscreen/models/metrics/ef.py
new file mode 100644
index 0000000000000000000000000000000000000000..603821538713f9f4b0216f17ad529911031173ef
--- /dev/null
+++ b/deepscreen/models/metrics/ef.py
@@ -0,0 +1,31 @@
+import math
+
+from torch import Tensor, topk
+from torchmetrics.retrieval.base import RetrievalMetric
+from torchmetrics.utilities.checks import _check_retrieval_functional_inputs
+
+
+class EF(RetrievalMetric):
+ is_differentiable: bool = False
+ higher_is_better: bool = True
+ full_state_update: bool = False
+
+ def __init__(
+ self,
+ alpha: float,
+ ):
+ super().__init__()
+ if alpha <= 0 or alpha > 1:
+ raise ValueError(f"Argument ``alpha`` has to be in interval (0, 1] but got {alpha}")
+ self.alpha = alpha
+
+ def _metric(self, preds: Tensor, target: Tensor) -> Tensor:
+ preds, target = _check_retrieval_functional_inputs(preds, target)
+
+ n_total = target.size(0)
+ n_sampled = math.ceil(n_total * self.alpha)
+ _, idx = topk(preds, n_sampled)
+ hits_sampled = target[idx].sum()
+ hits_total = target.sum()
+
+ return hits_sampled / (hits_total * self.alpha)
diff --git a/deepscreen/models/metrics/hit_rate.py b/deepscreen/models/metrics/hit_rate.py
new file mode 100644
index 0000000000000000000000000000000000000000..89842148ae6b81aef35a136c09e803e87c91723f
--- /dev/null
+++ b/deepscreen/models/metrics/hit_rate.py
@@ -0,0 +1,33 @@
+import math
+
+from torch import Tensor, topk
+from torchmetrics.retrieval.base import RetrievalMetric
+from torchmetrics.utilities.checks import _check_retrieval_functional_inputs
+
+
+class HitRate(RetrievalMetric):
+ """
+ Computes hit rate for virtual screening.
+ """
+ is_differentiable: bool = False
+ higher_is_better: bool = True
+ full_state_update: bool = False
+
+ def __init__(
+ self,
+ alpha: float = 0.01,
+ ):
+ super().__init__()
+ if alpha <= 0 or alpha > 1:
+ raise ValueError(f"Argument ``alpha`` has to be in interval (0, 1] but got {alpha}")
+ self.alpha = alpha
+
+ def _metric(self, preds: Tensor, target: Tensor) -> Tensor:
+ preds, target = _check_retrieval_functional_inputs(preds, target)
+
+ n_total = target.size(0)
+ n_sampled = math.ceil(n_total * self.alpha)
+ _, idx = topk(preds, n_sampled)
+ hits_sampled = target[idx].sum()
+
+ return hits_sampled / n_sampled
diff --git a/deepscreen/models/metrics/rie.py b/deepscreen/models/metrics/rie.py
new file mode 100644
index 0000000000000000000000000000000000000000..8acde35faee8f1d2277f158c2a914c2e70426f36
--- /dev/null
+++ b/deepscreen/models/metrics/rie.py
@@ -0,0 +1,41 @@
+import torch
+from torch import Tensor
+from torchmetrics.retrieval.base import RetrievalMetric
+from torchmetrics.utilities.checks import _check_retrieval_functional_inputs
+
+
+class RIE(RetrievalMetric):
+ is_differentiable: bool = False
+ higher_is_better: bool = True
+ full_state_update: bool = False
+
+ def __init__(
+ self,
+ alpha: float = 80.5,
+ ):
+ super().__init__()
+ self.alpha = alpha
+
+ def _metric(self, preds: Tensor, target: Tensor) -> Tensor:
+ preds, target = _check_retrieval_functional_inputs(preds, target)
+
+ n_total = target.size(0)
+ n_actives = target.sum()
+
+ if n_actives == 0:
+ return torch.tensor(0.0, device=preds.device)
+
+ r_a = n_actives / n_total
+ exp_a = torch.exp(torch.tensor(-self.alpha))
+
+ idx = torch.argsort(preds, descending=True, stable=True)
+ active_ranks = torch.take(target, idx).nonzero() + 1
+
+ return calc_rie(n_total, active_ranks, r_a, exp_a)
+
+
+def calc_rie(n_total, active_ranks, r_a, exp_a):
+ numerator = (exp_a ** (- active_ranks / n_total)).sum()
+ denominator = (1 - exp_a ** (-1)) / (exp_a ** (1 / n_total) - 1)
+
+ return numerator / (r_a * denominator)
diff --git a/deepscreen/models/metrics/sensitivity.py b/deepscreen/models/metrics/sensitivity.py
new file mode 100644
index 0000000000000000000000000000000000000000..50797e3a3eae55f7ad3e4833fef6e0f132799479
--- /dev/null
+++ b/deepscreen/models/metrics/sensitivity.py
@@ -0,0 +1,337 @@
+# Copyright The Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Any, Optional, Sequence, Union
+
+from torch import Tensor
+from torchmetrics.utilities.compute import _safe_divide, _adjust_weights_safe_divide
+from typing_extensions import Literal
+
+from torchmetrics.classification.base import _ClassificationTaskWrapper
+from torchmetrics.classification.stat_scores import BinaryStatScores, MulticlassStatScores, MultilabelStatScores
+from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
+from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
+from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
+
+if not _MATPLOTLIB_AVAILABLE:
+ __doctest_skip__ = ["BinarySensitivity.plot", "MulticlassSensitivity.plot", "MultilabelSensitivity.plot"]
+
+
+class BinarySensitivity(BinaryStatScores):
+ r"""Compute `Sensitivity`_ for binary tasks.
+
+ .. math:: \text{Sensitivity} = \frac{\text{TN}}{\text{TN} + \text{FP}}
+
+ Where :math:`\text{TN}` and :math:`\text{FP}` represent the number of true negatives and false positives
+ respectively. The metric is only proper defined when :math:`\text{TN} + \text{FP} \neq 0`. If this case is
+ encountered a score of 0 is returned.
+
+ As input to ``forward`` and ``update`` the metric accepts the following input:
+
+ - ``preds`` (:class:`~torch.Tensor`): An int or float tensor of shape ``(N, ...)``. If preds is a floating point
+ tensor with values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per
+ element. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``.
+ - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``
+
+ As output to ``forward`` and ``compute`` the metric returns the following output:
+
+ - ``bs`` (:class:`~torch.Tensor`): If ``multidim_average`` is set to ``global``, the metric returns a scalar value.
+ If ``multidim_average`` is set to ``samplewise``, the metric returns ``(N,)`` vector consisting of a scalar value
+ per sample.
+
+ Args:
+ threshold: Threshold for transforming probability to binary {0,1} predictions
+ multidim_average:
+ Defines how additionally dimensions ``...`` should be handled. Should be one of the following:
+
+ - ``global``: Additional dimensions are flatted along the batch dimension
+ - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
+ The statistics in this case are calculated over the additional dimensions.
+
+ ignore_index:
+ Specifies a target value that is ignored and does not contribute to the metric calculation
+ validate_args: bool indicating if input arguments and tensors should be validated for correctness.
+ Set to ``False`` for faster computations.
+ """
+ plot_lower_bound: float = 0.0
+ plot_upper_bound: float = 1.0
+
+ def compute(self) -> Tensor:
+ """Compute metric."""
+ tp, fp, tn, fn = self._final_state()
+ return _sensitivity_reduce(tp, fp, tn, fn, average="binary", multidim_average=self.multidim_average)
+
+ def plot(
+ self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
+ ) -> _PLOT_OUT_TYPE:
+ """Plot a single or multiple values from the metric.
+
+ Args:
+ val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
+ If no value is provided, will automatically call `metric.compute` and plot that result.
+ ax: An matplotlib axis object. If provided will add plot to that axis
+
+ Returns:
+ Figure object and Axes object
+
+ Raises:
+ ModuleNotFoundError:
+ If `matplotlib` is not installed
+ """
+ return self._plot(val, ax)
+
+
+class MulticlassSensitivity(MulticlassStatScores):
+ r"""Compute `Sensitivity`_ for multiclass tasks.
+
+ .. math:: \text{Sensitivity} = \frac{\text{TN}}{\text{TN} + \text{FP}}
+
+ Where :math:`\text{TN}` and :math:`\text{FP}` represent the number of true negatives and false positives
+ respectively. The metric is only proper defined when :math:`\text{TN} + \text{FP} \neq 0`. If this case is
+ encountered for any class, the metric for that class will be set to 0 and the overall metric may therefore be
+ affected in turn.
+
+ As input to ``forward`` and ``update`` the metric accepts the following input:
+
+ - ``preds`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)`` or float tensor of shape ``(N, C, ..)``.
+ If preds is a floating point we apply ``torch.argmax`` along the ``C`` dimension to automatically convert
+ probabilities/logits into an int tensor.
+ - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``
+
+ As output to ``forward`` and ``compute`` the metric returns the following output:
+
+ - ``mcs`` (:class:`~torch.Tensor`): The returned shape depends on the ``average`` and ``multidim_average``
+ arguments:
+
+ - If ``multidim_average`` is set to ``global``:
+
+ - If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor
+ - If ``average=None/'none'``, the shape will be ``(C,)``
+
+ - If ``multidim_average`` is set to ``samplewise``:
+
+ - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
+ - If ``average=None/'none'``, the shape will be ``(N, C)``
+
+ Args:
+ num_classes: Integer specifing the number of classes
+ average:
+ Defines the reduction that is applied over labels. Should be one of the following:
+
+ - ``micro``: Sum statistics over all labels
+ - ``macro``: Calculate statistics for each label and average them
+ - ``weighted``: calculates statistics for each label and computes weighted average using their support
+ - ``"none"`` or ``None``: calculates statistic for each label and applies no reduction
+
+ top_k:
+ Number of highest probability or logit score predictions considered to find the correct label.
+ Only works when ``preds`` contain probabilities/logits.
+ multidim_average:
+ Defines how additionally dimensions ``...`` should be handled. Should be one of the following:
+
+ - ``global``: Additional dimensions are flatted along the batch dimension
+ - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
+ The statistics in this case are calculated over the additional dimensions.
+
+ ignore_index:
+ Specifies a target value that is ignored and does not contribute to the metric calculation
+ validate_args: bool indicating if input arguments and tensors should be validated for correctness.
+ Set to ``False`` for faster computations.
+ """
+ plot_lower_bound: float = 0.0
+ plot_upper_bound: float = 1.0
+ plot_legend_name: str = "Class"
+
+ def compute(self) -> Tensor:
+ """Compute metric."""
+ tp, fp, tn, fn = self._final_state()
+ return _sensitivity_reduce(tp, fp, tn, fn, average=self.average, multidim_average=self.multidim_average)
+
+ def plot(
+ self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
+ ) -> _PLOT_OUT_TYPE:
+ """Plot a single or multiple values from the metric.
+
+ Args:
+ val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
+ If no value is provided, will automatically call `metric.compute` and plot that result.
+ ax: An matplotlib axis object. If provided will add plot to that axis
+
+ Returns:
+ Figure object and Axes object
+
+ Raises:
+ ModuleNotFoundError:
+ If `matplotlib` is not installed
+ """
+ return self._plot(val, ax)
+
+
+class MultilabelSensitivity(MultilabelStatScores):
+ r"""Compute `Sensitivity`_ for multilabel tasks.
+
+ .. math:: \text{Sensitivity} = \frac{\text{TN}}{\text{TN} + \text{FP}}
+
+ Where :math:`\text{TN}` and :math:`\text{FP}` represent the number of true negatives and false positives
+ respectively. The metric is only proper defined when :math:`\text{TN} + \text{FP} \neq 0`. If this case is
+ encountered for any label, the metric for that label will be set to 0 and the overall metric may therefore be
+ affected in turn.
+
+ As input to ``forward`` and ``update`` the metric accepts the following input:
+
+ - ``preds`` (:class:`~torch.Tensor`): An int or float tensor of shape ``(N, C, ...)``. If preds is a floating
+ point tensor with values outside [0,1] range we consider the input to be logits and will auto apply sigmoid
+ per element. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``.
+ - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``
+
+
+ As output to ``forward`` and ``compute`` the metric returns the following output:
+
+ - ``mls`` (:class:`~torch.Tensor`): The returned shape depends on the ``average`` and ``multidim_average``
+ arguments:
+
+ - If ``multidim_average`` is set to ``global``
+
+ - If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor
+ - If ``average=None/'none'``, the shape will be ``(C,)``
+
+ - If ``multidim_average`` is set to ``samplewise``
+
+ - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
+ - If ``average=None/'none'``, the shape will be ``(N, C)``
+
+ Args:
+ num_labels: Integer specifing the number of labels
+ threshold: Threshold for transforming probability to binary (0,1) predictions
+ average:
+ Defines the reduction that is applied over labels. Should be one of the following:
+
+ - ``micro``: Sum statistics over all labels
+ - ``macro``: Calculate statistics for each label and average them
+ - ``weighted``: calculates statistics for each label and computes weighted average using their support
+ - ``"none"`` or ``None``: calculates statistic for each label and applies no reduction
+
+ multidim_average: Defines how additionally dimensions ``...`` should be handled. Should be one of the following:
+
+ - ``global``: Additional dimensions are flatted along the batch dimension
+ - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
+ The statistics in this case are calculated over the additional dimensions.
+
+ ignore_index:
+ Specifies a target value that is ignored and does not contribute to the metric calculation
+ validate_args: bool indicating if input arguments and tensors should be validated for correctness.
+ Set to ``False`` for faster computations.
+ """
+ plot_lower_bound: float = 0.0
+ plot_upper_bound: float = 1.0
+ plot_legend_name: str = "Label"
+
+ def compute(self) -> Tensor:
+ """Compute metric."""
+ tp, fp, tn, fn = self._final_state()
+ return _sensitivity_reduce(
+ tp, fp, tn, fn, average=self.average, multidim_average=self.multidim_average, multilabel=True
+ )
+
+ def plot(
+ self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
+ ) -> _PLOT_OUT_TYPE:
+ """Plot a single or multiple values from the metric.
+
+ Args:
+ val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
+ If no value is provided, will automatically call `metric.compute` and plot that result.
+ ax: An matplotlib axis object. If provided will add plot to that axis
+
+ Returns:
+ Figure object and Axes object
+
+ Raises:
+ ModuleNotFoundError:
+ If `matplotlib` is not installed
+ """
+ return self._plot(val, ax)
+
+
+class Sensitivity(_ClassificationTaskWrapper):
+ r"""Compute `Sensitivity`_.
+
+ .. math:: \text{Sensitivity} = \frac{\text{TN}}{\text{TN} + \text{FP}}
+
+ Where :math:`\text{TN}` and :math:`\text{FP}` represent the number of true negatives and false positives
+ respectively. The metric is only proper defined when :math:`\text{TP} + \text{FP} \neq 0`. If this case is
+ encountered for any class/label, the metric for that class/label will be set to 0 and the overall metric may
+ therefore be affected in turn.
+
+ This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
+ ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+ :class:`~torchmetrics.classification.BinarySensitivity`, :class:`~torchmetrics.classification.MulticlassSensitivity`
+ and :class:`~torchmetrics.classification.MultilabelSensitivity` for the specific details of each argument influence
+ and examples.
+
+ Legacy Example:
+ """
+
+ def __new__( # type: ignore[misc]
+ cls,
+ task: Literal["binary", "multiclass", "multilabel"],
+ threshold: float = 0.5,
+ num_classes: Optional[int] = None,
+ num_labels: Optional[int] = None,
+ average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
+ multidim_average: Optional[Literal["global", "samplewise"]] = "global",
+ top_k: Optional[int] = 1,
+ ignore_index: Optional[int] = None,
+ validate_args: bool = True,
+ **kwargs: Any,
+ ) -> Metric:
+ """Initialize task metric."""
+ task = ClassificationTask.from_str(task)
+ assert multidim_average is not None # noqa: S101 # needed for mypy
+ kwargs.update(
+ {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
+ )
+ if task == ClassificationTask.BINARY:
+ return BinarySensitivity(threshold, **kwargs)
+ if task == ClassificationTask.MULTICLASS:
+ if not isinstance(num_classes, int):
+ raise ValueError(f"`num_classes` is expected to be `int` but `{type(num_classes)} was passed.`")
+ if not isinstance(top_k, int):
+ raise ValueError(f"`top_k` is expected to be `int` but `{type(top_k)} was passed.`")
+ return MulticlassSensitivity(num_classes, top_k, average, **kwargs)
+ if task == ClassificationTask.MULTILABEL:
+ if not isinstance(num_labels, int):
+ raise ValueError(f"`num_labels` is expected to be `int` but `{type(num_labels)} was passed.`")
+ return MultilabelSensitivity(num_labels, threshold, average, **kwargs)
+ raise ValueError(f"Task {task} not supported!")
+
+
+def _sensitivity_reduce(
+ tp: Tensor,
+ fp: Tensor,
+ tn: Tensor,
+ fn: Tensor,
+ average: Optional[Literal["binary", "micro", "macro", "weighted", "none"]],
+ multidim_average: Literal["global", "samplewise"] = "global",
+ multilabel: bool = False,
+) -> Tensor:
+ if average == "binary":
+ return _safe_divide(tp, tp + fn)
+ if average == "micro":
+ tp = tp.sum(dim=0 if multidim_average == "global" else 1)
+ fn = fn.sum(dim=0 if multidim_average == "global" else 1)
+ return _safe_divide(tp, tp + fn)
+
+ sensitivity_score = _safe_divide(tp, tp + fn)
+ return _adjust_weights_safe_divide(sensitivity_score, average, multilabel, tp, fp, fn)
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diff --git a/deepscreen/models/predictors/ark_dta.py b/deepscreen/models/predictors/ark_dta.py
new file mode 100644
index 0000000000000000000000000000000000000000..5894a1e0be3375efe7e0e690970eac8c2bf3f7f1
--- /dev/null
+++ b/deepscreen/models/predictors/ark_dta.py
@@ -0,0 +1,829 @@
+import torch
+from torch import nn
+
+
+class ArkDTA(nn.Module):
+ def __init__(self, args):
+ super(Net, self).__init__()
+ self.layer = nn.ModuleDict()
+ analysis_mode = args.analysis_mode
+ h = args.arkdta_hidden_dim
+ d = args.hp_dropout_rate
+ esm = args.arkdta_esm_model
+ esm_freeze = args.arkdta_esm_freeze
+ E = args.arkdta_ecfpvec_dim
+ L = args.arkdta_sab_depth
+ A = args.arkdta_attention_option
+ K = args.arkdta_num_heads
+ assert 'ARKMAB' in args.arkdta_residue_addon
+
+ self.layer['prot_encoder'] = FastaESM(h, esm, esm_freeze, analysis_mode)
+ self.layer['comp_encoder'] = EcfpConverter(h, L, E, analysis_mode)
+ self.layer['intg_arkmab'] = load_residue_addon(args)
+ self.layer['intg_pooling'] = load_complex_decoder(args)
+ self.layer['ba_predictor'] = AffinityMLP(h)
+ self.layer['dt_predictor'] = InteractionMLP(h)
+
+ def load_auxiliary_materials(self, **kwargs):
+ return_batch = kwargs['return_batch']
+
+ b = kwargs['atomresi_adj'].size(0)
+ x, y, z = kwargs['encoder_attention'].size()
+ logits0 = kwargs['encoder_attention'].view(x // b, b, y, z).mean(0)[:, :, :-1].sum(2).unsqueeze(
+ 2) # actual compsub
+ logits1 = kwargs['encoder_attention'].view(x // b, b, y, z).mean(0)[:, :, -1].unsqueeze(2) # inactive site
+ return_batch['task/es_pred'] = torch.cat([logits1, logits0], 2)
+ return_batch['task/es_true'] = (kwargs['atomresi_adj'].sum(1) > 0.).long().squeeze(1)
+ return_batch['mask/es_resi'] = (kwargs['atomresi_masks'].sum(1) > 0.).float().squeeze(1)
+
+ return return_batch
+
+ def forward(self, batch):
+ return_batch = dict()
+ residue_features, residue_masks, residue_fastas = batch[0], batch[1], batch[2]
+ ecfp_words, ecfp_masks = batch[3], batch[4]
+ atomresi_adj, atomresi_masks = batch[5], batch[6]
+ bav, dti, cids = batch[7], batch[8], batch[-1]
+
+ # Protein Encoder Module
+ residue_features = self.layer['prot_encoder'](X=residue_features,
+ fastas=residue_fastas,
+ masks=residue_masks)
+ residue_masks = residue_features[1]
+ residue_temps = residue_features[2]
+ protein_features = residue_features[3]
+ residue_features = residue_features[0]
+ return_batch['temp/lm_related'] = residue_temps * 0.
+
+ # Ligand Encoder Module
+ cstruct_features = self.layer['comp_encoder'](ecfp_words=ecfp_words,
+ ecfp_masks=ecfp_masks)
+ cstruct_masks = cstruct_features[1]
+ cstruct_features = cstruct_features[0]
+
+ # Protein-Ligand Integration Module (ARK-MAB)
+ residue_results = self.layer['intg_arkmab'](residue_features=residue_features, residue_masks=residue_masks,
+ ligelem_features=cstruct_features, ligelem_masks=cstruct_masks)
+ residue_features, residue_masks, attention_weights = residue_results
+ del residue_results;
+ torch.cuda.empty_cache()
+
+ # Protein-Ligand Integration Module (Pooling Layer)
+ complex_results = self.layer['intg_pooling'](residue_features=residue_features,
+ residue_masks=residue_masks,
+ attention_weights=attention_weights,
+ protein_features=protein_features)
+ binding_complex, _, _, _ = complex_results
+ del complex_results;
+ torch.cuda.empty_cache()
+
+ # Drug-Target Outcome Predictor
+ bav_predicted = self.layer['ba_predictor'](binding_complex=binding_complex)
+ dti_predicted = self.layer['dt_predictor'](binding_complex=binding_complex)
+
+ return_batch['task/ba_pred'] = bav_predicted.view(-1)
+ return_batch['task/dt_pred'] = dti_predicted.view(-1)
+ return_batch['task/ba_true'] = bav.view(-1)
+ return_batch['task/dt_true'] = dti.view(-1)
+ return_batch['meta/cid'] = cids
+
+ # Additional Materials for Calculating Auxiliary Loss
+ return_batch = self.load_auxiliary_materials(return_batch=return_batch,
+ atomresi_adj=atomresi_adj,
+ atomresi_masks=atomresi_masks,
+ encoder_attention=attention_weights)
+
+ return return_batch
+
+ @torch.no_grad()
+ def infer(self, batch):
+ return_batch = dict()
+ residue_features, residue_masks, residue_fastas = batch[0], batch[1], batch[2]
+ ecfp_words, ecfp_masks = batch[3], batch[4]
+ bav, dti, cids = batch[7], batch[8], batch[-1]
+
+ # Protein Encoder Module
+ residue_features = self.layer['prot_encoder'](X=residue_features,
+ fastas=residue_fastas,
+ masks=residue_masks)
+ residue_masks = residue_features[1]
+ residue_temps = residue_features[2]
+ protein_features = residue_features[3]
+ residue_features = residue_features[0]
+ return_batch['temp/lm_related'] = residue_temps * 0.
+
+ # Ligand Encoder Module
+ cstruct_features = self.layer['comp_encoder'](ecfp_words=ecfp_words,
+ ecfp_masks=ecfp_masks)
+ cstruct_masks = cstruct_features[1]
+ cstruct_features = cstruct_features[0]
+
+ # Protein-Ligand Integration Module (ARK-MAB)
+ residue_results = self.layer['intg_arkmab'](residue_features=residue_features, residue_masks=residue_masks,
+ ligelem_features=cstruct_features, ligelem_masks=cstruct_masks)
+ residue_features, residue_masks, attention_weights = residue_results
+ del residue_results;
+ torch.cuda.empty_cache()
+
+ # Protein-Ligand Integration Module (Pooling Layer)
+ complex_results = self.layer['intg_pooling'](residue_features=residue_features,
+ residue_masks=residue_masks,
+ attention_weights=attention_weights,
+ protein_features=protein_features)
+ binding_complex, _, _, _ = complex_results
+ del complex_results;
+ torch.cuda.empty_cache()
+
+ # Drug-Target Outcome Predictor
+ bav_predicted = self.layer['ba_predictor'](binding_complex=binding_complex)
+ dti_predicted = self.layer['dt_predictor'](binding_complex=binding_complex)
+
+ return_batch['task/ba_pred'] = bav_predicted.view(-1)
+ return_batch['task/dt_pred'] = dti_predicted.view(-1)
+ return_batch['task/ba_true'] = bav.view(-1)
+ return_batch['task/dt_true'] = dti.view(-1)
+ return_batch['meta/cid'] = cids
+
+ return return_batch
+
+
+class GraphDenseSequential(nn.Sequential):
+ def __init__(self, *args):
+ super(GraphDenseSequential, self).__init__(*args)
+
+ def forward(self, X, adj, mask):
+ for module in self._modules.values():
+ try:
+ X = module(X, adj, mask)
+ except BaseException:
+ X = module(X)
+
+ return X
+
+
+class MaskedGlobalPooling(nn.Module):
+ def __init__(self, pooling='max'):
+ super(MaskedGlobalPooling, self).__init__()
+ self.pooling = pooling
+
+ def forward(self, x, adj, masks):
+ if x.dim() == 2:
+ x = x.unsqueeze(0)
+ # print(x, adj, masks)
+ masks = masks.unsqueeze(2).repeat(1, 1, x.size(2))
+ if self.pooling == 'max':
+ x[masks == 0] = -99999.99999
+ x = x.max(1)[0]
+ elif self.pooling == 'add':
+ x = x.sum(1)
+ else:
+ print('Not Implemented')
+
+ return x
+
+
+class MaskedMean(nn.Module):
+ def __init__(self):
+ super(MaskedMean, self).__init__()
+
+ def forward(self, X, m):
+ if isinstance(m, torch.Tensor):
+ X = X * m.unsqueeze(2)
+
+ return X.mean(1)
+
+
+class MaskedMax(nn.Module):
+ def __init__(self):
+ super(MaskedMax, self).__init__()
+
+ def forward(self, X, m):
+ if isinstance(m, torch.Tensor):
+ X = X * m.unsqueeze(2)
+
+ return torch.max(X, 1)[0]
+
+
+class MaskedSum(nn.Module):
+ def __init__(self):
+ super(MaskedSum, self).__init__()
+
+ def forward(self, X, m):
+ if isinstance(m, torch.Tensor):
+ X = X * m.unsqueeze(2)
+
+ return X.sum(1)
+
+
+class MaskedScaledAverage(nn.Module):
+ def __init__(self):
+ super(MaskedScaledAverage, self).__init__()
+
+ def forward(self, X, m):
+ if isinstance(m, torch.Tensor):
+ X = X * m.unsqueeze(2)
+
+ return X.sum(1) / (m.sum(1) ** 0.5).unsqueeze(1)
+
+
+class Decoder(nn.Module):
+ def __init__(self, analysis_mode):
+ super(Decoder, self).__init__()
+ self.output_representations = []
+ self.query_representations = []
+ self.kvpair_representations = []
+ self.attention_weights = []
+
+ if analysis_mode: self.register_forward_hook(store_decoder_representations)
+
+ def show(self):
+ print("Number of Saved Numpy Arrays: ", len(self.representations))
+ for i, representation in enumerate(self.representations):
+ print(f"Shape of {i}th Numpy Array: ", representation.shape)
+
+ return self.representations
+
+ def flush(self):
+ del self.representations
+ self.representations = []
+
+ def release_qk(self):
+
+ return None
+
+ def forward(self, **kwargs):
+
+ return kwargs['X'], kwargs['X'], kwargs['residue_features'], None
+
+
+class DecoderPMA_Residue(Decoder):
+ def __init__(self, h: int, num_heads: int, num_seeds: int, attn_option: str, analysis_mode: bool):
+ super(DecoderPMA_Residue, self).__init__(analysis_mode)
+ # Aggregate the Residues into Residue Regions
+ pma_args = (h, num_seeds, num_heads, RFF(h), attn_option, False, analysis_mode, False)
+ self.decoder = PoolingMultiheadAttention(*pma_args)
+ # Model Region-Region Interaction through Set Attention
+ sab_depth = 0 if num_seeds < 4 else int((num_seeds // 2) ** 0.5)
+ sab_args = (h, num_heads, RFF(h), attn_option, False, analysis_mode, True)
+ self.pairwise = nn.ModuleList([SetAttentionBlock(*sab_args) for _ in range(sab_depth)])
+ # Concat, then reduce into h-dimensional Set Representation
+ self.aggregate = nn.Linear(h * num_seeds, h)
+
+ self.apply(initialization)
+
+ def forward(self, **kwargs):
+ residue_features = kwargs['residue_features']
+ residue_masks = kwargs['residue_masks']
+
+ output, attention = self.decoder(residue_features, residue_masks)
+ for sab in self.pairwise: output, _ = sab(output)
+ b, n, d = output.shape
+ output = self.aggregate(output.view(b, n * d))
+
+ return output, None, residue_features, attention
+
+
+class AffinityMLP(nn.Module):
+ def __init__(self, h: int):
+ super(AffinityMLP, self).__init__()
+ self.mlp = nn.Sequential(nn.Linear(h, h), nn.Dropout(0.1), nn.LeakyReLU(), nn.Linear(h, 1))
+
+ self.apply(initialization)
+
+ def forward(self, **kwargs):
+ '''
+ X: batch size x 1 x H
+ '''
+ X = kwargs['binding_complex']
+ X = X.squeeze(1) if X.dim() == 3 else X
+ yhat = self.mlp(X)
+
+ return yhat
+
+
+class InteractionMLP(nn.Module):
+ def __init__(self, h: int):
+ super(InteractionMLP, self).__init__()
+ self.mlp = nn.Sequential(nn.Linear(h, h), nn.Dropout(0.1), nn.LeakyReLU(), nn.Linear(h, 1), nn.Sigmoid())
+
+ self.apply(initialization)
+
+ def forward(self, **kwargs):
+ '''
+ X: batch size x 1 x H
+ '''
+ X = kwargs['binding_complex']
+ X = X.squeeze(1) if X.dim() == 3 else X
+ yhat = self.mlp(X)
+
+ return yhat
+
+
+class LigelemEncoder(nn.Module):
+ def __init__(self):
+ super(LigelemEncoder, self).__init__()
+ self.representations = []
+
+ def show(self):
+ print("Number of Saved Numpy Arrays: ", len(self.representations))
+ for i, representation in enumerate(self.representations):
+ print(f"Shape of {i}th Numpy Array: ", representation.shape)
+
+ return self.representations
+
+ def flush(self):
+ del self.representations
+ self.representations = []
+
+
+class EcfpConverter(LigelemEncoder):
+ def __init__(self, h: int, sab_depth: int, ecfp_dim: int, analysis_mode: bool):
+ super(EcfpConverter, self).__init__()
+ K = 4 # number of attention heads
+ self.ecfp_embeddings = nn.Embedding(ecfp_dim + 1, h, padding_idx=ecfp_dim)
+ self.encoder = nn.ModuleList([])
+ sab_args = (h, K, RFF(h), 'general_dot', False, analysis_mode, True)
+ self.encoder = nn.ModuleList([SetAttentionBlock(*sab_args) for _ in range(sab_depth)])
+
+ self.representations = []
+ if analysis_mode: self.register_forward_hook(store_elemwise_representations)
+ self.apply(initialization)
+
+ def forward(self, **kwargs):
+ '''
+ X : (b x d)
+ '''
+ ecfp_words = kwargs['ecfp_words']
+ ecfp_masks = kwargs['ecfp_masks']
+ ecfp_words = self.ecfp_embeddings(ecfp_words)
+
+ for sab in self.encoder:
+ ecfp_words, _ = sab(ecfp_words, ecfp_masks)
+
+ return [ecfp_words, ecfp_masks]
+
+
+class ResidueAddOn(nn.Module):
+ def __init__(self):
+ super(ResidueAddOn, self).__init__()
+
+ self.representations = []
+
+ def show(self):
+ print("Number of Saved Numpy Arrays: ", len(self.representations))
+ for i, representation in enumerate(self.representations):
+ print(f"Shape of {i}th Numpy Array: ", representation.shape)
+
+ return self.representations
+
+ def flush(self):
+ del self.representations
+ self.representations = []
+
+ def forward(self, **kwargs):
+ X, Xm = kwargs['X'], kwargs['Xm']
+
+ return X, Xm
+
+
+class ARKMAB(ResidueAddOn):
+ def __init__(self, h: int, num_heads: int, attn_option: str, analysis_mode: bool):
+ super(ARKMAB, self).__init__()
+ pmx_args = (h, num_heads, RFF(h), attn_option, False, analysis_mode, False)
+ self.pmx = PoolingMultiheadCrossAttention(*pmx_args)
+ self.inactive = nn.Parameter(torch.randn(1, 1, h))
+ self.fillmask = nn.Parameter(torch.ones(1, 1), requires_grad=False)
+
+ self.representations = []
+ if analysis_mode: pass
+ self.apply(initialization)
+
+ def forward(self, **kwargs):
+ '''
+ X: batch size x residues x H
+ Xm: batch size x residues x H
+ Y: batch size x ecfpsubs x H
+ Ym: batch size x ecfpsubs x H
+ '''
+ X, Xm = kwargs['residue_features'], kwargs['residue_masks']
+ Y, Ym = kwargs['ligelem_features'], kwargs['ligelem_masks']
+ pseudo_substructure = self.inactive.repeat(X.size(0), 1, 1)
+ pseudo_masks = self.fillmask.repeat(X.size(0), 1)
+
+ Y = torch.cat([Y, pseudo_substructure], 1)
+ Ym = torch.cat([Ym, pseudo_masks], 1)
+
+ X, attention = self.pmx(Y=Y, Ym=Ym, X=X, Xm=Xm)
+
+ return X, Xm, attention
+
+
+class ResidueEncoder(nn.Module):
+ def __init__(self):
+ super(ResidueEncoder, self).__init__()
+ self.representations = []
+
+ def show(self):
+ print("Number of Saved Numpy Arrays: ", len(self.representations))
+ for i, representation in enumerate(self.representations):
+ print(f"Shape of {i}th Numpy Array: ", representation.shape)
+
+ return self.representations
+
+ def flush(self):
+ del self.representations
+ self.representations = []
+
+
+class AminoAcidSeqCNN(ResidueEncoder):
+ def __init__(self, h: int, d: float, cnn_depth: int, kernel_size: int, analysis_mode: bool):
+ super(AminoAcidSeqCNN, self).__init__()
+ self.encoder = nn.ModuleList([nn.Sequential(nn.Linear(21, h), # Warning
+ nn.Dropout(d),
+ nn.LeakyReLU(),
+ nn.Linear(h, h))])
+ for _ in range(cnn_depth):
+ self.encoder.append(nn.Conv1d(h, h, kernel_size, 1, (kernel_size - 1) // 2))
+
+ self.representations = []
+ if analysis_mode: self.register_forward_hook(store_representations)
+ self.apply(initialization)
+
+ def forward(self, **kwargs):
+ X = kwargs['aaseqs']
+ for i, module in enumerate(self.encoder):
+ if i == 1: X = X.transpose(1, 2)
+ X = module(X)
+ X = X.transpose(1, 2)
+
+ return X
+
+
+class FastaESM(ResidueEncoder):
+ def __init__(self, h: int, esm_model: str, esm_freeze: bool, analysis_mode: bool):
+ super(FastaESM, self).__init__()
+ self.esm_version = 2 if 'esm2' in esm_model else 1
+ if esm_model == 'esm1b_t33_650M_UR505':
+ self.esm, alphabet = esm.pretrained.esm1b_t33_650M_UR50S()
+ self.layer_idx, self.emb_dim = 33, 1024
+ elif esm_model == 'esm1_t12_85M_UR505':
+ self.esm, alphabet = esm.pretrained.esm1_t12_85M_UR50S()
+ self.layer_idx, self.emb_dim = 12, 768
+ elif esm_model == 'esm2_t6_8M_UR50D':
+ self.esm, alphabet = esm.pretrained.esm2_t6_8M_UR50D()
+ self.layer_idx, self.emb_dim = 6, 320
+ elif esm_model == 'esm2_t12_35M_UR50D':
+ self.esm, alphabet = esm.pretrained.esm2_t12_35M_UR50D()
+ self.layer_idx, self.emb_dim = 12, 480
+ elif esm_model == 'esm2_t30_150M_UR50D':
+ self.esm, alphabet = esm.pretrained.esm2_t30_150M_UR50D()
+ self.layer_idx, self.emb_dim = 30, 640
+ else:
+ raise
+ self.batch_converter = alphabet.get_batch_converter()
+ if esm_freeze == 'True':
+ for p in self.esm.parameters():
+ p.requires_grad = False
+ assert h == self.emb_dim, f"The hidden dimension should be set to {self.emb_dim}, not {h}"
+ self.representations = []
+ if analysis_mode: self.register_forward_hook(store_elemwise_representations)
+
+ def esm1_pooling(self, embeddings, masks):
+
+ return embeddings[:, 1:, :].sum(1) / masks[:, 1:].sum(1).view(-1, 1)
+
+ def esm2_pooling(self, embeddings, masks):
+
+ return embeddings[:, 1:-1, :].sum(1) / masks[:, 1:-1].sum(1).view(-1, 1)
+
+ def forward(self, **kwargs):
+ fastas = kwargs['fastas']
+ _, _, tokenized = self.batch_converter(fastas)
+ tokenized = tokenized.cuda()
+ if self.esm_version == 2:
+ masks = torch.where(tokenized > 1, 1, 0).float()
+ else:
+ masks = torch.where((tokenized > 1) & (tokenized != 32), 1, 0).float()
+
+ embeddings = self.esm(tokenized, repr_layers=[self.layer_idx], return_contacts=True)
+ logits = embeddings["logits"].sum()
+ contacts = embeddings["contacts"].sum()
+ attentions = embeddings["attentions"].sum()
+ embeddings = embeddings["representations"][self.layer_idx]
+
+ assert masks.size(0) == embeddings.size(
+ 0), f"Batch sizes of masks {masks.size(0)} and {embeddings.size(0)} do not match."
+ assert masks.size(1) == embeddings.size(
+ 1), f"Lengths of masks {masks.size(1)} and {embeddings.size(1)} do not match."
+
+ if self.esm_version == 2:
+ return [embeddings[:, 1:-1, :], masks[:, 1:-1], logits + contacts + attentions,
+ self.esm2_pooling(embeddings, masks)]
+ else:
+ return [embeddings[:, 1:, :], masks[:, 1:], logits + contacts + attentions,
+ self.esm1_pooling(embeddings, masks)]
+
+
+class DotProduct(nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def forward(self, queries, keys):
+ return torch.bmm(queries, keys.transpose(1, 2))
+
+
+class ScaledDotProduct(nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def forward(self, queries, keys):
+ return torch.bmm(queries, keys.transpose(1, 2)) / (queries.size(2) ** 0.5)
+
+
+class GeneralDotProduct(nn.Module):
+ def __init__(self, hidden_dim):
+ super().__init__()
+ self.W = nn.Parameter(torch.randn(hidden_dim, hidden_dim))
+ torch.nn.init.orthogonal_(self.W)
+
+ def forward(self, queries, keys):
+ return torch.bmm(queries @ self.W, keys.transpose(1, 2))
+
+
+class ConcatDotProduct(nn.Module):
+ def __init__(self, hidden_dim):
+ super().__init__()
+ raise
+
+ def forward(self, queries, keys):
+ return
+
+
+class Additive(nn.Module):
+ def __init__(self, hidden_dim):
+ super().__init__()
+ self.U = nn.Parameter(torch.randn(hidden_dim, hidden_dim))
+ self.T = nn.Parameter(torch.randn(hidden_dim, hidden_dim))
+ self.b = nn.Parameter(torch.rand(hidden_dim).uniform_(-0.1, 0.1))
+ self.W = nn.Sequential(nn.Tanh(), nn.Linear(hidden_dim, 1))
+ torch.nn.init.orthogonal_(self.U)
+ torch.nn.init.orthogonal_(self.T)
+
+ def forward(self, queries, keys):
+ return self.W(queries.unsqueeze(1) @ self.U + keys.unsqueeze(2) @ self.T + self.b).squeeze(-1).transpose(1, 2)
+
+
+class Attention(nn.Module):
+ def __init__(self, similarity, hidden_dim=1024, store_qk=False):
+ super().__init__()
+ self.softmax = nn.Softmax(dim=2)
+ self.attention_maps = []
+ self.store_qk = store_qk
+ self.query_vectors, self.key_vectors = None, None
+
+ assert similarity in ['dot', 'scaled_dot', 'general_dot', 'concat_dot', 'additive']
+ if similarity == 'dot':
+ self.similarity = DotProduct()
+ elif similarity == 'scaled_dot':
+ self.similarity = ScaledDotProduct()
+ elif similarity == 'general_dot':
+ self.similarity = GeneralDotProduct(hidden_dim)
+ elif similarity == 'concat_dot':
+ self.similarity = ConcatDotProduct(hidden_dim)
+ elif similarity == 'additive':
+ self.similarity = Additive(hidden_dim)
+ else:
+ raise
+
+ def release_qk(self):
+ Q, K = self.query_vectors, self.key_vectors
+ self.query_vectors, self.key_vectors = None, None
+ torch.cuda.empty_cache()
+
+ return Q, K
+
+ def forward(self, queries, keys, qmasks=None, kmasks=None):
+ if self.store_qk:
+ self.query_vectors = queries
+ self.key_vectors = keys
+
+ if torch.is_tensor(qmasks) and not torch.is_tensor(kmasks):
+ dim0, dim1 = qmasks.size(0), keys.size(1)
+ kmasks = torch.ones(dim0, dim1).cuda()
+
+ elif not torch.is_tensor(qmasks) and torch.is_tensor(kmasks):
+ dim0, dim1 = kmasks.size(0), queries.size(1)
+ qmasks = torch.ones(dim0, dim1).cuda()
+ else:
+ pass
+
+ attention = self.similarity(queries, keys)
+ if torch.is_tensor(qmasks) and torch.is_tensor(kmasks):
+ qmasks = qmasks.repeat(queries.size(0) // qmasks.size(0), 1).unsqueeze(2)
+ kmasks = kmasks.repeat(keys.size(0) // kmasks.size(0), 1).unsqueeze(2)
+ attnmasks = torch.bmm(qmasks, kmasks.transpose(1, 2))
+ attention = torch.clip(attention, min=-10, max=10)
+ attention = attention.exp()
+ attention = attention * attnmasks
+ attention = attention / (attention.sum(2).unsqueeze(2) + 1e-5)
+ else:
+ attention = self.softmax(attention)
+
+ return attention
+
+
+@torch.no_grad()
+def save_attention_maps(self, input, output):
+ self.attention_maps.append(output.data.detach().cpu().numpy())
+
+
+class MultiheadAttention(nn.Module):
+ def __init__(self, d, h, sim='dot', analysis=False, store_qk=False):
+ super().__init__()
+ assert d % h == 0, f"{d} dimension, {h} heads"
+ self.h = h
+ p = d // h
+
+ self.project_queries = nn.Linear(d, d)
+ self.project_keys = nn.Linear(d, d)
+ self.project_values = nn.Linear(d, d)
+ self.concatenation = nn.Linear(d, d)
+ self.attention = Attention(sim, p, store_qk)
+
+ if analysis:
+ self.attention.register_forward_hook(save_attention_maps)
+
+ def release_qk(self):
+ Q, K = self.attention.release_qk()
+
+ Qb = Q.size(0) // self.h
+ Qn, Qd = Q.size(1), Q.size(2)
+
+ Kb = K.size(0) // self.h
+ Kn, Kd = K.size(1), K.size(2)
+
+ Q = Q.view(self.h, Qb, Qn, Qd)
+ K = K.view(self.h, Kb, Kn, Kd)
+
+ Q = Q.permute(1, 2, 0, 3).contiguous().view(Qb, Qn, Qd * self.h)
+ K = K.permute(1, 2, 0, 3).contiguous().view(Kb, Kn, Kd * self.h)
+
+ return Q, K
+
+ def forward(self, queries, keys, values, qmasks=None, kmasks=None):
+ h = self.h
+ b, n, d = queries.size()
+ _, m, _ = keys.size()
+ p = d // h
+
+ queries = self.project_queries(queries) # shape [b, n, d]
+ keys = self.project_keys(keys) # shape [b, m, d]
+ values = self.project_values(values) # shape [b, m, d]
+
+ queries = queries.view(b, n, h, p)
+ keys = keys.view(b, m, h, p)
+ values = values.view(b, m, h, p)
+
+ queries = queries.permute(2, 0, 1, 3).contiguous().view(h * b, n, p)
+ keys = keys.permute(2, 0, 1, 3).contiguous().view(h * b, m, p)
+ values = values.permute(2, 0, 1, 3).contiguous().view(h * b, m, p)
+
+ attn_w = self.attention(queries, keys, qmasks, kmasks) # shape [h * b, n, p]
+ output = torch.bmm(attn_w, values)
+ output = output.view(h, b, n, p)
+ output = output.permute(1, 2, 0, 3).contiguous().view(b, n, d)
+ output = self.concatenation(output) # shape [b, n, d]
+
+ return output, attn_w
+
+
+class MultiheadAttentionExpanded(nn.Module):
+ def __init__(self, d, h, sim='dot', analysis=False):
+ super().__init__()
+ self.project_queries = nn.ModuleList([nn.Linear(d, d) for _ in range(h)])
+ self.project_keys = nn.ModuleList([nn.Linear(d, d) for _ in range(h)])
+ self.project_values = nn.ModuleList([nn.Linear(d, d) for _ in range(h)])
+ self.concatenation = nn.Linear(h * d, d)
+ self.attention = Attention(sim, d)
+
+ if analysis:
+ self.attention.register_forward_hook(save_attention_maps)
+
+ def forward(self, queries, keys, values, qmasks=None, kmasks=None):
+ output = []
+ for Wq, Wk, Wv in zip(self.project_queries, self.project_keys, self.project_values):
+ Pq, Pk, Pv = Wq(queries), Wk(keys), Wv(values)
+ output.append(torch.bmm(self.attention(Pq, Pk, qmasks, kmasks), Pv))
+
+ output = self.concatenation(torch.cat(output, 1))
+
+ return output
+
+
+class EmptyModule(nn.Module):
+ def __init__(self, args):
+ super().__init__()
+
+ def forward(self, x):
+ return 0.
+
+
+class RFF(nn.Module):
+ def __init__(self, h):
+ super().__init__()
+ self.rff = nn.Sequential(nn.Linear(h, h), nn.ReLU(), nn.Linear(h, h), nn.ReLU(), nn.Linear(h, h), nn.ReLU())
+
+ def forward(self, x):
+ return self.rff(x)
+
+
+class MultiheadAttentionBlock(nn.Module):
+ def __init__(self, d, h, rff, similarity='dot', full_head=False, analysis=False, store_qk=False):
+ super().__init__()
+ self.multihead = MultiheadAttention(d, h, similarity, analysis,
+ store_qk) if not full_head else MultiheadAttentionExpanded(d, h, similarity,
+ analysis)
+ self.layer_norm1 = nn.LayerNorm(d)
+ self.layer_norm2 = nn.LayerNorm(d)
+ self.rff = rff
+
+ def release_qk(self):
+ Q, K = self.multihead.release_qk()
+
+ return Q, K
+
+ def forward(self, x, y, xm=None, ym=None, layer_norm=True):
+ h, a = self.multihead(x, y, y, xm, ym)
+ if layer_norm:
+ h = self.layer_norm1(x + h)
+ return self.layer_norm2(h + self.rff(h)), a
+ else:
+ h = x + h
+ return h + self.rff(h), a
+
+
+class SetAttentionBlock(nn.Module):
+ def __init__(self, d, h, rff, similarity='dot', full_head=False, analysis=False, store_qk=False):
+ super().__init__()
+ self.mab = MultiheadAttentionBlock(d, h, rff, similarity, full_head, analysis, store_qk)
+
+ def release_qk(self):
+ Q, K = self.mab.release_qk()
+
+ return Q, K
+
+ def forward(self, x, m=None, ln=True):
+ return self.mab(x, x, m, m, ln)
+
+
+class InducedSetAttentionBlock(nn.Module):
+ def __init__(self, d, m, h, rff1, rff2, similarity='dot', full_head=False, analysis=False, store_qk=False):
+ super().__init__()
+ self.mab1 = MultiheadAttentionBlock(d, h, rff1, similarity, full_head, analysis, store_qk)
+ self.mab2 = MultiheadAttentionBlock(d, h, rff2, similarity, full_head, analysis, store_qk)
+ self.inducing_points = nn.Parameter(torch.randn(1, m, d))
+
+ def release_qk(self):
+ raise NotImplemented
+
+ def forward(self, x, m=None, ln=True):
+ b = x.size(0)
+ p = self.inducing_points
+ p = p.repeat([b, 1, 1]) # shape [b, m, d]
+ h = self.mab1(p, x, None, m, ln) # shape [b, m, d]
+
+ return self.mab2(x, h, m, None, ln)
+
+
+class PoolingMultiheadAttention(nn.Module):
+ def __init__(self, d, k, h, rff, similarity='dot', full_head=False, analysis=False, store_qk=False):
+ super().__init__()
+ self.mab = MultiheadAttentionBlock(d, h, rff, similarity, full_head, analysis, store_qk)
+ self.seed_vectors = nn.Parameter(torch.randn(1, k, d))
+ torch.nn.init.xavier_uniform_(self.seed_vectors)
+
+ def release_qk(self):
+ Q, K = self.mab.release_qk()
+
+ return Q, K
+
+ def forward(self, z, m=None, ln=True):
+ b = z.size(0)
+ s = self.seed_vectors
+ s = s.repeat([b, 1, 1]) # random seed vector: shape [b, k, d]
+
+ return self.mab(s, z, None, m, ln)
+
+
+class PoolingMultiheadCrossAttention(nn.Module):
+ def __init__(self, d, h, rff, similarity='dot', full_head=False, analysis=False, store_qk=False):
+ super().__init__()
+ self.mab = MultiheadAttentionBlock(d, h, rff, similarity, full_head, analysis, store_qk)
+
+ def release_qk(self):
+ Q, K = self.mab.release_qk()
+
+ return Q, K
+
+ def forward(self, X, Y, Xm=None, Ym=None, ln=True):
+ return self.mab(X, Y, Xm, Ym, ln)
diff --git a/deepscreen/models/predictors/bacpi.py b/deepscreen/models/predictors/bacpi.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff732fa9a784048e09aba8fba2723c3832dd5f8c
--- /dev/null
+++ b/deepscreen/models/predictors/bacpi.py
@@ -0,0 +1,284 @@
+from collections import defaultdict
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from rdkit import Chem
+from rdkit.Chem import AllChem
+
+
+class BACPI(nn.Module):
+ def __init__(
+ self,
+ n_atom,
+ n_amino,
+ comp_dim,
+ prot_dim,
+ gat_dim,
+ num_head,
+ dropout,
+ alpha,
+ window,
+ layer_cnn,
+ latent_dim,
+ ):
+ super().__init__()
+ self.embedding_layer_atom = nn.Embedding(n_atom + 1, comp_dim)
+ self.embedding_layer_amino = nn.Embedding(n_amino + 1, prot_dim)
+
+ self.dropout = dropout
+ self.alpha = alpha
+ self.layer_cnn = layer_cnn
+
+ self.gat_layers = [GATLayer(comp_dim, gat_dim, dropout=dropout, alpha=alpha, concat=True)
+ for _ in range(num_head)]
+ for i, layer in enumerate(self.gat_layers):
+ self.add_module('gat_layer_{}'.format(i), layer)
+ self.gat_out = GATLayer(gat_dim * num_head, comp_dim, dropout=dropout, alpha=alpha, concat=False)
+ self.W_comp = nn.Linear(comp_dim, latent_dim)
+
+ self.conv_layers = nn.ModuleList([nn.Conv2d(in_channels=1, out_channels=1, kernel_size=2 * window + 1,
+ stride=1, padding=window) for _ in range(layer_cnn)])
+ self.W_prot = nn.Linear(prot_dim, latent_dim)
+
+ self.fp0 = nn.Parameter(torch.empty(size=(1024, latent_dim)))
+ nn.init.xavier_uniform_(self.fp0, gain=1.414)
+ self.fp1 = nn.Parameter(torch.empty(size=(latent_dim, latent_dim)))
+ nn.init.xavier_uniform_(self.fp1, gain=1.414)
+
+ self.bidat_num = 4
+
+ self.U = nn.ParameterList([
+ nn.Parameter(torch.empty(size=(latent_dim, latent_dim))) for _ in range(self.bidat_num)
+ ])
+ for i in range(self.bidat_num):
+ nn.init.xavier_uniform_(self.U[i], gain=1.414)
+
+ self.transform_c2p = nn.ModuleList([nn.Linear(latent_dim, latent_dim) for _ in range(self.bidat_num)])
+ self.transform_p2c = nn.ModuleList([nn.Linear(latent_dim, latent_dim) for _ in range(self.bidat_num)])
+
+ self.bihidden_c = nn.ModuleList([nn.Linear(latent_dim, latent_dim) for _ in range(self.bidat_num)])
+ self.bihidden_p = nn.ModuleList([nn.Linear(latent_dim, latent_dim) for _ in range(self.bidat_num)])
+ self.biatt_c = nn.ModuleList([nn.Linear(latent_dim * 2, 1) for _ in range(self.bidat_num)])
+ self.biatt_p = nn.ModuleList([nn.Linear(latent_dim * 2, 1) for _ in range(self.bidat_num)])
+
+ self.comb_c = nn.Linear(latent_dim * self.bidat_num, latent_dim)
+ self.comb_p = nn.Linear(latent_dim * self.bidat_num, latent_dim)
+
+ def comp_gat(self, atoms, atoms_mask, adj):
+ atoms_vector = self.embedding_layer_atom(atoms)
+ atoms_multi_head = torch.cat([gat(atoms_vector, adj) for gat in self.gat_layers], dim=2)
+ atoms_vector = F.elu(self.gat_out(atoms_multi_head, adj))
+ atoms_vector = F.leaky_relu(self.W_comp(atoms_vector), self.alpha)
+ return atoms_vector
+
+ def prot_cnn(self, amino, amino_mask):
+ amino_vector = self.embedding_layer_amino(amino)
+ amino_vector = torch.unsqueeze(amino_vector, 1)
+ for i in range(self.layer_cnn):
+ amino_vector = F.leaky_relu(self.conv_layers[i](amino_vector), self.alpha)
+ amino_vector = torch.squeeze(amino_vector, 1)
+ amino_vector = F.leaky_relu(self.W_prot(amino_vector), self.alpha)
+ return amino_vector
+
+ def mask_softmax(self, a, mask, dim=-1):
+ a_max = torch.max(a, dim, keepdim=True)[0]
+ a_exp = torch.exp(a - a_max)
+ a_exp = a_exp * mask
+ a_softmax = a_exp / (torch.sum(a_exp, dim, keepdim=True) + 1e-6)
+ return a_softmax
+
+ def bidirectional_attention_prediction(self, atoms_vector, atoms_mask, fps, amino_vector, amino_mask):
+ b = atoms_vector.shape[0]
+ for i in range(self.bidat_num):
+ A = torch.tanh(torch.matmul(torch.matmul(atoms_vector, self.U[i]), amino_vector.transpose(1, 2)))
+ A = A * torch.matmul(atoms_mask.view(b, -1, 1).float(), amino_mask.view(b, 1, -1).float())
+
+ atoms_trans = torch.matmul(A, torch.tanh(self.transform_p2c[i](amino_vector)))
+ amino_trans = torch.matmul(A.transpose(1, 2), torch.tanh(self.transform_c2p[i](atoms_vector)))
+
+ atoms_tmp = torch.cat([torch.tanh(self.bihidden_c[i](atoms_vector)), atoms_trans], dim=2)
+ amino_tmp = torch.cat([torch.tanh(self.bihidden_p[i](amino_vector)), amino_trans], dim=2)
+
+ atoms_att = self.mask_softmax(self.biatt_c[i](atoms_tmp).view(b, -1), atoms_mask.view(b, -1).float())
+ amino_att = self.mask_softmax(self.biatt_p[i](amino_tmp).view(b, -1), amino_mask.view(b, -1).float())
+
+ cf = torch.sum(atoms_vector * atoms_att.view(b, -1, 1), dim=1)
+ pf = torch.sum(amino_vector * amino_att.view(b, -1, 1), dim=1)
+
+ if i == 0:
+ cat_cf = cf
+ cat_pf = pf
+ else:
+ cat_cf = torch.cat([cat_cf.view(b, -1), cf.view(b, -1)], dim=1)
+ cat_pf = torch.cat([cat_pf.view(b, -1), pf.view(b, -1)], dim=1)
+
+ cf_final = torch.cat([self.comb_c(cat_cf).view(b, -1), fps.view(b, -1)], dim=1)
+ pf_final = self.comb_p(cat_pf)
+ cf_pf = F.leaky_relu(
+ torch.matmul(
+ cf_final.view(b, -1, 1), pf_final.view(b, 1, -1)
+ ).view(b, -1), 0.1
+ )
+
+ return cf_pf
+
+ def forward(self, compound, protein):
+ atom, adj, fp = compound
+
+ atom, atom_lengths = atom
+ adj, _ = adj
+ fp, _ = fp
+ amino, amino_lengths = protein
+
+ atom_mask = torch.arange(atom.size(1), device=atom.device) >= atom_lengths.unsqueeze(1)
+ amino_mask = torch.arange(amino.size(1), device=amino.device) >= amino_lengths.unsqueeze(1)
+
+ atoms_vector = self.comp_gat(atom, atom_mask, adj)
+ amino_vector = self.prot_cnn(amino, amino_mask)
+
+ super_feature = F.leaky_relu(torch.matmul(fp.float(), self.fp0), 0.1)
+ super_feature = F.leaky_relu(torch.matmul(super_feature, self.fp1), 0.1)
+
+ prediction = self.bidirectional_attention_prediction(
+ atoms_vector, atom_mask, super_feature, amino_vector, amino_mask)
+
+ return prediction
+
+
+class GATLayer(nn.Module):
+ def __init__(self, in_features, out_features, dropout=0.5, alpha=0.2, concat=True):
+ super().__init__()
+ self.in_features = in_features
+ self.out_features = out_features
+ self.dropout = dropout
+ self.alpha = alpha
+ self.concat = concat
+
+ self.W = nn.Parameter(torch.empty(size=(in_features, out_features)))
+ nn.init.xavier_uniform_(self.W.data, gain=1.414)
+ self.a = nn.Parameter(torch.empty(size=(2 * out_features, 1)))
+ nn.init.xavier_uniform_(self.a.data, gain=1.414)
+
+ def forward(self, h, adj):
+ Wh = torch.matmul(h, self.W)
+ a_input = self._prepare_attentional_mechanism_input(Wh)
+ e = F.leaky_relu(torch.matmul(a_input, self.a).squeeze(3), self.alpha)
+
+ zero_vec = -9e15 * torch.ones_like(e)
+ attention = torch.where(adj > 0, e, zero_vec)
+ attention = F.softmax(attention, dim=2)
+ # attention = F.dropout(attention, self.dropout, training=self.training)
+ h_prime = torch.bmm(attention, Wh)
+
+ return F.elu(h_prime) if self.concat else h_prime
+
+ def _prepare_attentional_mechanism_input(self, Wh):
+ b = Wh.size()[0]
+ N = Wh.size()[1]
+
+ Wh_repeated_in_chunks = Wh.repeat_interleave(N, dim=1)
+ Wh_repeated_alternating = Wh.repeat_interleave(N, dim=0).view(b, N * N, self.out_features)
+ all_combinations_matrix = torch.cat([Wh_repeated_in_chunks, Wh_repeated_alternating], dim=2)
+
+ return all_combinations_matrix.view(b, N, N, 2 * self.out_features)
+
+
+atom_dict = defaultdict(lambda: len(atom_dict))
+bond_dict = defaultdict(lambda: len(bond_dict))
+fingerprint_dict = defaultdict(lambda: len(fingerprint_dict))
+edge_dict = defaultdict(lambda: len(edge_dict))
+word_dict = defaultdict(lambda: len(word_dict))
+
+
+def create_atoms(mol):
+ atoms = [a.GetSymbol() for a in mol.GetAtoms()]
+ for a in mol.GetAromaticAtoms():
+ i = a.GetIdx()
+ atoms[i] = (atoms[i], 'aromatic')
+ atoms = [atom_dict[a] for a in atoms]
+ return np.array(atoms)
+
+
+def create_ijbonddict(mol):
+ i_jbond_dict = defaultdict(lambda: [])
+ for b in mol.GetBonds():
+ i, j = b.GetBeginAtomIdx(), b.GetEndAtomIdx()
+ bond = bond_dict[str(b.GetBondType())]
+ i_jbond_dict[i].append((j, bond))
+ i_jbond_dict[j].append((i, bond))
+
+ atoms_set = set(range(mol.GetNumAtoms()))
+ isolate_atoms = atoms_set - set(i_jbond_dict.keys())
+ bond = bond_dict['nan']
+ for a in isolate_atoms:
+ i_jbond_dict[a].append((a, bond))
+
+ return i_jbond_dict
+
+
+def atom_features(atoms, i_jbond_dict, radius):
+ if (len(atoms) == 1) or (radius == 0):
+ fingerprints = [fingerprint_dict[a] for a in atoms]
+ else:
+ nodes = atoms
+ i_jedge_dict = i_jbond_dict
+ for _ in range(radius):
+ fingerprints = []
+ for i, j_edge in i_jedge_dict.items():
+ neighbors = [(nodes[j], edge) for j, edge in j_edge]
+ fingerprint = (nodes[i], tuple(sorted(neighbors)))
+ fingerprints.append(fingerprint_dict[fingerprint])
+
+ nodes = fingerprints
+ _i_jedge_dict = defaultdict(lambda: [])
+ for i, j_edge in i_jedge_dict.items():
+ for j, edge in j_edge:
+ both_side = tuple(sorted((nodes[i], nodes[j])))
+ edge = edge_dict[(both_side, edge)]
+ _i_jedge_dict[i].append((j, edge))
+ i_jedge_dict = _i_jedge_dict
+
+ return np.array(fingerprints)
+
+
+def create_adjacency(mol):
+ adjacency = Chem.GetAdjacencyMatrix(mol)
+ adjacency = np.array(adjacency)
+ adjacency += np.eye(adjacency.shape[0], dtype=int)
+ return adjacency
+
+
+def get_fingerprints(mol):
+ fp = AllChem.GetMorganFingerprintAsBitVect(mol, 2, nBits=1024, useChirality=True)
+ return np.array(fp)
+
+
+def split_sequence(sequence, ngram=3):
+ sequence = '-' + sequence + '='
+ words = [word_dict[sequence[i:i + ngram]]
+ for i in range(len(sequence) - ngram + 1)]
+ return np.array(words)
+
+
+def drug_featurizer(smiles, radius=2):
+ from deepscreen.utils import get_logger
+ log = get_logger(__name__)
+ try:
+ mol = Chem.MolFromSmiles(smiles)
+ if not mol:
+ return None
+ mol = Chem.AddHs(mol)
+ atoms = create_atoms(mol)
+ i_jbond_dict = create_ijbonddict(mol)
+
+ compound = atom_features(atoms, i_jbond_dict, radius)
+ adjacency = create_adjacency(mol)
+ fp = get_fingerprints(mol)
+
+ return compound, adjacency, fp
+
+ except Exception as e:
+ log.warning(f"Failed to featurize SMILES ({smiles}) to graph due to {str(e)}")
+ return None
diff --git a/deepscreen/models/predictors/coa_dti_pro.py b/deepscreen/models/predictors/coa_dti_pro.py
new file mode 100644
index 0000000000000000000000000000000000000000..343f0eb3b485392deb5230b0712d80ab2c2961fe
--- /dev/null
+++ b/deepscreen/models/predictors/coa_dti_pro.py
@@ -0,0 +1,386 @@
+import math
+from collections import defaultdict
+from typing import Literal
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from rdkit import Chem
+from scipy.sparse import coo_matrix
+from torch_geometric.data import Data
+from torch_geometric.nn.pool.topk_pool import TopKPooling
+from torch_geometric.nn.glob import global_mean_pool as gap, global_max_pool as gmp
+from torch_geometric.utils import add_self_loops, remove_self_loops
+from torch_geometric.nn.conv.message_passing import MessagePassing
+
+
+class CoaDTIPro(nn.Module):
+ def __init__(self,
+ esm_model_and_alphabet, n_fingerprint, dim, n_word, layer_output, layer_coa, nhead=8, dropout=0.1,
+ co_attention: Literal['stack', 'encoder', 'inter'] = 'inter', gcn_pooling=False, ):
+ super().__init__()
+ self.co_attention = co_attention
+ self.layer_output = layer_output
+ self.layer_coa = layer_coa
+ self.embed_word = nn.Embedding(n_word, dim)
+ self.gnn = GNN(n_fingerprint, gcn_pooling)
+ self.esm_model, self.alphabet = esm_model_and_alphabet
+ self.batch_converter = self.alphabet.get_batch_converter()
+
+ self.W_attention = nn.Linear(dim, dim)
+
+ self.W_out = nn.Sequential(
+ nn.Linear(2 * dim, dim),
+ nn.Linear(dim, 128),
+ nn.Linear(128, 64)
+ )
+
+ self.coa_layers = CoAttention(dim, nhead, dropout, layer_coa, co_attention)
+ self.lin = nn.Linear(768, 512) # bert1024 esm768
+ self.W_interaction = nn.Linear(64, 2)
+
+ def attention_cnn(self, x, xs, layer):
+ """The attention mechanism is applied to the last layer of CNN."""
+ xs = torch.unsqueeze(torch.unsqueeze(xs, 0), 0)
+ for i in range(layer):
+ xs = torch.relu(self.W_cnn[i](xs))
+ xs = torch.squeeze(torch.squeeze(xs, 0), 0)
+
+ h = torch.relu(self.W_attention(x))
+ hs = torch.relu(self.W_attention(xs))
+ weights = torch.tanh(F.linear(h, hs))
+ ys = torch.t(weights) * hs
+
+ return torch.unsqueeze(torch.mean(ys, 0), 0)
+
+ def forward(self, inputs, proteins):
+ """Compound vector with GNN."""
+ compound_vector = self.gnn(inputs)
+ compound_vector = torch.unsqueeze(compound_vector, 0) # sequence-like GNN ouput
+
+ _, _, proteins = self.batch_converter([(None, protein) for protein in proteins])
+ with torch.no_grad():
+ results = self.esm_model(proteins.to(compound_vector.device), repr_layers=[6])
+ token_representations = results["representations"][6]
+
+ protein_vector = token_representations[:, 1:, :]
+ protein_vector = self.lin(torch.squeeze(protein_vector, 1))
+
+ protein_vector, compound_vector = self.coa_layers(protein_vector, compound_vector)
+
+ protein_vector = protein_vector.mean(dim=1)
+ compound_vector = compound_vector.mean(dim=1)
+ """Concatenate the above two vectors and output the interaction."""
+ cat_vector = torch.cat((compound_vector, protein_vector), 1)
+ cat_vector = torch.tanh(self.W_out(cat_vector))
+ interaction = self.W_interaction(cat_vector)
+ return interaction
+
+
+class CoAttention(nn.Module):
+ def __init__(self, dim, nhead, dropout, layer_coa, co_attention):
+ super().__init__()
+ self.co_attention = co_attention
+ if self.co_attention == 'encoder':
+ self.coa_layers = EncoderCrossAtt(dim, nhead, dropout, layer_coa)
+ elif self.co_attention == 'stack':
+ self.coa_layers = nn.ModuleList([StackCrossAtt(dim, nhead, dropout) for _ in range(layer_coa)])
+ elif self.co_attention == 'inter':
+ self.coa_layers = nn.ModuleList([InterCrossAtt(dim, nhead, dropout) for _ in range(layer_coa)])
+
+ def forward(self, protein_vector, compound_vector):
+ # x and y are the input tensors for the two modalities
+ # edge_index_x and edge_index_y are the edge indices for the graph data
+ if self.co_attention == 'encoder':
+ return self.coa_layers(protein_vector, compound_vector)
+ else:
+ # loop over the sequential layers and pass the arguments
+ for layer in self.coa_layers:
+ protein_vector, compound_vector = layer(protein_vector, compound_vector)
+ return protein_vector, compound_vector
+
+
+class EncoderCrossAtt(nn.Module):
+ def __init__(self, dim, nhead, dropout, layers):
+ super().__init__()
+ # self.encoder_layers = nn.ModuleList([SEA(dim, dropout) for _ in range(layers)])
+ self.encoder_layers = nn.ModuleList([SA(dim, nhead, dropout) for _ in range(layers)])
+ self.decoder_sa = nn.ModuleList([SA(dim, nhead, dropout) for _ in range(layers)])
+ self.decoder_coa = nn.ModuleList([DPA(dim, nhead, dropout) for _ in range(layers)])
+ self.layer_coa = layers
+
+ def forward(self, protein_vector, compound_vector):
+ for i in range(self.layer_coa):
+ compound_vector = self.encoder_layers[i](compound_vector, None) # self-attention
+ for i in range(self.layer_coa):
+ protein_vector = self.decoder_sa[i](protein_vector, None)
+ protein_vector = self.decoder_coa[i](protein_vector, compound_vector, None)# co-attention
+
+ return protein_vector, compound_vector
+
+
+class InterCrossAtt(nn.Module):
+ def __init__(self, dim, nhead, dropout):
+ super().__init__()
+ self.sca = SA(dim, nhead, dropout)
+ self.spa = SA(dim, nhead, dropout)
+ self.coa_pc = DPA(dim, nhead, dropout)
+ self.coa_cp = DPA(dim, nhead, dropout)
+
+ def forward(self, protein_vector, compound_vector):
+ compound_vector = self.sca(compound_vector, None) # self-attention
+ protein_vector = self.spa(protein_vector, None) # self-attention
+ compound_covector = self.coa_pc(compound_vector, protein_vector, None) # co-attention
+ protein_covector = self.coa_cp(protein_vector, compound_vector, None) # co-attention
+
+ return protein_covector, compound_covector
+
+
+class StackCrossAtt(nn.Module):
+ def __init__(self, dim, nhead, dropout):
+ super().__init__()
+ self.sca = SA(dim, nhead, dropout)
+ self.spa = SA(dim, nhead, dropout)
+ self.coa_cp = DPA(dim, nhead, dropout)
+
+ def forward(self, protein_vector, compound_vector):
+ compound_vector = self.sca(compound_vector, None) # self-attention
+ protein_vector = self.spa(protein_vector, None) # self-attention
+ protein_covector = self.coa_cp(protein_vector, compound_vector, None) # co-attention
+
+ return protein_covector, compound_vector
+
+
+class MHAtt(nn.Module):
+ def __init__(self, hid_dim, n_heads, dropout):
+ super().__init__()
+
+ self.linear_v = nn.Linear(hid_dim, hid_dim)
+ self.linear_k = nn.Linear(hid_dim, hid_dim)
+ self.linear_q = nn.Linear(hid_dim, hid_dim)
+ self.linear_merge = nn.Linear(hid_dim, hid_dim)
+ self.hid_dim = hid_dim
+ self.dropout = dropout
+ self.nhead = n_heads
+
+ self.dropout = nn.Dropout(dropout)
+ self.hidden_size_head = int(self.hid_dim / self.nhead)
+
+ def forward(self, v, k, q, mask):
+ n_batches = q.size(0)
+ v = self.linear_v(v).view(n_batches, -1, self.nhead, self.hidden_size_head).transpose(1, 2)
+ k = self.linear_k(k).view(n_batches, -1, self.nhead, self.hidden_size_head).transpose(1, 2)
+ q = self.linear_q(q).view(n_batches, -1, self.nhead, self.hidden_size_head).transpose(1, 2)
+
+ atted = self.att(v, k, q, mask)
+ atted = atted.transpose(1, 2).contiguous().view(n_batches, -1, self.hid_dim)
+
+ atted = self.linear_merge(atted)
+
+ return atted
+
+ def att(self, value, key, query, mask):
+ d_k = query.size(-1)
+
+ scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
+
+ if mask is not None:
+ scores = scores.masked_fill(mask, -1e9)
+
+ att_map = F.softmax(scores, dim=-1)
+ att_map = self.dropout(att_map)
+
+ return torch.matmul(att_map, value)
+
+
+class DPA(nn.Module):
+ def __init__(self, hid_dim, n_heads, dropout):
+ super().__init__()
+
+ self.mhatt1 = MHAtt(hid_dim, n_heads, dropout)
+ self.dropout1 = nn.Dropout(dropout)
+ self.norm1 = nn.LayerNorm(hid_dim)
+
+ def forward(self, x, y, y_mask=None):
+ x = self.norm1(x + self.dropout1(self.mhatt1(y, y, x, y_mask)))
+ return x
+
+
+class SA(nn.Module):
+ def __init__(self, hid_dim, n_heads, dropout):
+ super().__init__()
+
+ self.mhatt1 = MHAtt(hid_dim, n_heads, dropout)
+ self.dropout1 = nn.Dropout(dropout)
+ self.norm1 = nn.LayerNorm(hid_dim)
+
+ def forward(self, x, mask=None):
+ x = self.norm1(x + self.dropout1(self.mhatt1(x, x, x, mask)))
+ return x
+
+
+class SAGEConv(MessagePassing):
+ def __init__(self, in_channels, out_channels):
+ super().__init__(aggr='max') # "Max" aggregation.
+ self.lin = torch.nn.Linear(in_channels, out_channels)
+ self.act = torch.nn.ReLU()
+ self.update_lin = torch.nn.Linear(in_channels + out_channels, in_channels, bias=False)
+ self.update_act = torch.nn.ReLU()
+
+ def forward(self, x, edge_index):
+ # x has shape [N, in_channels]
+ # edge_index has shape [2, E]
+ edge_index, _ = remove_self_loops(edge_index)
+ edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))
+
+ return self.propagate(edge_index, size=(x.size(0), x.size(0)), x=x)
+
+ def message(self, x_j):
+ # x_j has shape [E, in_channels]
+ x_j = self.lin(x_j)
+ x_j = self.act(x_j)
+
+ return x_j
+
+ def update(self, aggr_out, x):
+ # aggr_out has shape [N, out_channels]
+ new_embedding = torch.cat([aggr_out, x], dim=1)
+
+ new_embedding = self.update_lin(new_embedding)
+ new_embedding = self.update_act(new_embedding)
+
+ return new_embedding
+
+
+class GNN(nn.Module):
+ def __init__(self, n_fingerprint, pooling, embed_dim=128):
+ super().__init__()
+ self.pooling = pooling
+ self.embed_fingerprint = nn.Embedding(num_embeddings=n_fingerprint, embedding_dim=embed_dim)
+ self.conv1 = SAGEConv(embed_dim, 128)
+ self.pool1 = TopKPooling(128, ratio=0.8)
+ self.conv2 = SAGEConv(128, 128)
+ self.pool2 = TopKPooling(128, ratio=0.8)
+ self.conv3 = SAGEConv(128, 128)
+ self.pool3 = TopKPooling(128, ratio=0.8)
+ self.linp1 = torch.nn.Linear(256, 128)
+ self.linp2 = torch.nn.Linear(128, 512)
+
+ self.lin = torch.nn.Linear(128, 512)
+ self.bn1 = torch.nn.BatchNorm1d(128)
+ self.bn2 = torch.nn.BatchNorm1d(64)
+ self.act1 = torch.nn.ReLU()
+ self.act2 = torch.nn.ReLU()
+
+ def forward(self, data):
+ # x, edge_index, batch = data.x, data.edge_index, data.batch
+ x, edge_index, batch = data.x, data.edge_index, data.batch
+ x = self.embed_fingerprint(x)
+ x = x.squeeze(1)
+ x = F.relu(self.conv1(x, edge_index))
+
+ if self.pooling:
+ x, edge_index, _, batch, _, _ = self.pool1(x, edge_index, None, batch)
+ x1 = torch.cat([gmp(x, batch), gap(x, batch)], dim=1)
+
+ x = F.relu(self.conv2(x, edge_index))
+
+ x, edge_index, _, batch, _, _ = self.pool2(x, edge_index, None, batch)
+ x2 = torch.cat([gmp(x, batch), gap(x, batch)], dim=1)
+
+ x, edge_index, _, batch, _, _ = self.pool3(x, edge_index, None, batch)
+ x3 = torch.cat([gmp(x, batch), gap(x, batch)], dim=1)
+
+ x = x1 + x2 + x3
+ x = self.linp1(x)
+ x = self.act1(x)
+ x = self.linp2(x)
+
+ else:
+ x = F.relu(self.conv2(x, edge_index))
+ x = self.lin(x)
+
+ return x
+
+
+atom_dict = defaultdict(lambda: len(atom_dict)) # 51 bindingdb: 26
+bond_dict = defaultdict(lambda: len(bond_dict)) # 4 bindingdb: 4
+fingerprint_dict = defaultdict(lambda: len(fingerprint_dict)) # 6341 bindingdb: 20366
+edge_dict = defaultdict(lambda: len(edge_dict)) # 17536 bindingdb: 77916
+word_dict = defaultdict(lambda: len(word_dict)) # 22 bindingdb: 21
+
+
+def drug_featurizer(smiles, radius=2):
+ mol = Chem.AddHs(Chem.MolFromSmiles(smiles))
+ atoms = create_atoms(mol)
+ i_jbond_dict = create_ijbonddict(mol)
+ fingerprints = extract_fingerprints(atoms, i_jbond_dict, radius)
+ adjacency = coo_matrix(Chem.GetAdjacencyMatrix(mol))
+ adjacency = coo_matrix(adjacency)
+ edge_index = np.array([adjacency.row, adjacency.col])
+
+ return Data(x=torch.LongTensor(fingerprints).unsqueeze(1), edge_index=torch.LongTensor(edge_index))
+
+
+def create_atoms(mol):
+ """Create a list of atom (e.g., hydrogen and oxygen) IDs
+ considering the aromaticity."""
+ # GetSymbol: obtain the symbol of the atom
+ atoms = [a.GetSymbol() for a in mol.GetAtoms()]
+ for a in mol.GetAromaticAtoms():
+ i = a.GetIdx()
+ atoms[i] = (atoms[i], 'aromatic')
+ # turn it into index
+ atoms = [atom_dict[a] for a in atoms]
+
+ return np.array(atoms)
+
+
+def create_ijbonddict(mol):
+ """Create a dictionary, which each key is a node ID
+ and each value is the tuples of its neighboring node
+ and bond (e.g., single and double) IDs."""
+ i_jbond_dict = defaultdict(lambda: [])
+ for b in mol.GetBonds():
+ i, j = b.GetBeginAtomIdx(), b.GetEndAtomIdx()
+ bond = bond_dict[str(b.GetBondType())]
+ i_jbond_dict[i].append((j, bond))
+ i_jbond_dict[j].append((i, bond))
+ return i_jbond_dict
+
+
+def extract_fingerprints(atoms, i_jbond_dict, radius=2):
+ """Extract the r-radius subgraphs (i.e., fingerprints)
+ from a molecular graph using Weisfeiler-Lehman algorithm."""
+ fingerprints = None
+
+ if (len(atoms) == 1) or (radius == 0):
+ fingerprints = [fingerprint_dict[a] for a in atoms]
+
+ else:
+ nodes = atoms
+ i_jedge_dict = i_jbond_dict
+
+ for _ in range(radius):
+
+ """Update each node ID considering its neighboring nodes and edges
+ (i.e., r-radius subgraphs or fingerprints)."""
+ fingerprints = []
+ for i, j_edge in i_jedge_dict.items():
+ neighbors = [(nodes[j], edge) for j, edge in j_edge]
+ fingerprint = (nodes[i], tuple(sorted(neighbors)))
+ fingerprints.append(fingerprint_dict[fingerprint])
+ nodes = fingerprints
+
+ """Also update each edge ID considering two nodes
+ on its both sides."""
+ _i_jedge_dict = defaultdict(lambda: [])
+ for i, j_edge in i_jedge_dict.items():
+ for j, edge in j_edge:
+ both_side = tuple(sorted((nodes[i], nodes[j])))
+ edge = edge_dict[(both_side, edge)]
+ _i_jedge_dict[i].append((j, edge))
+ i_jedge_dict = _i_jedge_dict
+
+ return np.array(fingerprints)
diff --git a/deepscreen/models/predictors/custom.py b/deepscreen/models/predictors/custom.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f5d4d714c420588e858dd3f9f0f94c40d8eed2c
--- /dev/null
+++ b/deepscreen/models/predictors/custom.py
@@ -0,0 +1,21 @@
+from torch import nn
+
+
+class CustomPredictor(nn.Module):
+ def __init__(
+ self,
+ drug_encoder: nn.Module,
+ protein_encoder: nn.Module,
+ decoder: nn.Module,
+ ):
+ super().__init__()
+ self.drug_encoder = drug_encoder
+ self.protein_encoder = protein_encoder
+ self.decoder = decoder
+
+ def forward(self, enc_drug, enc_protein):
+ enc_drug = self.drug_encoder(enc_drug)
+ enc_protein = self.protein_encoder(enc_protein)
+ preds = self.decoder(enc_drug, enc_protein)
+
+ return preds
diff --git a/deepscreen/models/predictors/deep_conv_dti.py b/deepscreen/models/predictors/deep_conv_dti.py
new file mode 100644
index 0000000000000000000000000000000000000000..cbc150e180a21ed0a515878cfddf27b6f2ddd106
--- /dev/null
+++ b/deepscreen/models/predictors/deep_conv_dti.py
@@ -0,0 +1,95 @@
+import torch
+import torch.nn as nn
+
+
+class DeepConvDTI(nn.Module):
+ def __init__(self, dropout=0.2, drug_layers=(1024, 512), protein_windows=(10, 15, 20, 25), n_filters=64,
+ decay=0.0, fc_layers=None, convolution=True, activation=nn.ReLU(), protein_layers=None):
+ super().__init__()
+ self.dropout = dropout
+ self.drug_layers = drug_layers
+ self.protein_windows = protein_windows
+ self.filters = n_filters
+ self.decay = decay
+ self.fc_layers = fc_layers
+ self.convolution = convolution
+ self.activation = activation # Use any nn.Module as the activation function
+ self.protein_layers = protein_layers
+
+ # Define the drug branch of the model
+ self.drug_branch = []
+ for layer_size in drug_layers:
+ self.drug_branch += [
+ nn.LazyLinear(layer_size),
+ nn.BatchNorm1d(layer_size),
+ activation,
+ nn.Dropout(dropout)
+ ]
+ self.drug_branch = nn.Sequential(*self.drug_branch)
+
+ # Define the protein branch of the model
+ if convolution:
+ # Use embedding and convolution layers for protein sequences
+ self.protein_embedding = nn.Embedding(26, 20)
+ # Use a list of parallel convolution and pooling layers with different window sizes
+ self.protein_convs = nn.ModuleList()
+ for window_size in protein_windows:
+ conv = nn.Sequential(
+ nn.Conv1d(20, n_filters, window_size, padding="same"),
+ nn.BatchNorm1d(n_filters),
+ activation,
+ nn.AdaptiveMaxPool1d(1)
+ )
+ self.protein_convs.append(conv)
+
+ if protein_layers:
+ self.protein_branch = []
+ for layer_size in protein_layers:
+ self.protein_branch += [
+ nn.LazyLinear(layer_size),
+ nn.BatchNorm1d(layer_size),
+ activation,
+ nn.Dropout(dropout)
+ ]
+ self.protein_branch = nn.Sequential(*self.protein_branch)
+
+ # Define the final branch of the model that combines the drug and protein branches
+ self.final_branch = []
+ if fc_layers:
+ # Add additional dense layers for the final branch
+ for layer_size in fc_layers:
+ self.final_branch += [
+ nn.LazyLinear(layer_size),
+ nn.BatchNorm1d(layer_size),
+ activation
+ ]
+ self.final_branch = nn.Sequential(*self.final_branch)
+
+ def forward(self, input_d, input_p):
+ # Forward pass of the drug branch
+ output_d = self.drug_branch(input_d.float())
+
+ # Forward pass of the protein branch
+ if self.convolution:
+ # Embed the protein sequence and transpose the dimensions
+ output_p = self.protein_embedding(input_p)
+ output_p = output_p.transpose(1, 2)
+ # Apply the parallel convolution and pooling layers
+ conv_outputs = []
+ for conv in self.protein_convs:
+ conv_output = conv(output_p).squeeze(-1)
+ conv_outputs.append(conv_output)
+ # Concatenate the convolution outputs
+ output_p = torch.cat(conv_outputs, dim=1)
+ else:
+ output_p = input_p
+
+ if self.protein_layers:
+ # Apply the additional dense layers to the protein branch
+ output_p = self.protein_branch(output_p)
+
+ # Concatenate the drug and protein outputs
+ output_t = torch.cat([output_d, output_p], dim=1)
+ # Apply the final dense layers
+ output_t = self.final_branch(output_t)
+ return output_t
diff --git a/deepscreen/models/predictors/deep_dta.py b/deepscreen/models/predictors/deep_dta.py
new file mode 100644
index 0000000000000000000000000000000000000000..ecc663ec3cdf3d18b01269ee161fbe32e6374391
--- /dev/null
+++ b/deepscreen/models/predictors/deep_dta.py
@@ -0,0 +1,39 @@
+import torch
+import torch.nn as nn
+
+# TODO this is an easy model; refactor it to be customized by config file only
+
+
+class DeepDTA(nn.Module):
+ """
+ From DeepDTA
+ """
+ def __init__(
+ self,
+ drug_cnn: nn.Module,
+ protein_cnn: nn.Module,
+ num_features_drug: int,
+ num_features_protein: int,
+ embed_dim: int,
+ ):
+ super().__init__()
+ self.drug_cnn = drug_cnn
+ self.protein_cnn = protein_cnn
+ self.fc = nn.Sequential(nn.LazyLinear(1024), nn.ReLU(), nn.Dropout(0.1),
+ nn.Linear(1024, 512), nn.ReLU(), nn.Dropout(0.1))
+
+ # protein sequence encoder (1d conv)
+ self.drug_embedding = nn.Embedding(num_features_drug, embed_dim)
+ self.protein_embedding = nn.Embedding(num_features_protein, embed_dim)
+
+ def forward(self, v_d, v_p):
+ v_d = self.drug_embedding(v_d.long())
+ v_d = self.drug_cnn(v_d)
+
+ v_p = self.protein_embedding(v_p.long())
+ v_p = self.protein_cnn(v_p)
+
+ v_f = torch.cat([v_d, v_p], 1)
+ v_f = self.fc(v_f)
+
+ return v_f
diff --git a/deepscreen/models/predictors/deep_dtaf.py b/deepscreen/models/predictors/deep_dtaf.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb30b040cba4f0b3054734790eb43bec49d9e9f2
--- /dev/null
+++ b/deepscreen/models/predictors/deep_dtaf.py
@@ -0,0 +1,190 @@
+import torch
+import torch.nn as nn
+
+PT_FEATURE_SIZE = 40
+
+
+class DeepDTAF(nn.Module):
+ def __init__(self, smi_charset_len):
+ super().__init__()
+
+ smi_embed_size = 128
+ seq_embed_size = 128
+
+ seq_oc = 128
+ pkt_oc = 128
+ smi_oc = 128
+
+ self.smi_embed = nn.Embedding(smi_charset_len, smi_embed_size)
+
+ self.seq_embed = nn.Linear(PT_FEATURE_SIZE, seq_embed_size) # (N, *, H_{in}) -> (N, *, H_{out})
+
+ conv_seq = []
+ ic = seq_embed_size
+ for oc in [32, 64, 64, seq_oc]:
+ conv_seq.append(DilatedParllelResidualBlockA(ic, oc))
+ ic = oc
+ conv_seq.append(nn.AdaptiveMaxPool1d(1)) # (N, oc)
+ conv_seq.append(Squeeze())
+ self.conv_seq = nn.Sequential(*conv_seq)
+
+ # (N, H=32, L)
+ conv_pkt = []
+ ic = seq_embed_size
+ for oc in [32, 64, pkt_oc]:
+ conv_pkt.append(nn.Conv1d(ic, oc, 3)) # (N,C,L)
+ conv_pkt.append(nn.BatchNorm1d(oc))
+ conv_pkt.append(nn.PReLU())
+ ic = oc
+ conv_pkt.append(nn.AdaptiveMaxPool1d(1))
+ conv_pkt.append(Squeeze())
+ self.conv_pkt = nn.Sequential(*conv_pkt) # (N,oc)
+
+ conv_smi = []
+ ic = smi_embed_size
+ for oc in [32, 64, smi_oc]:
+ conv_smi.append(DilatedParllelResidualBlockB(ic, oc))
+ ic = oc
+ conv_smi.append(nn.AdaptiveMaxPool1d(1))
+ conv_smi.append(Squeeze())
+ self.conv_smi = nn.Sequential(*conv_smi) # (N,128)
+
+ self.cat_dropout = nn.Dropout(0.2)
+
+ self.classifier = nn.Sequential(
+ nn.Linear(seq_oc + pkt_oc + smi_oc, 128),
+ nn.Dropout(0.5),
+ nn.PReLU(),
+ nn.Linear(128, 64),
+ nn.Dropout(0.5),
+ nn.PReLU(),
+ # nn.Linear(64, 1),
+ # nn.PReLU()
+ )
+
+ def forward(self, seq, pkt, smi):
+ # assert seq.shape == (N,L,43)
+ seq_embed = self.seq_embed(seq) # (N,L,32)
+ seq_embed = torch.transpose(seq_embed, 1, 2) # (N,32,L)
+ seq_conv = self.conv_seq(seq_embed) # (N,128)
+
+ # assert pkt.shape == (N,L,43)
+ pkt_embed = self.seq_embed(pkt) # (N,L,32)
+ pkt_embed = torch.transpose(pkt_embed, 1, 2)
+ pkt_conv = self.conv_pkt(pkt_embed) # (N,128)
+
+ # assert smi.shape == (N, L)
+ smi_embed = self.smi_embed(smi) # (N,L,32)
+ smi_embed = torch.transpose(smi_embed, 1, 2)
+ smi_conv = self.conv_smi(smi_embed) # (N,128)
+
+ cat = torch.cat([seq_conv, pkt_conv, smi_conv], dim=1) # (N,128*3)
+ cat = self.cat_dropout(cat)
+
+ output = self.classifier(cat)
+ return output
+
+
+class Squeeze(nn.Module):
+ def forward(self, input: torch.Tensor):
+ return input.squeeze()
+
+
+class CDilated(nn.Module):
+ def __init__(self, nIn, nOut, kSize, stride=1, d=1):
+ super().__init__()
+ padding = int((kSize - 1) / 2) * d
+ self.conv = nn.Conv1d(nIn, nOut, kSize, stride=stride, padding=padding, bias=False, dilation=d)
+
+ def forward(self, input):
+ output = self.conv(input)
+ return output
+
+
+class DilatedParllelResidualBlockA(nn.Module):
+ def __init__(self, nIn, nOut, add=True):
+ super().__init__()
+ n = int(nOut / 5)
+ n1 = nOut - 4 * n
+ self.c1 = nn.Conv1d(nIn, n, 1, padding=0)
+ self.br1 = nn.Sequential(nn.BatchNorm1d(n), nn.PReLU())
+ self.d1 = CDilated(n, n1, 3, 1, 1) # dilation rate of 2^0
+ self.d2 = CDilated(n, n, 3, 1, 2) # dilation rate of 2^1
+ self.d4 = CDilated(n, n, 3, 1, 4) # dilation rate of 2^2
+ self.d8 = CDilated(n, n, 3, 1, 8) # dilation rate of 2^3
+ self.d16 = CDilated(n, n, 3, 1, 16) # dilation rate of 2^4
+ self.br2 = nn.Sequential(nn.BatchNorm1d(nOut), nn.PReLU())
+
+ if nIn != nOut:
+ # print(f'{nIn}-{nOut}: add=False')
+ add = False
+ self.add = add
+
+ def forward(self, input):
+ # reduce
+ output1 = self.c1(input)
+ output1 = self.br1(output1)
+ # split and transform
+ d1 = self.d1(output1)
+ d2 = self.d2(output1)
+ d4 = self.d4(output1)
+ d8 = self.d8(output1)
+ d16 = self.d16(output1)
+
+ # heirarchical fusion for de-gridding
+ add1 = d2
+ add2 = add1 + d4
+ add3 = add2 + d8
+ add4 = add3 + d16
+
+ # merge
+ combine = torch.cat([d1, add1, add2, add3, add4], 1)
+
+ # if residual version
+ if self.add:
+ combine = input + combine
+ output = self.br2(combine)
+ return output
+
+
+class DilatedParllelResidualBlockB(nn.Module):
+ def __init__(self, nIn, nOut, add=True):
+ super().__init__()
+ n = int(nOut / 4)
+ n1 = nOut - 3 * n
+ self.c1 = nn.Conv1d(nIn, n, 1, padding=0)
+ self.br1 = nn.Sequential(nn.BatchNorm1d(n), nn.PReLU())
+ self.d1 = CDilated(n, n1, 3, 1, 1) # dilation rate of 2^0
+ self.d2 = CDilated(n, n, 3, 1, 2) # dilation rate of 2^1
+ self.d4 = CDilated(n, n, 3, 1, 4) # dilation rate of 2^2
+ self.d8 = CDilated(n, n, 3, 1, 8) # dilation rate of 2^3
+ self.br2 = nn.Sequential(nn.BatchNorm1d(nOut), nn.PReLU())
+
+ if nIn != nOut:
+ # print(f'{nIn}-{nOut}: add=False')
+ add = False
+ self.add = add
+
+ def forward(self, input):
+ # reduce
+ output1 = self.c1(input)
+ output1 = self.br1(output1)
+ # split and transform
+ d1 = self.d1(output1)
+ d2 = self.d2(output1)
+ d4 = self.d4(output1)
+ d8 = self.d8(output1)
+
+ # heirarchical fusion for de-gridding
+ add1 = d2
+ add2 = add1 + d4
+ add3 = add2 + d8
+
+ # merge
+ combine = torch.cat([d1, add1, add2, add3], 1)
+
+ # if residual version
+ if self.add:
+ combine = input + combine
+ output = self.br2(combine)
+ return output
diff --git a/deepscreen/models/predictors/deep_scams.py b/deepscreen/models/predictors/deep_scams.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba7f1f597a0b453d205f3d59b129a9d0962fca5c
--- /dev/null
+++ b/deepscreen/models/predictors/deep_scams.py
@@ -0,0 +1,38 @@
+import numpy as np
+from torch import nn
+from rdkit.Chem import Descriptors, AllChem, MolFromSmiles
+
+from deepscreen.models.components.mlp import LazyMLP
+
+DeepSCAMs = LazyMLP(
+ out_channels=1,
+ hidden_channels=[100, 1000, 1000],
+ activation=nn.Tanh,
+ dropout=0.0
+)
+
+
+def featurizer(smiles, radius=2, n_bits=1024):
+ descr = Descriptors._descList[0:2] + Descriptors._descList[3:]
+ calc = [x[1] for x in descr]
+ try:
+ mol = MolFromSmiles(smiles)
+ features = []
+ fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius=radius, nBits=n_bits)
+ fp_list = []
+ fp_list.extend(fp.ToBitString())
+ fp_expl = [float(x) for x in fp_list]
+ ds_n = []
+ for d in calc:
+ v = d(mol)
+ if v > np.finfo(np.float32).max:
+ ds_n.append(np.finfo(np.float32).max)
+ else:
+ ds_n.append(np.float32(v))
+
+ features += [fp_expl + list(ds_n)]
+ except:
+ log.warning(f'RDKit could not find process SMILES: {smiles}; converted to all 0 features')
+ features = np.zeros((n_bits,))
+
+ return features
diff --git a/deepscreen/models/predictors/drug_ban.py b/deepscreen/models/predictors/drug_ban.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d639940752f06b1922b3004fd5c4e0732dfcc44
--- /dev/null
+++ b/deepscreen/models/predictors/drug_ban.py
@@ -0,0 +1,328 @@
+import torch.nn as nn
+import torch.nn.functional as F
+import torch
+import math
+from dgllife.model.gnn import GCN
+from torch.nn.utils.weight_norm import weight_norm
+
+
+class DrugBAN(nn.Module):
+ def __init__(
+ self,
+ drug_in_feats,
+ drug_embedding,
+ drug_hidden_feats,
+ protein_emb_dim,
+ num_filters,
+ kernel_size,
+ mlp_in_dim,
+ mlp_hidden_dim,
+ mlp_out_dim,
+ drug_padding,
+ protein_padding,
+ ban_heads,
+ ):
+ super().__init__()
+ self.drug_extractor = MolecularGCN(in_feats=drug_in_feats, dim_embedding=drug_embedding,
+ padding=drug_padding,
+ hidden_feats=drug_hidden_feats)
+ self.protein_extractor = ProteinCNN(protein_emb_dim, num_filters, kernel_size, protein_padding)
+
+ self.bcn = weight_norm(
+ BANLayer(v_dim=drug_hidden_feats[-1], q_dim=num_filters[-1], h_dim=mlp_in_dim, h_out=ban_heads),
+ name='h_mat', dim=None)
+ self.mlp_classifier = MLPDecoder(mlp_in_dim, mlp_hidden_dim, mlp_out_dim)
+
+ def forward(self, bg_d, v_p):
+ v_d = self.drug_extractor(bg_d)
+ v_p = self.protein_extractor(v_p)
+ f, att = self.bcn(v_d, v_p)
+ score = self.mlp_classifier(f)
+ # if mode == "train":
+ # return v_d, v_p, f, score
+ # elif mode == "eval":
+ # return v_d, v_p, score, att
+ return score
+
+
+class MolecularGCN(nn.Module):
+ def __init__(self, in_feats, dim_embedding=128, padding=True, hidden_feats=None, activation=None):
+ super().__init__()
+ self.init_transform = nn.Linear(in_feats, dim_embedding, bias=False)
+ if padding:
+ with torch.no_grad():
+ self.init_transform.weight[-1].fill_(0)
+ self.gnn = GCN(in_feats=dim_embedding, hidden_feats=hidden_feats, activation=activation)
+ self.output_feats = hidden_feats[-1]
+
+ def forward(self, batch_graph):
+ node_feats = batch_graph.ndata.pop('h')
+ node_feats = self.init_transform(node_feats)
+ node_feats = self.gnn(batch_graph, node_feats)
+ batch_size = batch_graph.batch_size
+ node_feats = node_feats.view(batch_size, -1, self.output_feats)
+ return node_feats
+
+
+class ProteinCNN(nn.Module):
+ def __init__(self, embedding_dim, num_filters, kernel_size, padding=True):
+ super().__init__()
+ if padding:
+ self.embedding = nn.Embedding(26, embedding_dim, padding_idx=0)
+ else:
+ self.embedding = nn.Embedding(26, embedding_dim)
+ in_ch = [embedding_dim] + num_filters
+ self.in_ch = in_ch[-1]
+ kernels = kernel_size
+ self.conv1 = nn.Conv1d(in_channels=in_ch[0], out_channels=in_ch[1], kernel_size=kernels[0])
+ self.bn1 = nn.BatchNorm1d(in_ch[1])
+ self.conv2 = nn.Conv1d(in_channels=in_ch[1], out_channels=in_ch[2], kernel_size=kernels[1])
+ self.bn2 = nn.BatchNorm1d(in_ch[2])
+ self.conv3 = nn.Conv1d(in_channels=in_ch[2], out_channels=in_ch[3], kernel_size=kernels[2])
+ self.bn3 = nn.BatchNorm1d(in_ch[3])
+
+ def forward(self, v):
+ v = self.embedding(v.long())
+ v = v.transpose(2, 1)
+ v = self.bn1(F.relu(self.conv1(v)))
+ v = self.bn2(F.relu(self.conv2(v)))
+ v = self.bn3(F.relu(self.conv3(v)))
+ v = v.view(v.size(0), v.size(2), -1)
+ return v
+
+
+class MLPDecoder(nn.Module):
+ def __init__(self, in_dim, hidden_dim, out_dim):
+ super().__init__()
+ self.fc1 = nn.Linear(in_dim, hidden_dim)
+ self.bn1 = nn.BatchNorm1d(hidden_dim)
+ self.fc2 = nn.Linear(hidden_dim, hidden_dim)
+ self.bn2 = nn.BatchNorm1d(hidden_dim)
+ self.fc3 = nn.Linear(hidden_dim, out_dim)
+ self.bn3 = nn.BatchNorm1d(out_dim)
+ # self.fc4 = nn.Linear(out_dim, binary)
+
+ def forward(self, x):
+ x = self.bn1(F.relu(self.fc1(x)))
+ x = self.bn2(F.relu(self.fc2(x)))
+ x = self.bn3(F.relu(self.fc3(x)))
+ # x = self.fc4(x)
+ return x
+
+
+# noinspection PyTypeChecker
+class SimpleClassifier(nn.Module):
+ def __init__(self, in_dim, hid_dim, out_dim, dropout):
+ super().__init__()
+ layers = [
+ weight_norm(nn.Linear(in_dim, hid_dim), dim=None),
+ nn.ReLU(),
+ nn.Dropout(dropout, inplace=True),
+ weight_norm(nn.Linear(hid_dim, out_dim), dim=None)
+ ]
+ self.main = nn.Sequential(*layers)
+
+ def forward(self, x):
+ logits = self.main(x)
+ return logits
+
+
+class RandomLayer(nn.Module):
+ def __init__(self, input_dim_list, output_dim=256):
+ super().__init__()
+ self.input_num = len(input_dim_list)
+ self.output_dim = output_dim
+ self.random_matrix = [torch.randn(input_dim_list[i], output_dim) for i in range(self.input_num)]
+
+ def forward(self, input_list):
+ return_list = [torch.mm(input_list[i], self.random_matrix[i]) for i in range(self.input_num)]
+ return_tensor = return_list[0] / math.pow(float(self.output_dim), 1.0 / len(return_list))
+ for single in return_list[1:]:
+ return_tensor = torch.mul(return_tensor, single)
+ return return_tensor
+
+ def cuda(self, *args):
+ super(RandomLayer, self).cuda(*args)
+ self.random_matrix = [val.cuda(*args) for val in self.random_matrix]
+
+
+# noinspection PyTypeChecker
+class BANLayer(nn.Module):
+ def __init__(self, v_dim, q_dim, h_dim, h_out, act='ReLU', dropout=0.2, k=3):
+ super().__init__()
+
+ self.c = 32
+ self.k = k
+ self.v_dim = v_dim
+ self.q_dim = q_dim
+ self.h_dim = h_dim
+ self.h_out = h_out
+
+ self.v_net = FCNet([v_dim, h_dim * self.k], act=act, dropout=dropout)
+ self.q_net = FCNet([q_dim, h_dim * self.k], act=act, dropout=dropout)
+ # self.dropout = nn.Dropout(dropout[1])
+ if 1 < k:
+ self.p_net = nn.AvgPool1d(self.k, stride=self.k)
+
+ if h_out <= self.c:
+ self.h_mat = nn.Parameter(torch.Tensor(1, h_out, 1, h_dim * self.k).normal_())
+ self.h_bias = nn.Parameter(torch.Tensor(1, h_out, 1, 1).normal_())
+ else:
+ self.h_net = weight_norm(nn.Linear(h_dim * self.k, h_out), dim=None)
+
+ self.bn = nn.BatchNorm1d(h_dim)
+
+ def attention_pooling(self, v, q, att_map):
+ fusion_logits = torch.einsum('bvk,bvq,bqk->bk', (v, att_map, q))
+ if 1 < self.k:
+ fusion_logits = fusion_logits.unsqueeze(1) # b x 1 x d
+ fusion_logits = self.p_net(fusion_logits).squeeze(1) * self.k # sum-pooling
+ return fusion_logits
+
+ def forward(self, v, q, softmax=False):
+ v_num = v.size(1)
+ q_num = q.size(1)
+ if self.h_out <= self.c:
+ v_ = self.v_net(v)
+ q_ = self.q_net(q)
+ att_maps = torch.einsum('xhyk,bvk,bqk->bhvq', (self.h_mat, v_, q_)) + self.h_bias
+ else:
+ v_ = self.v_net(v).transpose(1, 2).unsqueeze(3)
+ q_ = self.q_net(q).transpose(1, 2).unsqueeze(2)
+ d_ = torch.matmul(v_, q_) # b x h_dim x v x q
+ att_maps = self.h_net(d_.transpose(1, 2).transpose(2, 3)) # b x v x q x h_out
+ att_maps = att_maps.transpose(2, 3).transpose(1, 2) # b x h_out x v x q
+ if softmax:
+ p = nn.functional.softmax(att_maps.view(-1, self.h_out, v_num * q_num), 2)
+ att_maps = p.view(-1, self.h_out, v_num, q_num)
+ logits = self.attention_pooling(v_, q_, att_maps[:, 0, :, :])
+ for i in range(1, self.h_out):
+ logits_i = self.attention_pooling(v_, q_, att_maps[:, i, :, :])
+ logits += logits_i
+ logits = self.bn(logits)
+ return logits, att_maps
+
+
+# noinspection PyTypeChecker
+class FCNet(nn.Module):
+ """Simple class for non-linear fully connect network
+ Modified from https://github.com/jnhwkim/ban-vqa/blob/master/fc.py
+ """
+
+ def __init__(self, dims, act='ReLU', dropout=0.0):
+ super().__init__()
+
+ layers = []
+ for i in range(len(dims) - 2):
+ in_dim = dims[i]
+ out_dim = dims[i + 1]
+ if 0 < dropout:
+ layers.append(nn.Dropout(dropout))
+ layers.append(weight_norm(nn.Linear(in_dim, out_dim), dim=None))
+ if '' != act:
+ layers.append(getattr(nn, act)())
+ if 0 < dropout:
+ layers.append(nn.Dropout(dropout))
+ layers.append(weight_norm(nn.Linear(dims[-2], dims[-1]), dim=None))
+ if '' != act:
+ layers.append(getattr(nn, act)())
+
+ self.main = nn.Sequential(*layers)
+
+ def forward(self, x):
+ return self.main(x)
+
+
+class BCNet(nn.Module):
+ """Simple class for non-linear bilinear connect network
+ Modified from https://github.com/jnhwkim/ban-vqa/blob/master/bc.py
+ """
+
+ # noinspection PyTypeChecker
+ def __init__(self, v_dim, q_dim, h_dim, h_out, act='ReLU', dropout=(0.2, 0.5), k=3):
+ super().__init__()
+
+ self.c = 32
+ self.k = k
+ self.v_dim = v_dim
+ self.q_dim = q_dim
+ self.h_dim = h_dim
+ self.h_out = h_out
+
+ self.v_net = FCNet([v_dim, h_dim * self.k], act=act, dropout=dropout[0])
+ self.q_net = FCNet([q_dim, h_dim * self.k], act=act, dropout=dropout[0])
+ self.dropout = nn.Dropout(dropout[1]) # attention
+ if 1 < k:
+ self.p_net = nn.AvgPool1d(self.k, stride=self.k)
+
+ if h_out is None:
+ pass
+ elif h_out <= self.c:
+ self.h_mat = nn.Parameter(torch.Tensor(1, h_out, 1, h_dim * self.k).normal_())
+ self.h_bias = nn.Parameter(torch.Tensor(1, h_out, 1, 1).normal_())
+ else:
+ self.h_net = weight_norm(nn.Linear(h_dim * self.k, h_out), dim=None)
+
+ def forward(self, v, q):
+ if self.h_out is None:
+ v_ = self.v_net(v)
+ q_ = self.q_net(q)
+ logits = torch.einsum('bvk,bqk->bvqk', (v_, q_))
+ return logits
+
+ # low-rank bilinear pooling using einsum
+ elif self.h_out <= self.c:
+ v_ = self.dropout(self.v_net(v))
+ q_ = self.q_net(q)
+ logits = torch.einsum('xhyk,bvk,bqk->bhvq', (self.h_mat, v_, q_)) + self.h_bias
+ return logits # b x h_out x v x q
+
+ # batch outer product, linear projection
+ # memory efficient but slow computation
+ else:
+ v_ = self.dropout(self.v_net(v)).transpose(1, 2).unsqueeze(3)
+ q_ = self.q_net(q).transpose(1, 2).unsqueeze(2)
+ d_ = torch.matmul(v_, q_) # b x h_dim x v x q
+ logits = self.h_net(d_.transpose(1, 2).transpose(2, 3)) # b x v x q x h_out
+ return logits.transpose(2, 3).transpose(1, 2) # b x h_out x v x q
+
+ def forward_with_weights(self, v, q, w):
+ v_ = self.v_net(v) # b x v x d
+ q_ = self.q_net(q) # b x q x d
+ logits = torch.einsum('bvk,bvq,bqk->bk', (v_, w, q_))
+ if 1 < self.k:
+ logits = logits.unsqueeze(1) # b x 1 x d
+ logits = self.p_net(logits).squeeze(1) * self.k # sum-pooling
+ return logits
+
+
+def drug_featurizer(smiles, max_drug_nodes=290):
+ from dgllife.utils import smiles_to_bigraph, CanonicalAtomFeaturizer, CanonicalBondFeaturizer
+
+ from deepscreen.utils import get_logger
+ log = get_logger(__name__)
+
+ try:
+ v_d = smiles_to_bigraph(smiles=smiles,
+ node_featurizer=CanonicalAtomFeaturizer(),
+ edge_featurizer=CanonicalBondFeaturizer(self_loop=True),
+ add_self_loop=True)
+ if v_d is None:
+ return None
+ actual_node_feats = v_d.ndata.pop('h')
+ num_actual_nodes = actual_node_feats.shape[0]
+ num_virtual_nodes = max_drug_nodes - num_actual_nodes
+ virtual_node_bit = torch.zeros([num_actual_nodes, 1])
+ actual_node_feats = torch.cat((actual_node_feats, virtual_node_bit), 1)
+ v_d.ndata['h'] = actual_node_feats
+ virtual_node_feat = torch.cat(
+ (torch.zeros(num_virtual_nodes, 74), torch.ones(num_virtual_nodes, 1)), 1
+ )
+ v_d.add_nodes(num_virtual_nodes, {"h": virtual_node_feat})
+ v_d = v_d.add_self_loop()
+ return v_d
+
+ except Exception as e:
+ log.warning(f"Failed to featurize SMILES ({smiles}) to graph due to {str(e)}")
+ return None
+
diff --git a/deepscreen/models/predictors/drug_vqa.py b/deepscreen/models/predictors/drug_vqa.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2262676a9eb59145647aaa67f96bb6b07bdcd18
--- /dev/null
+++ b/deepscreen/models/predictors/drug_vqa.py
@@ -0,0 +1,229 @@
+from math import floor
+from typing import Literal
+
+import torch.nn as nn
+import torch
+import torch.nn.functional as F
+# from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence
+
+
+def conv(in_channels, out_channels, kernel_size, conv_dim, stride=1):
+ conv_layer = None
+ match conv_dim:
+ case 1:
+ conv_layer = nn.Conv1d
+ case 2:
+ conv_layer = nn.Conv2d
+ case 3:
+ conv_layer = nn.Conv3d
+ return conv_layer(in_channels, out_channels,
+ kernel_size=kernel_size, stride=stride, padding=floor(kernel_size / 2), bias=False)
+
+
+def batch_norm(out_channels, conv_dim):
+ bn_layer = None
+ match conv_dim:
+ case 1:
+ bn_layer = nn.BatchNorm1d
+ case 2:
+ bn_layer = nn.BatchNorm2d
+ case 3:
+ bn_layer = nn.BatchNorm3d
+ return bn_layer(out_channels)
+
+
+def conv3x3(in_channels, out_channels, stride=1):
+ return nn.Conv2d(in_channels, out_channels, kernel_size=3,
+ stride=stride, padding=1, bias=False)
+
+
+def conv5x5(in_channels, out_channels, stride=1):
+ return nn.Conv2d(in_channels, out_channels, kernel_size=5,
+ stride=stride, padding=2, bias=False)
+
+
+def conv1x1(in_channels, out_channels, stride=1):
+ return nn.Conv2d(in_channels, out_channels, kernel_size=1,
+ stride=stride, padding=0, bias=False)
+
+
+# Residual block
+class ResidualBlock(nn.Module):
+ def __init__(self, in_channels, out_channels, conv_dim, stride=1, downsample=None):
+ super().__init__()
+ # self.conv1 = conv5x5(in_channels, out_channels, stride)
+ self.conv1 = conv(in_channels, out_channels, kernel_size=5, conv_dim=conv_dim, stride=stride)
+ self.bn1 = batch_norm(out_channels, conv_dim=conv_dim)
+ self.elu = nn.ELU(inplace=True)
+ # self.conv2 = conv3x3(out_channels, out_channels)
+ self.conv2 = conv(out_channels, out_channels, kernel_size=3, conv_dim=conv_dim, stride=stride)
+ self.bn2 = batch_norm(out_channels, conv_dim=conv_dim)
+ self.downsample = downsample
+
+ def forward(self, x):
+ residual = x
+ out = self.conv1(x)
+ out = self.bn1(out)
+ out = self.elu(out)
+ out = self.conv2(out)
+ out = self.bn2(out)
+ if self.downsample:
+ residual = self.downsample(x)
+ out += residual
+ out = self.elu(out)
+ return out
+
+
+class DrugVQA(nn.Module):
+ """
+ The class is an implementation of the DrugVQA model including regularization and without pruning.
+ Slight modifications have been done for speedup
+ """
+
+ def __init__(
+ self,
+ conv_dim: Literal[1, 2, 3],
+ lstm_hid_dim: int,
+ d_a: int,
+ r: int,
+ n_chars_smi: int,
+ n_chars_seq: int,
+ dropout: float,
+ in_channels: int,
+ cnn_channels: int,
+ cnn_layers: int,
+ emb_dim: int,
+ dense_hid: int,
+ ):
+ """
+ lstm_hid_dim: {int} hidden dimension for lstm
+ d_a : {int} hidden dimension for the dense layer
+ r : {int} attention-hops or attention heads
+ n_chars_smi : {int} voc size of smiles
+ n_chars_seq : {int} voc size of protein sequence
+ dropout : {float}
+ in_channels : {int} channels of CNN block input
+ cnn_channels: {int} channels of CNN block
+ cnn_layers : {int} num of layers of each CNN block
+ emb_dim : {int} embeddings dimension
+ dense_hid : {int} hidden dim for the output dense
+ """
+ super().__init__()
+ self.conv_dim = conv_dim
+ self.lstm_hid_dim = lstm_hid_dim
+ self.r = r
+ self.in_channels = in_channels
+ # rnn
+ self.embeddings = nn.Embedding(n_chars_smi, emb_dim)
+ # self.seq_embed = nn.Embedding(n_chars_seq, emb_dim)
+ self.lstm = nn.LSTM(emb_dim, self.lstm_hid_dim, 2, batch_first=True, bidirectional=True,
+ dropout=dropout)
+ self.linear_first = nn.Linear(2 * self.lstm_hid_dim, d_a)
+ self.linear_second = nn.Linear(d_a, r)
+ self.linear_first_seq = nn.Linear(cnn_channels, d_a)
+ self.linear_second_seq = nn.Linear(d_a, self.r)
+
+ # cnn
+ # self.conv = conv3x3(1, self.in_channels)
+ self.conv = conv(1, self.in_channels, kernel_size=3, conv_dim=conv_dim)
+ self.bn = batch_norm(in_channels, conv_dim=conv_dim)
+ self.elu = nn.ELU(inplace=False)
+ self.layer1 = self.make_layer(cnn_channels, cnn_layers)
+ self.layer2 = self.make_layer(cnn_channels, cnn_layers)
+
+ self.linear_final_step = nn.Linear(self.lstm_hid_dim * 2 + d_a, dense_hid)
+ # self.linear_final = nn.Linear(dense_hid, n_classes)
+ self.softmax = nn.Softmax(dim=1)
+
+ # @staticmethod
+ # def softmax(input, axis=1):
+ # """
+ # Softmax applied to axis=n
+ # Args:
+ # input: {Tensor,Variable} input on which softmax is to be applied
+ # axis : {int} axis on which softmax is to be applied
+ #
+ # Returns:
+ # softmaxed tensors
+ # """
+ # input_size = input.size()
+ # trans_input = input.transpose(axis, len(input_size) - 1)
+ # trans_size = trans_input.size()
+ # input_2d = trans_input.contiguous().view(-1, trans_size[-1])
+ # soft_max_2d = F.softmax(input_2d)
+ # soft_max_nd = soft_max_2d.view(*trans_size)
+ # return soft_max_nd.transpose(axis, len(input_size) - 1)
+
+ def make_layer(self, out_channels, blocks, stride=1):
+ downsample = None
+ if (stride != 1) or (self.in_channels != out_channels):
+ downsample = nn.Sequential(
+ # conv3x3(self.in_channels, out_channels, stride=stride),
+ conv(self.in_channels, out_channels, kernel_size=3, conv_dim=self.conv_dim, stride=stride),
+ batch_norm(out_channels, conv_dim=self.conv_dim)
+ )
+ layers = [ResidualBlock(self.in_channels, out_channels,
+ conv_dim=self.conv_dim, stride=stride, downsample=downsample)]
+ self.in_channels = out_channels
+ for i in range(1, blocks):
+ layers.append(ResidualBlock(out_channels, out_channels, conv_dim=self.conv_dim))
+ return nn.Sequential(*layers)
+
+ def forward(self, enc_drug, enc_protein):
+ smile_embed = self.embeddings(enc_drug.long())
+ # self.hidden_state = tuple(hidden_state.to(smile_embed).detach() for hidden_state in self.hidden_state)
+ outputs, hidden_state = self.lstm(smile_embed)
+ sentence_att = F.tanh(self.linear_first(outputs))
+ sentence_att = self.linear_second(sentence_att)
+ sentence_att = self.softmax(sentence_att)
+ sentence_att = sentence_att.transpose(1, 2)
+ sentence_embed = sentence_att @ outputs
+ avg_sentence_embed = torch.sum(sentence_embed, 1) / self.r # multi head
+
+ pic = self.conv(enc_protein.float().unsqueeze(1))
+ pic = self.bn(pic)
+ pic = self.elu(pic)
+ pic = self.layer1(pic)
+ pic = self.layer2(pic)
+ pic_emb = torch.mean(pic, 2).unsqueeze(2)
+ pic_emb = pic_emb.permute(0, 2, 1)
+ seq_att = F.tanh(self.linear_first_seq(pic_emb))
+ seq_att = self.linear_second_seq(seq_att)
+ seq_att = self.softmax(seq_att)
+ seq_att = seq_att.transpose(1, 2)
+ seq_embed = seq_att @ pic_emb
+ avg_seq_embed = torch.sum(seq_embed, 1) / self.r
+
+ sscomplex = torch.cat([avg_sentence_embed, avg_seq_embed], dim=1)
+ sscomplex = F.relu(self.linear_final_step(sscomplex))
+
+ # if not bool(self.type):
+ # output = F.sigmoid(self.linear_final(sscomplex))
+ # return output, seq_att
+ # else:
+ # return F.log_softmax(self.linear_final(sscomplex)), seq_att
+
+ return sscomplex, seq_att
+
+
+class AttentionL2Regularization(nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def forward(self, seq_att):
+ batch_size = seq_att.size(0)
+ identity = torch.eye(seq_att.size(1), device=seq_att.device)
+ identity = identity.unsqueeze(0).expand(batch_size, seq_att.size(1), seq_att.size(1))
+ loss = torch.mean(self.l2_matrix_norm(seq_att @ seq_att.transpose(1, 2) - identity))
+ return loss
+
+ @staticmethod
+ def l2_matrix_norm(m):
+ """
+ m = ||A * A_T - I||
+ Missing from the original DrugVQA GitHub source code.
+ Opting to use the faster Frobenius norm rather than the induced L2 matrix norm (spectral norm)
+ proposed in the original research, because the goal is to minimize the difference between
+ the attention matrix and the identity matrix.
+ """
+ return torch.linalg.norm(m, ord='fro', dim=(1, 2))
diff --git a/deepscreen/models/predictors/electra_dta.py b/deepscreen/models/predictors/electra_dta.py
new file mode 100644
index 0000000000000000000000000000000000000000..b06fe6ab33516a1d866cf74f8ce2c52a88c00991
--- /dev/null
+++ b/deepscreen/models/predictors/electra_dta.py
@@ -0,0 +1,102 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class ElectraDTA(nn.Module):
+ def __init__(self, smilen, seq_len, hidden_dim):
+ super().__init__()
+ self.drug_input = nn.Linear(smilen, hidden_dim)
+ self.prot_input = nn.Linear(seq_len, hidden_dim)
+ self.num_filters = hidden_dim
+ self.filter_length = 3
+ self.n_layers = 4
+ self.seblock = True
+ self.encode_smiles = ConvBlock(hidden_dim, self.seblock, self.num_filters, self.filter_length)
+ self.encode_prot = ConvBlock(hidden_dim, self.seblock, self.num_filters, self.filter_length)
+ self.global_pool = nn.AdaptiveMaxPool1d(1)
+ self.concat = nn.Linear(hidden_dim * 4, hidden_dim * 4)
+ self.predictions = FCNet(hidden_dim * 4) # you need to define this function
+ self.interaction_model = nn.Sequential(self.drug_input, self.prot_input, self.encode_smiles, self.encode_prot,
+ self.global_pool, self.concat, self.predictions)
+
+ def forward(self, x):
+ x = self.interaction_model(x)
+ return x
+
+
+class SEBlock(nn.Module):
+ def __init__(self, channels, r=8):
+ super().__init__()
+ self.squeeze = nn.AdaptiveAvgPool1d(1)
+ self.excitation = nn.Sequential(
+ nn.Linear(channels, channels // r),
+ nn.ReLU(),
+ nn.Linear(channels // r, channels),
+ nn.Sigmoid()
+ )
+
+ def forward(self, x):
+ out = self.squeeze(x)
+ out = self.excitation(out)
+ return x * out
+
+
+class ConvBlock(nn.Module):
+ def __init__(self, input_channels, seblock, num_filters, filter_length):
+ super().__init__()
+ self.conv1 = nn.Conv1d(input_channels, num_filters, filter_length, padding='valid', stride=1)
+ self.conv2 = nn.Conv1d(num_filters, num_filters * 2, filter_length, padding='valid', stride=1)
+ self.seblock = seblock
+ if seblock:
+ self.se1 = SEBlock(num_filters)
+ self.se2 = SEBlock(num_filters * 2)
+
+ def forward(self, x):
+ x = F.relu(self.conv1(x))
+ if self.seblock:
+ x = self.se1(x)
+ x = F.relu(self.conv2(x))
+ if self.seblock:
+ x = self.se2(x)
+ return x
+
+
+class Highway(nn.Module):
+ def __init__(self, dim, n_layers, activation=nn.Tanh(), gate_bias=0):
+ super(Highway, self).__init__()
+ self.n_layers = n_layers
+ self.activation = activation
+ self.T_gates = nn.ModuleList([nn.Linear(dim, dim) for _ in range(n_layers)])
+ self.transforms = nn.ModuleList([nn.Linear(dim, dim) for _ in range(n_layers)])
+ self.sigmoid = nn.Sigmoid()
+ nn.init.constant_(self.linear.bias, gate_bias)
+
+ def forward(self, x):
+ for i in range(self.n_layers):
+ T = self.sigmoid(self.gate(x))
+ C = 1 - T
+ H = self.activation(self.linear(x))
+ x = T * H + C * x
+ return x
+
+
+class FCNet(nn.Module):
+ def __init__(self, input_dim):
+ super().__init__()
+ self.n_layers = 4
+ self.highway = Highway(input_dim, self.n_layers, gate_bias=-2)
+ self.fc1 = nn.Linear(input_dim, 1024)
+ self.fc2 = nn.Linear(1024, 1024)
+ self.fc3 = nn.Linear(1024, 512)
+ # self.fc4 = nn.Linear(512, 1)
+ self.dropout = nn.Dropout(0.4)
+
+ def forward(self, x):
+ x = self.highway(x)
+ x = F.relu(self.fc1(x))
+ x = self.dropout(x)
+ x = F.relu(self.fc2(x))
+ x = self.dropout(x)
+ x = F.relu(self.fc3(x))
+ # x = self.fc4(x)
+ return x
diff --git a/deepscreen/models/predictors/graph_dta.py b/deepscreen/models/predictors/graph_dta.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebfd3887456167e8789c62310867eb32fb2083a3
--- /dev/null
+++ b/deepscreen/models/predictors/graph_dta.py
@@ -0,0 +1,59 @@
+import torch
+import torch.nn as nn
+
+from lightning import LightningModule
+
+
+class GraphDTA(LightningModule):
+ """
+ From GraphDTA (Nguyen et al., 2020; https://doi.org/10.1093/bioinformatics/btaa921).
+ """
+ def __init__(
+ self,
+ gnn: nn.Module,
+ num_features_protein: int,
+ n_filters: int,
+ embed_dim: int,
+ output_dim: int,
+ dropout: float
+ ):
+ super().__init__()
+ self.gnn = gnn
+
+ # protein sequence encoder (1d conv)
+ self.embedding_xt = nn.Embedding(num_features_protein, embed_dim)
+ self.conv_xt = nn.Conv1d(in_channels=1000, out_channels=n_filters, kernel_size=8)
+ self.fc1_xt = nn.Linear(32 * 121, output_dim)
+
+ # combined layers
+ self.fc1 = nn.Linear(256, 1024)
+ self.fc2 = nn.Linear(1024, 512)
+
+ # activation and regularization
+ self.relu = nn.ReLU()
+ self.dropout = nn.Dropout(dropout)
+
+ # protein input feedforward
+ def conv_forward_xt(self, v_p):
+ v_p = self.embedding_xt(v_p.long())
+ v_p = self.conv_xt(v_p)
+ # flatten
+ v_p = v_p.view(-1, 32 * 121)
+ v_p = self.fc1_xt(v_p)
+ return v_p
+
+ def forward(self, v_d, v_p):
+ v_d = self.gnn(v_d)
+ v_p = self.conv_forward_xt(v_p)
+
+ # concat
+ v_f = torch.cat((v_d, v_p), 1)
+ # dense layers
+ v_f = self.fc1(v_f)
+ v_f = self.relu(v_f)
+ v_f = self.dropout(v_f)
+ v_f = self.fc2(v_f)
+ v_f = self.relu(v_f)
+ v_f = self.dropout(v_f)
+ # v_f = self.out(v_f)
+ return v_f
diff --git a/deepscreen/models/predictors/hyper_attention_dti.py b/deepscreen/models/predictors/hyper_attention_dti.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b16665cfab263a8273c303166e020b92460bc1c
--- /dev/null
+++ b/deepscreen/models/predictors/hyper_attention_dti.py
@@ -0,0 +1,94 @@
+import torch
+import torch.nn as nn
+
+# TODO this is an easy model; refactor it to be customized by config file only
+
+
+class HyperAttentionDTI(nn.Module):
+ def __init__(
+ self,
+ protein_kernel=(4, 8, 12),
+ drug_kernel=(4, 6, 8),
+ conv=40,
+ char_dim=64,
+ protein_max_len=1000,
+ drug_max_len=100
+ ):
+ super().__init__()
+
+ self.drug_embed = nn.Embedding(63, char_dim, padding_idx=0)
+ self.drug_cnn = nn.Sequential(
+ nn.Conv1d(in_channels=char_dim, out_channels=conv, kernel_size=drug_kernel[0]),
+ nn.ReLU(),
+ nn.Conv1d(in_channels=conv, out_channels=conv * 2, kernel_size=drug_kernel[1]),
+ nn.ReLU(),
+ nn.Conv1d(in_channels=conv * 2, out_channels=conv * 4, kernel_size=drug_kernel[2]),
+ nn.ReLU(),
+ )
+ self.drug_max_pool = nn.MaxPool1d(
+ drug_max_len - drug_kernel[0] - drug_kernel[1] - drug_kernel[2] + 3)
+
+ self.protein_embed = nn.Embedding(26, char_dim, padding_idx=0)
+ self.protein_cnn = nn.Sequential(
+ nn.Conv1d(in_channels=char_dim, out_channels=conv, kernel_size=protein_kernel[0]),
+ nn.ReLU(),
+ nn.Conv1d(in_channels=conv, out_channels=conv * 2, kernel_size=protein_kernel[1]),
+ nn.ReLU(),
+ nn.Conv1d(in_channels=conv * 2, out_channels=conv * 4, kernel_size=protein_kernel[2]),
+ nn.ReLU(),
+ )
+ self.protein_max_pool = nn.MaxPool1d(
+ protein_max_len - protein_kernel[0] - protein_kernel[1] - protein_kernel[2] + 3)
+
+ self.attention_layer = nn.Linear(conv * 4, conv * 4)
+ self.protein_attention_layer = nn.Linear(conv * 4, conv * 4)
+ self.drug_attention_layer = nn.Linear(conv * 4, conv * 4)
+
+ self.dropout1 = nn.Dropout(0.1)
+ self.dropout2 = nn.Dropout(0.1)
+ self.dropout3 = nn.Dropout(0.1)
+ self.relu = nn.ReLU()
+ self.tanh = nn.Tanh()
+ self.sigmoid = nn.Sigmoid()
+ self.leaky_relu = nn.LeakyReLU()
+ self.fc1 = nn.Linear(conv * 8, 1024)
+ self.fc2 = nn.Linear(1024, 1024)
+ self.fc3 = nn.Linear(1024, 512)
+ # self.out = nn.Linear(512, 1)
+
+ def forward(self, drug, protein):
+ drugembed = self.drug_embed(drug.long())
+ proteinembed = self.protein_embed(protein.long())
+ drugembed = drugembed.permute(0, 2, 1)
+ proteinembed = proteinembed.permute(0, 2, 1)
+
+ drug_conv = self.drug_cnn(drugembed)
+ protein_conv = self.protein_cnn(proteinembed)
+
+ drug_att = self.drug_attention_layer(drug_conv.permute(0, 2, 1))
+ protein_att = self.protein_attention_layer(protein_conv.permute(0, 2, 1))
+
+ d_att_layers = torch.unsqueeze(drug_att, 2).repeat(1, 1, protein_conv.shape[-1], 1) # repeat along protein size
+ p_att_layers = torch.unsqueeze(protein_att, 1).repeat(1, drug_conv.shape[-1], 1, 1) # repeat along drug size
+ atten_matrix = self.attention_layer(self.relu(d_att_layers + p_att_layers))
+ compound_atte = torch.mean(atten_matrix, 2)
+ protein_atte = torch.mean(atten_matrix, 1)
+ compound_atte = self.sigmoid(compound_atte.permute(0, 2, 1))
+ protein_atte = self.sigmoid(protein_atte.permute(0, 2, 1))
+
+ drug_conv = drug_conv * 0.5 + drug_conv * compound_atte
+ protein_conv = protein_conv * 0.5 + protein_conv * protein_atte
+
+ drug_conv = self.drug_max_pool(drug_conv).squeeze(2)
+ protein_conv = self.protein_max_pool(protein_conv).squeeze(2)
+
+ preds = torch.cat([drug_conv, protein_conv], dim=1)
+ preds = self.dropout1(preds)
+ preds = self.leaky_relu(self.fc1(preds))
+ preds = self.dropout2(preds)
+ preds = self.leaky_relu(self.fc2(preds))
+ preds = self.dropout3(preds)
+ preds = self.leaky_relu(self.fc3(preds))
+ # preds = self.out(preds)
+
+ return preds
diff --git a/deepscreen/models/predictors/m_graph_dta.py b/deepscreen/models/predictors/m_graph_dta.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebe849647f7536748e8c8352d29ea20931d7ea1c
--- /dev/null
+++ b/deepscreen/models/predictors/m_graph_dta.py
@@ -0,0 +1,266 @@
+"""
+MGraphDTA: Deep Multiscale Graph Neural Network for Explainable Drug-target binding affinity Prediction
+"""
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from rdkit import Chem
+from torch.nn.modules.batchnorm import _BatchNorm
+import torch_geometric.nn as gnn
+from torch import Tensor
+from collections import OrderedDict
+
+from deepscreen.data.featurizers.categorical import one_of_k_encoding, one_of_k_encoding_unk
+
+
+class MGraphDTA(nn.Module):
+ def __init__(self, block_num, vocab_protein_size, embedding_size=128, filter_num=32):
+ super().__init__()
+ self.protein_encoder = TargetRepresentation(block_num, vocab_protein_size, embedding_size)
+ self.ligand_encoder = GraphDenseNet(num_input_features=87,
+ out_dim=filter_num * 3,
+ block_config=[8, 8, 8],
+ bn_sizes=[2, 2, 2])
+
+ self.classifier = nn.Sequential(
+ nn.Linear(filter_num * 3 * 2, 1024),
+ nn.ReLU(),
+ nn.Dropout(0.1),
+ nn.Linear(1024, 1024),
+ nn.ReLU(),
+ nn.Dropout(0.1),
+ nn.Linear(1024, 256),
+ nn.ReLU(),
+ nn.Dropout(0.1)
+ )
+
+ def forward(self, emb_drug, emb_protein):
+ protein_x = self.protein_encoder(emb_protein)
+ ligand_x = self.ligand_encoder(emb_drug)
+
+ x = torch.cat([protein_x, ligand_x], dim=-1)
+ x = self.classifier(x)
+
+ return x
+
+
+class Conv1dReLU(nn.Module):
+ """
+ kernel_size=3, stride=1, padding=1
+ kernel_size=5, stride=1, padding=2
+ kernel_size=7, stride=1, padding=3
+ """
+
+ def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0):
+ super().__init__()
+ self.inc = nn.Sequential(
+ nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride,
+ padding=padding),
+ nn.ReLU()
+ )
+
+ def forward(self, x):
+ return self.inc(x)
+
+
+class LinearReLU(nn.Module):
+ def __init__(self, in_features, out_features, bias=True):
+ super().__init__()
+ self.inc = nn.Sequential(
+ nn.Linear(in_features=in_features, out_features=out_features, bias=bias),
+ nn.ReLU()
+ )
+
+ def forward(self, x):
+ return self.inc(x)
+
+
+class StackCNN(nn.Module):
+ def __init__(self, layer_num, in_channels, out_channels, kernel_size, stride=1, padding=0):
+ super().__init__()
+
+ self.inc = nn.Sequential(OrderedDict([('conv_layer0',
+ Conv1dReLU(in_channels, out_channels, kernel_size=kernel_size,
+ stride=stride, padding=padding))]))
+ for layer_idx in range(layer_num - 1):
+ self.inc.add_module('conv_layer%d' % (layer_idx + 1),
+ Conv1dReLU(out_channels, out_channels, kernel_size=kernel_size, stride=stride,
+ padding=padding))
+
+ self.inc.add_module('pool_layer', nn.AdaptiveMaxPool1d(1))
+
+ def forward(self, x):
+ return self.inc(x).squeeze(-1)
+
+
+class TargetRepresentation(nn.Module):
+ def __init__(self, block_num, vocab_size, embedding_num):
+ super().__init__()
+ self.embed = nn.Embedding(vocab_size, embedding_num, padding_idx=0)
+ self.block_list = nn.ModuleList()
+ for block_idx in range(block_num):
+ self.block_list.append(
+ StackCNN(block_idx + 1, embedding_num, 96, 3)
+ )
+
+ self.linear = nn.Linear(block_num * 96, 96)
+
+ def forward(self, x):
+ x = self.embed(x).permute(0, 2, 1)
+ feats = [block(x) for block in self.block_list]
+ x = torch.cat(feats, -1)
+ x = self.linear(x)
+
+ return x
+
+
+class NodeLevelBatchNorm(_BatchNorm):
+ r"""
+ Applies Batch Normalization over a batch of graph data.
+ Shape:
+ - Input: [batch_nodes_dim, node_feature_dim]
+ - Output: [batch_nodes_dim, node_feature_dim]
+ batch_nodes_dim: all nodes of a batch graph
+ """
+
+ def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,
+ track_running_stats=True):
+ super(NodeLevelBatchNorm, self).__init__(
+ num_features, eps, momentum, affine, track_running_stats)
+
+ def _check_input_dim(self, input):
+ if input.dim() != 2:
+ raise ValueError('expected 2D input (got {}D input)'
+ .format(input.dim()))
+
+ def forward(self, input):
+ self._check_input_dim(input)
+ if self.momentum is None:
+ exponential_average_factor = 0.0
+ else:
+ exponential_average_factor = self.momentum
+ if self.training and self.track_running_stats:
+ if self.num_batches_tracked is not None:
+ self.num_batches_tracked = self.num_batches_tracked + 1
+ if self.momentum is None:
+ exponential_average_factor = 1.0 / float(self.num_batches_tracked)
+ else:
+ exponential_average_factor = self.momentum
+
+ return torch.functional.F.batch_norm(
+ input, self.running_mean, self.running_var, self.weight, self.bias,
+ self.training or not self.track_running_stats,
+ exponential_average_factor, self.eps)
+
+ def extra_repr(self):
+ return 'num_features={num_features}, eps={eps}, ' \
+ 'affine={affine}'.format(**self.__dict__)
+
+
+class GraphConvBn(nn.Module):
+ def __init__(self, in_channels, out_channels):
+ super().__init__()
+ self.conv = gnn.GraphConv(in_channels, out_channels)
+ self.norm = NodeLevelBatchNorm(out_channels)
+
+ def forward(self, data):
+ x, edge_index, batch = data.x, data.edge_index, data.batch
+ data.x = F.relu(self.norm(self.conv(x, edge_index)))
+
+ return data
+
+
+class DenseLayer(nn.Module):
+ def __init__(self, num_input_features, growth_rate=32, bn_size=4):
+ super().__init__()
+ self.conv1 = GraphConvBn(num_input_features, int(growth_rate * bn_size))
+ self.conv2 = GraphConvBn(int(growth_rate * bn_size), growth_rate)
+
+ def bn_function(self, data):
+ concated_features = torch.cat(data.x, 1)
+ data.x = concated_features
+
+ data = self.conv1(data)
+
+ return data
+
+ def forward(self, data):
+ if isinstance(data.x, Tensor):
+ data.x = [data.x]
+
+ data = self.bn_function(data)
+ data = self.conv2(data)
+
+ return data
+
+
+class DenseBlock(nn.ModuleDict):
+ def __init__(self, num_layers, num_input_features, growth_rate=32, bn_size=4):
+ super().__init__()
+ for i in range(num_layers):
+ layer = DenseLayer(num_input_features + i * growth_rate, growth_rate, bn_size)
+ self.add_module('layer%d' % (i + 1), layer)
+
+ def forward(self, data):
+ features = [data.x]
+ for name, layer in self.items():
+ data = layer(data)
+ features.append(data.x)
+ data.x = features
+
+ data.x = torch.cat(data.x, 1)
+
+ return data
+
+
+class GraphDenseNet(nn.Module):
+ def __init__(self, num_input_features, out_dim, growth_rate=32, block_config=(3, 3, 3, 3), bn_sizes=(2, 3, 4, 4)):
+ super().__init__()
+ self.features = nn.Sequential(OrderedDict([('conv0', GraphConvBn(num_input_features, 32))]))
+ num_input_features = 32
+
+ for i, num_layers in enumerate(block_config):
+ block = DenseBlock(
+ num_layers, num_input_features, growth_rate=growth_rate, bn_size=bn_sizes[i]
+ )
+ self.features.add_module('block%d' % (i + 1), block)
+ num_input_features += int(num_layers * growth_rate)
+
+ trans = GraphConvBn(num_input_features, num_input_features // 2)
+ self.features.add_module("transition%d" % (i + 1), trans)
+ num_input_features = num_input_features // 2
+
+ self.classifier = nn.Linear(num_input_features, out_dim)
+
+ def forward(self, data):
+ data = self.features(data)
+ x = gnn.global_mean_pool(data.x, data.batch)
+ x = self.classifier(x)
+
+ return x
+
+
+def atom_features(atom):
+ encoding = one_of_k_encoding_unk(atom.GetSymbol(),
+ ['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca', 'Fe', 'As',
+ 'Al', 'I', 'B', 'V', 'K', 'Tl', 'Yb', 'Sb', 'Sn', 'Ag', 'Pd', 'Co', 'Se', 'Ti',
+ 'Zn', 'H', 'Li', 'Ge', 'Cu', 'Au', 'Ni', 'Cd', 'In', 'Mn', 'Zr', 'Cr', 'Pt', 'Hg',
+ 'Pb', 'Unknown'])
+ encoding += one_of_k_encoding(atom.GetDegree(), [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) + one_of_k_encoding_unk(
+ atom.GetTotalNumHs(), [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+ encoding += one_of_k_encoding_unk(atom.GetImplicitValence(), [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+ encoding += one_of_k_encoding_unk(atom.GetHybridization(), [
+ Chem.rdchem.HybridizationType.SP, Chem.rdchem.HybridizationType.SP2,
+ Chem.rdchem.HybridizationType.SP3, Chem.rdchem.HybridizationType.SP3D,
+ Chem.rdchem.HybridizationType.SP3D2, 'other'])
+ encoding += [atom.GetIsAromatic()]
+
+ try:
+ encoding += one_of_k_encoding_unk(
+ atom.GetProp('_CIPCode'),
+ ['R', 'S']) + [atom.HasProp('_ChiralityPossible')]
+ except:
+ encoding += [0, 0] + [atom.HasProp('_ChiralityPossible')]
+
+ return np.array(encoding)
diff --git a/deepscreen/models/predictors/mol_trans.py b/deepscreen/models/predictors/mol_trans.py
new file mode 100644
index 0000000000000000000000000000000000000000..224b24a7234a469513fb798173adc048ace6ad05
--- /dev/null
+++ b/deepscreen/models/predictors/mol_trans.py
@@ -0,0 +1,301 @@
+import math
+import copy
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+
+class MolTrans(nn.Module):
+ """
+ Interaction Network with 2D interaction map
+ """
+ def __init__(
+ self,
+ input_dim_drug: 23532,
+ input_dim_target: 16693,
+ max_drug_seq,
+ max_protein_seq,
+ emb_size: 384,
+ dropout_rate: 0.1,
+ # DenseNet
+ scale_down_ratio: 0.25,
+ growth_rate: 20,
+ transition_rate: 0.5,
+ num_dense_blocks: 4,
+ kernal_dense_size: 3,
+ # Encoder
+ intermediate_size: 1536,
+ num_attention_heads: 12,
+ attention_probs_dropout_prob: 0.1,
+ hidden_dropout_prob: 0.1,
+ # flatten_dim: 78192,
+ # batch_size
+ ):
+ super().__init__()
+ self.max_d = max_drug_seq
+ self.max_p = max_protein_seq
+ self.emb_size = emb_size
+ self.dropout_rate = dropout_rate
+
+ # densenet
+ self.scale_down_ratio = scale_down_ratio
+ self.growth_rate = growth_rate
+ self.transition_rate = transition_rate
+ self.num_dense_blocks = num_dense_blocks
+ self.kernal_dense_size = kernal_dense_size
+ # self.batch_size = batch_size
+ self.input_dim_drug = input_dim_drug
+ self.input_dim_target = input_dim_target
+ self.n_layer = 2
+
+ # encoder
+ self.hidden_size = emb_size
+ self.intermediate_size = intermediate_size
+ self.num_attention_heads = num_attention_heads
+ self.attention_probs_dropout_prob = attention_probs_dropout_prob
+ self.hidden_dropout_prob = hidden_dropout_prob
+
+ # self.flatten_dim = flatten_dim
+
+ # specialized embedding with positional one
+ self.demb = Embeddings(self.input_dim_drug, self.emb_size, self.max_d, self.dropout_rate)
+ self.pemb = Embeddings(self.input_dim_target, self.emb_size, self.max_p, self.dropout_rate)
+
+ self.d_encoder = EncoderMultipleLayers(self.n_layer, self.hidden_size, self.intermediate_size,
+ self.num_attention_heads, self.attention_probs_dropout_prob,
+ self.hidden_dropout_prob)
+ self.p_encoder = EncoderMultipleLayers(self.n_layer, self.hidden_size, self.intermediate_size,
+ self.num_attention_heads, self.attention_probs_dropout_prob,
+ self.hidden_dropout_prob)
+
+ self.icnn = nn.Conv2d(1, 3, 3, padding=0)
+
+ self.decoder = nn.Sequential(
+ # nn.Linear(self.flatten_dim, 512),
+ nn.LazyLinear(512),
+ nn.ReLU(True),
+
+ nn.BatchNorm1d(512),
+ nn.Linear(512, 64),
+ nn.ReLU(True),
+
+ nn.BatchNorm1d(64),
+ nn.Linear(64, 32),
+ nn.ReLU(True),
+
+ # # output layer
+ # nn.Linear(32, 1)
+ )
+
+ def forward(self, v_d, v_p):
+ d, d_mask = v_d
+ p, p_mask = v_p
+ ex_d_mask = d_mask.unsqueeze(1).unsqueeze(2)
+ ex_p_mask = p_mask.unsqueeze(1).unsqueeze(2)
+
+ ex_d_mask = (1.0 - ex_d_mask) * -10000.0
+ ex_p_mask = (1.0 - ex_p_mask) * -10000.0
+
+ d_emb = self.demb(d) # batch_size x seq_length x embed_size
+ p_emb = self.pemb(p)
+
+ batch_size = d_emb.size(0)
+
+ # set output_all_encoded_layers be false, to obtain the last layer hidden states only.
+ d_encoded_layers = self.d_encoder(d_emb.float(), ex_d_mask.float())
+ # print(d_encoded_layers.shape)
+ p_encoded_layers = self.p_encoder(p_emb.float(), ex_p_mask.float())
+ # print(p_encoded_layers.shape)
+
+ # repeat to have the same tensor size for aggregation
+ d_aug = torch.unsqueeze(d_encoded_layers, 2).repeat(1, 1, self.max_p, 1) # repeat along protein size
+ p_aug = torch.unsqueeze(p_encoded_layers, 1).repeat(1, self.max_d, 1, 1) # repeat along drug size
+
+ i = d_aug * p_aug # interaction
+ i_v = i.view(batch_size, -1, self.max_d, self.max_p)
+ # batch_size x embed size x max_drug_seq_len x max_protein_seq_len
+ i_v = torch.sum(i_v, dim=1)
+ i_v = torch.unsqueeze(i_v, 1)
+
+ i_v = F.dropout(i_v, p=self.dropout_rate)
+
+ i = self.icnn(i_v).view(batch_size, -1)
+ score = self.decoder(i)
+ return score
+
+
+class LayerNorm(nn.Module):
+ def __init__(self, hidden_size, variance_epsilon=1e-12):
+ super(LayerNorm, self).__init__()
+ self.gamma = nn.Parameter(torch.ones(hidden_size))
+ self.beta = nn.Parameter(torch.zeros(hidden_size))
+ self.variance_epsilon = variance_epsilon
+
+ def forward(self, x):
+ u = x.mean(-1, keepdim=True)
+ s = (x - u).pow(2).mean(-1, keepdim=True)
+ x = (x - u) / torch.sqrt(s + self.variance_epsilon)
+ return self.gamma * x + self.beta
+
+
+class Embeddings(nn.Module):
+ """Construct the embeddings from protein/target, position embeddings.
+ """
+
+ def __init__(self, vocab_size, hidden_size, max_position_size, dropout_rate):
+ super(Embeddings, self).__init__()
+ self.word_embeddings = nn.Embedding(vocab_size, hidden_size)
+ self.position_embeddings = nn.Embedding(max_position_size, hidden_size)
+
+ self.LayerNorm = LayerNorm(hidden_size)
+ self.dropout = nn.Dropout(dropout_rate)
+
+ def forward(self, input_ids):
+ seq_length = input_ids.size(1)
+ position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
+ position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+
+ words_embeddings = self.word_embeddings(input_ids)
+ position_embeddings = self.position_embeddings(position_ids)
+
+ embeddings = words_embeddings + position_embeddings
+ embeddings = self.LayerNorm(embeddings)
+ embeddings = self.dropout(embeddings)
+ return embeddings
+
+
+class SelfAttention(nn.Module):
+ def __init__(self, hidden_size, num_attention_heads, attention_probs_dropout_prob):
+ super(SelfAttention, self).__init__()
+ if hidden_size % num_attention_heads != 0:
+ raise ValueError(
+ "The hidden size (%d) is not a multiple of the number of attention "
+ "heads (%d)" % (hidden_size, num_attention_heads))
+ self.num_attention_heads = num_attention_heads
+ self.attention_head_size = int(hidden_size / num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.query = nn.Linear(hidden_size, self.all_head_size)
+ self.key = nn.Linear(hidden_size, self.all_head_size)
+ self.value = nn.Linear(hidden_size, self.all_head_size)
+
+ self.dropout = nn.Dropout(attention_probs_dropout_prob)
+
+ def transpose_for_scores(self, x):
+ new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+ x = x.view(*new_x_shape)
+ return x.permute(0, 2, 1, 3)
+
+ def forward(self, hidden_states, attention_mask):
+ mixed_query_layer = self.query(hidden_states)
+ mixed_key_layer = self.key(hidden_states)
+ mixed_value_layer = self.value(hidden_states)
+
+ query_layer = self.transpose_for_scores(mixed_query_layer)
+ key_layer = self.transpose_for_scores(mixed_key_layer)
+ value_layer = self.transpose_for_scores(mixed_value_layer)
+
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+ attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+
+ attention_scores = attention_scores + attention_mask
+
+ # Normalize the attention scores to probabilities.
+ attention_probs = nn.Softmax(dim=-1)(attention_scores)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs = self.dropout(attention_probs)
+
+ context_layer = torch.matmul(attention_probs, value_layer)
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(*new_context_layer_shape)
+ return context_layer
+
+
+class SelfOutput(nn.Module):
+ def __init__(self, hidden_size, hidden_dropout_prob):
+ super(SelfOutput, self).__init__()
+ self.dense = nn.Linear(hidden_size, hidden_size)
+ self.LayerNorm = LayerNorm(hidden_size)
+ self.dropout = nn.Dropout(hidden_dropout_prob)
+
+ def forward(self, hidden_states, input_tensor):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class Attention(nn.Module):
+ def __init__(self, hidden_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob):
+ super(Attention, self).__init__()
+ self.self = SelfAttention(hidden_size, num_attention_heads, attention_probs_dropout_prob)
+ self.output = SelfOutput(hidden_size, hidden_dropout_prob)
+
+ def forward(self, input_tensor, attention_mask):
+ self_output = self.self(input_tensor, attention_mask)
+ attention_output = self.output(self_output, input_tensor)
+ return attention_output
+
+
+class Intermediate(nn.Module):
+ def __init__(self, hidden_size, intermediate_size):
+ super(Intermediate, self).__init__()
+ self.dense = nn.Linear(hidden_size, intermediate_size)
+
+ def forward(self, hidden_states):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = F.relu(hidden_states)
+ return hidden_states
+
+
+class Output(nn.Module):
+ def __init__(self, intermediate_size, hidden_size, hidden_dropout_prob):
+ super(Output, self).__init__()
+ self.dense = nn.Linear(intermediate_size, hidden_size)
+ self.LayerNorm = LayerNorm(hidden_size)
+ self.dropout = nn.Dropout(hidden_dropout_prob)
+
+ def forward(self, hidden_states, input_tensor):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class Encoder(nn.Module):
+ def __init__(self, hidden_size, intermediate_size, num_attention_heads, attention_probs_dropout_prob,
+ hidden_dropout_prob):
+ super(Encoder, self).__init__()
+ self.attention = Attention(hidden_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob)
+ self.intermediate = Intermediate(hidden_size, intermediate_size)
+ self.output = Output(intermediate_size, hidden_size, hidden_dropout_prob)
+
+ def forward(self, hidden_states, attention_mask):
+ attention_output = self.attention(hidden_states, attention_mask)
+ intermediate_output = self.intermediate(attention_output)
+ layer_output = self.output(intermediate_output, attention_output)
+ return layer_output
+
+
+class EncoderMultipleLayers(nn.Module):
+ def __init__(self, n_layer, hidden_size, intermediate_size, num_attention_heads, attention_probs_dropout_prob,
+ hidden_dropout_prob):
+ super().__init__()
+ layer = Encoder(hidden_size, intermediate_size, num_attention_heads, attention_probs_dropout_prob,
+ hidden_dropout_prob)
+ self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(n_layer)])
+
+ def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True):
+ all_encoder_layers = []
+ for layer_module in self.layer:
+ hidden_states = layer_module(hidden_states, attention_mask)
+ # if output_all_encoded_layers:
+ # all_encoder_layers.append(hidden_states)
+ # if not output_all_encoded_layers:
+ # all_encoder_layers.append(hidden_states)
+ return hidden_states
diff --git a/deepscreen/models/predictors/monn.py b/deepscreen/models/predictors/monn.py
new file mode 100644
index 0000000000000000000000000000000000000000..ccbbd764d8f6d6cb334fe801f518948818ac94de
--- /dev/null
+++ b/deepscreen/models/predictors/monn.py
@@ -0,0 +1,299 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+# some predefined parameters
+elem_list = ['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca', 'Fe', 'As', 'Al', 'I', 'B', 'V', 'K',
+ 'Tl', 'Yb', 'Sb', 'Sn', 'Ag', 'Pd', 'Co', 'Se', 'Ti', 'Zn', 'H', 'Li', 'Ge', 'Cu', 'Au', 'Ni', 'Cd', 'In',
+ 'Mn', 'Zr', 'Cr', 'Pt', 'Hg', 'Pb', 'W', 'Ru', 'Nb', 'Re', 'Te', 'Rh', 'Tc', 'Ba', 'Bi', 'Hf', 'Mo', 'U',
+ 'Sm', 'Os', 'Ir', 'Ce', 'Gd', 'Ga', 'Cs', 'unknown']
+atom_fdim = len(elem_list) + 6 + 6 + 6 + 1
+bond_fdim = 6
+max_nb = 6
+
+
+class MaskedBCELoss(nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.criterion = nn.BCEWithLogitsLoss(reduction='none')
+
+ def forward(self, preds, target):
+ pairwise_pred, pairwise_mask, vertex_mask, seq_mask = preds
+ batch_size = pairwise_pred.size(0)
+ loss_all = self.criterion(pairwise_pred, target)
+ loss_mask = torch.matmul(vertex_mask.view(batch_size, -1, 1),
+ seq_mask.view(batch_size, 1, -1)) * pairwise_mask.view(-1, 1, 1)
+ loss = torch.sum(loss_all * loss_mask) / torch.sum(pairwise_mask).clamp(min=1e-10)
+ return loss
+
+
+# FIXME: the dataset construction doesn't freaking work yet (Dataset_construction_protocol.txt)
+class MONN(nn.Module):
+ # init_A, init_B, init_W = loading_emb(measure)
+ # net = Net(init_A, init_B, init_W, params)
+ def __init__(self, init_atom_features, init_bond_features, init_word_features, params):
+ super().__init__()
+ self.init_atom_features = init_atom_features
+ self.init_bond_features = init_bond_features
+ self.init_word_features = init_word_features
+ self.criterion1 = nn.MSELoss()
+ self.criterion2 = MaskedBCELoss()
+
+ """hyper part"""
+ GNN_depth, inner_CNN_depth, DMA_depth, k_head, kernel_size, hidden_size1, hidden_size2 = params
+ self.GNN_depth = GNN_depth
+ self.inner_CNN_depth = inner_CNN_depth
+ self.DMA_depth = DMA_depth
+ self.k_head = k_head
+ self.kernel_size = kernel_size
+ self.hidden_size1 = hidden_size1
+ self.hidden_size2 = hidden_size2
+
+ """GraphConv Module"""
+ self.vertex_embedding = nn.Linear(atom_fdim,
+ self.hidden_size1) # first transform vertex features into hidden representations
+
+ # GWM parameters
+ self.W_a_main = nn.ModuleList(
+ [nn.ModuleList([nn.Linear(self.hidden_size1, self.hidden_size1) for i in range(self.k_head)]) for i in
+ range(self.GNN_depth)])
+ self.W_a_super = nn.ModuleList(
+ [nn.ModuleList([nn.Linear(self.hidden_size1, self.hidden_size1) for i in range(self.k_head)]) for i in
+ range(self.GNN_depth)])
+ self.W_main = nn.ModuleList(
+ [nn.ModuleList([nn.Linear(self.hidden_size1, self.hidden_size1) for i in range(self.k_head)]) for i in
+ range(self.GNN_depth)])
+ self.W_bmm = nn.ModuleList(
+ [nn.ModuleList([nn.Linear(self.hidden_size1, 1) for i in range(self.k_head)]) for i in
+ range(self.GNN_depth)])
+
+ self.W_super = nn.ModuleList([nn.Linear(self.hidden_size1, self.hidden_size1) for i in range(self.GNN_depth)])
+ self.W_main_to_super = nn.ModuleList(
+ [nn.Linear(self.hidden_size1 * self.k_head, self.hidden_size1) for i in range(self.GNN_depth)])
+ self.W_super_to_main = nn.ModuleList(
+ [nn.Linear(self.hidden_size1, self.hidden_size1) for i in range(self.GNN_depth)])
+
+ self.W_zm1 = nn.ModuleList([nn.Linear(self.hidden_size1, self.hidden_size1) for i in range(self.GNN_depth)])
+ self.W_zm2 = nn.ModuleList([nn.Linear(self.hidden_size1, self.hidden_size1) for i in range(self.GNN_depth)])
+ self.W_zs1 = nn.ModuleList([nn.Linear(self.hidden_size1, self.hidden_size1) for i in range(self.GNN_depth)])
+ self.W_zs2 = nn.ModuleList([nn.Linear(self.hidden_size1, self.hidden_size1) for i in range(self.GNN_depth)])
+ self.GRU_main = nn.GRUCell(self.hidden_size1, self.hidden_size1)
+ self.GRU_super = nn.GRUCell(self.hidden_size1, self.hidden_size1)
+
+ # WLN parameters
+ self.label_U2 = nn.ModuleList([nn.Linear(self.hidden_size1 + bond_fdim, self.hidden_size1) for i in
+ range(self.GNN_depth)]) # assume no edge feature transformation
+ self.label_U1 = nn.ModuleList(
+ [nn.Linear(self.hidden_size1 * 2, self.hidden_size1) for i in range(self.GNN_depth)])
+
+ """CNN-RNN Module"""
+ # CNN parameters
+ self.embed_seq = nn.Embedding(len(self.init_word_features), 20, padding_idx=0)
+ self.embed_seq.weight = nn.Parameter(self.init_word_features)
+ self.embed_seq.weight.requires_grad = False
+
+ self.conv_first = nn.Conv1d(20, self.hidden_size1, kernel_size=self.kernel_size,
+ padding=(self.kernel_size - 1) / 2)
+ self.conv_last = nn.Conv1d(self.hidden_size1, self.hidden_size1, kernel_size=self.kernel_size,
+ padding=(self.kernel_size - 1) / 2)
+
+ self.plain_CNN = nn.ModuleList([])
+ for i in range(self.inner_CNN_depth):
+ self.plain_CNN.append(nn.Conv1d(self.hidden_size1, self.hidden_size1, kernel_size=self.kernel_size,
+ padding=(self.kernel_size - 1) / 2))
+
+ # RNN parameters
+ # self.rnn = SRU(input_size=self.hidden_size1, hidden_size=self.hidden_size1/2, num_layers=1, dropout=0.0, bidirectional=True, layer_norm=False, rescale=True)
+
+ """Affinity Prediction Module"""
+ self.super_final = nn.Linear(self.hidden_size1, self.hidden_size2)
+ self.c_final = nn.Linear(self.hidden_size1, self.hidden_size2)
+ self.p_final = nn.Linear(self.hidden_size1, self.hidden_size2)
+
+ # DMA parameters
+ self.mc0 = nn.Linear(hidden_size2, hidden_size2)
+ self.mp0 = nn.Linear(hidden_size2, hidden_size2)
+
+ self.mc1 = nn.ModuleList([nn.Linear(self.hidden_size2, self.hidden_size2) for i in range(self.DMA_depth)])
+ self.mp1 = nn.ModuleList([nn.Linear(self.hidden_size2, self.hidden_size2) for i in range(self.DMA_depth)])
+
+ self.hc0 = nn.ModuleList([nn.Linear(self.hidden_size2, self.hidden_size2) for i in range(self.DMA_depth)])
+ self.hp0 = nn.ModuleList([nn.Linear(self.hidden_size2, self.hidden_size2) for i in range(self.DMA_depth)])
+ self.hc1 = nn.ModuleList([nn.Linear(self.hidden_size2, 1) for i in range(self.DMA_depth)])
+ self.hp1 = nn.ModuleList([nn.Linear(self.hidden_size2, 1) for i in range(self.DMA_depth)])
+
+ self.c_to_p_transform = nn.ModuleList(
+ [nn.Linear(self.hidden_size2, self.hidden_size2) for i in range(self.DMA_depth)])
+ self.p_to_c_transform = nn.ModuleList(
+ [nn.Linear(self.hidden_size2, self.hidden_size2) for i in range(self.DMA_depth)])
+
+ self.GRU_dma = nn.GRUCell(self.hidden_size2, self.hidden_size2)
+ # Output layer
+ self.W_out = nn.Linear(self.hidden_size2 * self.hidden_size2 * 2, 1)
+
+ """Pairwise Interaction Prediction Module"""
+ self.pairwise_compound = nn.Linear(self.hidden_size1, self.hidden_size1)
+ self.pairwise_protein = nn.Linear(self.hidden_size1, self.hidden_size1)
+
+ def mask_softmax(self, a, mask, dim=-1):
+ a_max = torch.max(a, dim, keepdim=True)[0]
+ a_exp = torch.exp(a - a_max)
+ a_exp = a_exp * mask
+ a_softmax = a_exp / (torch.sum(a_exp, dim, keepdim=True) + 1e-6)
+ return a_softmax
+
+ def graph_conv_module(self, batch_size, vertex_mask, vertex, edge, atom_adj, bond_adj, nbs_mask):
+ n_vertex = vertex_mask.size(1)
+
+ # initial features
+ vertex_initial = torch.index_select(self.init_atom_features, 0, vertex.view(-1))
+ vertex_initial = vertex_initial.view(batch_size, -1, atom_fdim)
+ edge_initial = torch.index_select(self.init_bond_features, 0, edge.view(-1))
+ edge_initial = edge_initial.view(batch_size, -1, bond_fdim)
+
+ vertex_feature = F.leaky_relu(self.vertex_embedding(vertex_initial), 0.1)
+ super_feature = torch.sum(vertex_feature * vertex_mask.view(batch_size, -1, 1), dim=1, keepdim=True)
+
+ for GWM_iter in range(self.GNN_depth):
+ # prepare main node features
+ for k in range(self.k_head):
+ a_main = torch.tanh(self.W_a_main[GWM_iter][k](vertex_feature))
+ a_super = torch.tanh(self.W_a_super[GWM_iter][k](super_feature))
+ a = self.W_bmm[GWM_iter][k](a_main * super_feature)
+ attn = self.mask_softmax(a.view(batch_size, -1), vertex_mask).view(batch_size, -1, 1)
+ k_main_to_super = torch.bmm(attn.transpose(1, 2), self.W_main[GWM_iter][k](vertex_feature))
+ if k == 0:
+ m_main_to_super = k_main_to_super
+ else:
+ m_main_to_super = torch.cat([m_main_to_super, k_main_to_super], dim=-1) # concat k-head
+ main_to_super = torch.tanh(self.W_main_to_super[GWM_iter](m_main_to_super))
+ main_self = self.wln_unit(batch_size, vertex_mask, vertex_feature, edge_initial, atom_adj, bond_adj,
+ nbs_mask, GWM_iter)
+
+ super_to_main = torch.tanh(self.W_super_to_main[GWM_iter](super_feature))
+ super_self = torch.tanh(self.W_super[GWM_iter](super_feature))
+ # warp gate and GRU for update main node features, use main_self and super_to_main
+ z_main = torch.sigmoid(self.W_zm1[GWM_iter](main_self) + self.W_zm2[GWM_iter](super_to_main))
+ hidden_main = (1 - z_main) * main_self + z_main * super_to_main
+ vertex_feature = self.GRU_main(hidden_main.view(-1, self.hidden_size1),
+ vertex_feature.view(-1, self.hidden_size1))
+ vertex_feature = vertex_feature.view(batch_size, n_vertex, self.hidden_size1)
+ # warp gate and GRU for update super node features
+ z_supper = torch.sigmoid(self.W_zs1[GWM_iter](super_self) + self.W_zs2[GWM_iter](main_to_super))
+ hidden_super = (1 - z_supper) * super_self + z_supper * main_to_super
+ super_feature = self.GRU_super(hidden_super.view(batch_size, self.hidden_size1),
+ super_feature.view(batch_size, self.hidden_size1))
+ super_feature = super_feature.view(batch_size, 1, self.hidden_size1)
+
+ return vertex_feature, super_feature
+
+ def wln_unit(self, batch_size, vertex_mask, vertex_features, edge_initial, atom_adj, bond_adj, nbs_mask, GNN_iter):
+ n_vertex = vertex_mask.size(1)
+ n_nbs = nbs_mask.size(2)
+
+ vertex_mask = vertex_mask.view(batch_size, n_vertex, 1)
+ nbs_mask = nbs_mask.view(batch_size, n_vertex, n_nbs, 1)
+
+ vertex_nei = torch.index_select(vertex_features.view(-1, self.hidden_size1), 0, atom_adj).view(batch_size,
+ n_vertex, n_nbs,
+ self.hidden_size1)
+ edge_nei = torch.index_select(edge_initial.view(-1, bond_fdim), 0, bond_adj).view(batch_size, n_vertex, n_nbs,
+ bond_fdim)
+
+ # Weisfeiler Lehman relabelling
+ l_nei = torch.cat((vertex_nei, edge_nei), -1)
+ nei_label = F.leaky_relu(self.label_U2[GNN_iter](l_nei), 0.1)
+ nei_label = torch.sum(nei_label * nbs_mask, dim=-2)
+ new_label = torch.cat((vertex_features, nei_label), 2)
+ new_label = self.label_U1[GNN_iter](new_label)
+ vertex_features = F.leaky_relu(new_label, 0.1)
+
+ return vertex_features
+
+ def cnn_module(self, sequence):
+ ebd = self.embed_seq(sequence)
+ ebd = ebd.transpose(1, 2)
+ x = F.leaky_relu(self.conv_first(ebd), 0.1)
+
+ for i in range(self.inner_CNN_depth):
+ x = self.plain_CNN[i](x)
+ x = F.leaky_relu(x, 0.1)
+
+ x = F.leaky_relu(self.conv_last(x), 0.1)
+ H = x.transpose(1, 2)
+ # H, hidden = self.rnn(H)
+
+ return H
+
+ def pairwise_pred_module(self, batch_size, comp_feature, prot_feature, vertex_mask, seq_mask):
+ pairwise_c_feature = F.leaky_relu(self.pairwise_compound(comp_feature), 0.1)
+ pairwise_p_feature = F.leaky_relu(self.pairwise_protein(prot_feature), 0.1)
+ pairwise_pred = torch.matmul(pairwise_c_feature, pairwise_p_feature.transpose(1, 2))
+ # TODO: difference between the pairwise_mask here and in the data?
+ pairwise_mask = torch.matmul(vertex_mask.view(batch_size, -1, 1), seq_mask.view(batch_size, 1, -1))
+ pairwise_pred = pairwise_pred * pairwise_mask
+ return pairwise_pred
+
+ def affinity_pred_module(self, batch_size, comp_feature, prot_feature, super_feature, vertex_mask, seq_mask,
+ pairwise_pred):
+ comp_feature = F.leaky_relu(self.c_final(comp_feature), 0.1)
+ prot_feature = F.leaky_relu(self.p_final(prot_feature), 0.1)
+ super_feature = F.leaky_relu(self.super_final(super_feature.view(batch_size, -1)), 0.1)
+
+ cf, pf = self.dma_gru(batch_size, comp_feature, vertex_mask, prot_feature, seq_mask, pairwise_pred)
+
+ cf = torch.cat([cf.view(batch_size, -1), super_feature.view(batch_size, -1)], dim=1)
+ kroneck = F.leaky_relu(
+ torch.matmul(cf.view(batch_size, -1, 1), pf.view(batch_size, 1, -1)).view(batch_size, -1), 0.1)
+
+ affinity_pred = self.W_out(kroneck)
+ return affinity_pred
+
+ def dma_gru(self, batch_size, comp_feats, vertex_mask, prot_feats, seq_mask, pairwise_pred):
+ vertex_mask = vertex_mask.view(batch_size, -1, 1)
+ seq_mask = seq_mask.view(batch_size, -1, 1)
+
+ cf = torch.Tensor()
+ pf = torch.Tensor()
+
+ c0 = torch.sum(comp_feats * vertex_mask, dim=1) / torch.sum(vertex_mask, dim=1)
+ p0 = torch.sum(prot_feats * seq_mask, dim=1) / torch.sum(seq_mask, dim=1)
+
+ m = c0 * p0
+ for DMA_iter in range(self.DMA_depth):
+ c_to_p = torch.matmul(pairwise_pred.transpose(1, 2),
+ F.tanh(self.c_to_p_transform[DMA_iter](comp_feats))) # batch * n_residue * hidden
+ p_to_c = torch.matmul(pairwise_pred,
+ F.tanh(self.p_to_c_transform[DMA_iter](prot_feats))) # batch * n_vertex * hidden
+
+ c_tmp = F.tanh(self.hc0[DMA_iter](comp_feats)) * F.tanh(self.mc1[DMA_iter](m)).view(batch_size, 1,
+ -1) * p_to_c
+ p_tmp = F.tanh(self.hp0[DMA_iter](prot_feats)) * F.tanh(self.mp1[DMA_iter](m)).view(batch_size, 1,
+ -1) * c_to_p
+
+ c_att = self.mask_softmax(self.hc1[DMA_iter](c_tmp).view(batch_size, -1), vertex_mask.view(batch_size, -1))
+ p_att = self.mask_softmax(self.hp1[DMA_iter](p_tmp).view(batch_size, -1), seq_mask.view(batch_size, -1))
+
+ cf = torch.sum(comp_feats * c_att.view(batch_size, -1, 1), dim=1)
+ pf = torch.sum(prot_feats * p_att.view(batch_size, -1, 1), dim=1)
+
+ m = self.GRU_dma(m, cf * pf)
+
+ return cf, pf
+
+ #
+ def forward(self, enc_drug, enc_protein):
+ vertex_mask, vertex, edge, atom_adj, bond_adj, nbs_mask = enc_drug
+ seq_mask, sequence = enc_protein
+
+ batch_size = vertex.size(0)
+
+ atom_feature, super_feature = self.graph_conv_module(batch_size, vertex_mask, vertex, edge, atom_adj, bond_adj,
+ nbs_mask)
+ prot_feature = self.cnn_module(sequence)
+
+ pairwise_pred = self.pairwise_pred_module(batch_size, atom_feature, prot_feature, vertex_mask, seq_mask)
+ affinity_pred = self.affinity_pred_module(batch_size, atom_feature, prot_feature, super_feature, vertex_mask,
+ seq_mask, pairwise_pred)
+
+ return affinity_pred, pairwise_pred
diff --git a/deepscreen/models/predictors/multi_entity.py b/deepscreen/models/predictors/multi_entity.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ced64056001bc776aeb836036b7c72ef5546ed0
--- /dev/null
+++ b/deepscreen/models/predictors/multi_entity.py
@@ -0,0 +1,46 @@
+from itertools import zip_longest
+from typing import Sequence, Dict, Union
+
+import torch
+from lightning_utilities.core.rank_zero import rank_zero_warn
+from torch import nn
+
+
+class MultiEntityInteraction(nn.Module):
+ def __init__(
+ self,
+ encoders: Union[nn.Module, Sequence[nn.Module], Dict[str, nn.Module]],
+ decoders: Union[nn.Module, Sequence[nn.Module], Dict[str, nn.Module]],
+ ):
+ super().__init__()
+
+ # Add new encoders to MultiEntityInteraction.
+ if isinstance(encoders, nn.Module):
+ # set compatible with original type expectations
+ encoders = [encoders]
+ elif isinstance(encoders, Sequence):
+ # Check all values are encoders
+ for i, encoder in enumerate(encoders):
+ if not isinstance(encoder, nn.Module):
+ raise ValueError(
+ f"Value {encoder} at index {i} is not an instance of `nn.Module`."
+ )
+ elif isinstance(encoders, dict):
+ # Check all values are encoders
+ for k, encoder in encoders.items():
+ if not isinstance(encoder, nn.Module):
+ raise ValueError(
+ f"Value {encoder} at key {k} is not an instance of `nn.Module`."
+ )
+ else:
+ raise ValueError(
+ "Unknown input to MultiEntityInteraction. Expected, `nn.Module`, or `dict`/`sequence` of the"
+ f" previous, but got {encoders}"
+ )
+ self.encoders = encoders
+ self.decoders = decoders
+
+ def forward(self, inputs):
+ preds = [encoder(x) for encoder, x in zip_longest(self.encoders, inputs)]
+
+ return preds
diff --git a/deepscreen/models/predictors/transformer_cpi.py b/deepscreen/models/predictors/transformer_cpi.py
new file mode 100644
index 0000000000000000000000000000000000000000..afbe42ee103b0a43e5833c827c56c4f21da887da
--- /dev/null
+++ b/deepscreen/models/predictors/transformer_cpi.py
@@ -0,0 +1,308 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+
+class TransformerCPI(nn.Module):
+ def __init__(self, protein_dim, hidden_dim, n_layers, kernel_size, dropout, n_heads, pf_dim, atom_dim=34):
+ super().__init__()
+
+ self.encoder = Encoder(protein_dim, hidden_dim, n_layers, kernel_size, dropout)
+ self.decoder = Decoder(atom_dim, hidden_dim, n_layers, n_heads, pf_dim, DecoderLayer, SelfAttention,
+ PositionwiseFeedforward, dropout)
+ self.weight = nn.Parameter(torch.FloatTensor(atom_dim, atom_dim))
+ self.init_weight()
+
+ def init_weight(self):
+ stdv = 1. / math.sqrt(self.weight.size(1))
+ self.weight.data.uniform_(-stdv, stdv)
+
+ def gcn(self, input, adj):
+ # input =[batch,num_node, atom_dim]
+ # adj = [batch,num_node, num_node]
+ support = torch.matmul(input, self.weight)
+ # support =[batch,num_node,atom_dim]
+ output = torch.bmm(adj, support)
+ # output = [batch,num_node,atom_dim]
+ return output
+
+ def forward(self, compound, adj, protein, atom_num, protein_num):
+ # compound = [batch,atom_num, atom_dim]
+ # adj = [batch,atom_num, atom_num]
+ # protein = [batch,protein len, 100]
+ compound_max_len = compound.shape[1]
+ protein_max_len = protein.shape[1]
+ compound_mask, protein_mask = self.make_masks(atom_num, protein_num, compound_max_len, protein_max_len)
+ compound = self.gcn(compound, adj)
+ # compound = torch.unsqueeze(compound, dim=0)
+ # compound = [batch size=1 ,atom_num, atom_dim]
+
+ # protein = torch.unsqueeze(protein, dim=0)
+ # protein =[ batch size=1,protein len, protein_dim]
+ enc_src = self.encoder(protein)
+ # enc_src = [batch size, protein len, hid dim]
+
+ out = self.decoder(compound, enc_src, compound_mask, protein_mask)
+ # out = [batch size, 2]
+ # out = torch.squeeze(out, dim=0)
+ return out
+
+ @staticmethod
+ def make_masks(atom_num, protein_num, compound_max_len, protein_max_len):
+ n_atom = len(atom_num) # batch size
+ compound_mask = torch.zeros((n_atom, compound_max_len))
+ protein_mask = torch.zeros((n_atom, protein_max_len))
+ for i in range(n_atom):
+ compound_mask[i, :atom_num[i]] = 1
+ protein_mask[i, :protein_num[i]] = 1
+ compound_mask = compound_mask.unsqueeze(1).unsqueeze(3)
+ protein_mask = protein_mask.unsqueeze(1).unsqueeze(2)
+ return compound_mask, protein_mask
+
+ @staticmethod
+ def pack(atoms, adjs, proteins, labels):
+ atoms_len = 0
+ proteins_len = 0
+ N = len(atoms)
+
+ atom_num = []
+ for atom in atoms:
+ atom_num.append(atom.shape[0])
+ if atom.shape[0] >= atoms_len:
+ atoms_len = atom.shape[0]
+
+ protein_num = []
+ for protein in proteins:
+ protein_num.append(protein.shape[0])
+ if protein.shape[0] >= proteins_len:
+ proteins_len = protein.shape[0]
+
+ atoms_new = torch.zeros((N, atoms_len, 34))
+ for i, atom in enumerate(atoms):
+ a_len = atom.shape[0]
+ atoms_new[i, :a_len, :] = atom
+
+ adjs_new = torch.zeros((N, atoms_len, atoms_len))
+ for i, adj in adjs:
+ a_len = adj.shape[0]
+ adj = adj + torch.eye(a_len)
+ adjs_new[i, :a_len, :a_len] = adj
+
+ proteins_new = torch.zeros((N, proteins_len, 100))
+ for i, protein in enumerate(proteins):
+ a_len = protein.shape[0]
+ proteins_new[i, :a_len, :] = protein
+
+ return atoms_new, adjs_new, proteins_new, atom_num, protein_num
+
+
+class SelfAttention(nn.Module):
+ def __init__(self, hidden_dim, n_heads, dropout):
+ super().__init__()
+
+ self.hidden_dim = hidden_dim
+ self.n_heads = n_heads
+
+ assert hidden_dim % n_heads == 0
+
+ self.w_q = nn.Linear(hidden_dim, hidden_dim)
+ self.w_k = nn.Linear(hidden_dim, hidden_dim)
+ self.w_v = nn.Linear(hidden_dim, hidden_dim)
+
+ self.fc = nn.Linear(hidden_dim, hidden_dim)
+
+ self.do = nn.Dropout(dropout)
+
+ self.scale = torch.sqrt(torch.FloatTensor([hidden_dim // n_heads]))
+
+ def forward(self, query, key, value, mask=None):
+ bsz = query.shape[0]
+
+ # query = key = value [batch size, sent len, hid dim]
+
+ q = self.w_q(query)
+ k = self.w_k(key)
+ v = self.w_v(value)
+
+ # q, k, v = [batch size, sent len, hid dim]
+
+ q = q.view(bsz, -1, self.n_heads, self.hidden_dim // self.n_heads).permute(0, 2, 1, 3)
+ k = k.view(bsz, -1, self.n_heads, self.hidden_dim // self.n_heads).permute(0, 2, 1, 3)
+ v = v.view(bsz, -1, self.n_heads, self.hidden_dim // self.n_heads).permute(0, 2, 1, 3)
+
+ # k, v = [batch size, n heads, sent len_K, hid dim // n heads]
+ # q = [batch size, n heads, sent len_q, hid dim // n heads]
+ energy = torch.matmul(q, k.permute(0, 1, 3, 2)) / self.scale
+
+ # energy = [batch size, n heads, sent len_Q, sent len_K]
+ if mask is not None:
+ energy = energy.masked_fill(mask == 0, -1e10)
+
+ attention = self.do(F.softmax(energy, dim=-1))
+
+ # attention = [batch size, n heads, sent len_Q, sent len_K]
+
+ x = torch.matmul(attention, v)
+
+ # x = [batch size, n heads, sent len_Q, hid dim // n heads]
+
+ x = x.permute(0, 2, 1, 3).contiguous()
+
+ # x = [batch size, sent len_Q, n heads, hid dim // n heads]
+
+ x = x.view(bsz, -1, self.n_heads * (self.hidden_dim // self.n_heads))
+
+ # x = [batch size, src sent len_Q, hid dim]
+
+ x = self.fc(x)
+
+ # x = [batch size, sent len_Q, hid dim]
+
+ return x
+
+
+class Encoder(nn.Module):
+ """protein feature extraction."""
+
+ def __init__(self, protein_dim, hidden_dim, n_layers, kernel_size, dropout):
+ super().__init__()
+
+ assert kernel_size % 2 == 1, "Kernel size must be odd (for now)"
+
+ self.input_dim = protein_dim
+ self.hidden_dim = hidden_dim
+ self.kernel_size = kernel_size
+ self.dropout = dropout
+ self.n_layers = n_layers
+ # self.pos_embedding = nn.Embedding(1000, hidden_dim)
+ self.scale = torch.sqrt(torch.FloatTensor([0.5]))
+ self.convs = nn.ModuleList(
+ [nn.Conv1d(hidden_dim, 2 * hidden_dim, kernel_size, padding=(kernel_size - 1) // 2) for _ in
+ range(self.n_layers)]) # convolutional layers
+ self.dropout = nn.Dropout(dropout)
+ self.fc = nn.Linear(self.input_dim, self.hidden_dim)
+ self.gn = nn.GroupNorm(8, hidden_dim * 2)
+ self.ln = nn.LayerNorm(hidden_dim)
+
+ def forward(self, protein):
+ # pos = torch.arange(0, protein.shape[1]).unsqueeze(0).repeat(protein.shape[0], 1)
+ # protein = protein + self.pos_embedding(pos)
+ # protein = [batch size, protein len,protein_dim]
+ conv_input = self.fc(protein)
+ # conv_input=[batch size,protein len,hid dim]
+ # permute for convolutional layer
+ conv_input = conv_input.permute(0, 2, 1)
+ # conv_input = [batch size, hid dim, protein len]
+ for i, conv in enumerate(self.convs):
+ # pass through convolutional layer
+ conved = conv(self.dropout(conv_input))
+ # conved = [batch size, 2*hid dim, protein len]
+
+ # pass through GLU activation function
+ conved = F.glu(conved, dim=1)
+ # conved = [batch size, hid dim, protein len]
+
+ # apply residual connection / high way
+ conved = (conved + conv_input) * self.scale
+ # conved = [batch size, hid dim, protein len]
+
+ # set conv_input to conved for next loop iteration
+ conv_input = conved
+
+ conved = conved.permute(0, 2, 1)
+ # conved = [batch size,protein len,hid dim]
+ conved = self.ln(conved)
+ return conved
+
+
+class PositionwiseFeedforward(nn.Module):
+ def __init__(self, hidden_dim, pf_dim, dropout):
+ super().__init__()
+
+ self.hidden_dim = hidden_dim
+ self.pf_dim = pf_dim
+
+ self.fc_1 = nn.Conv1d(hidden_dim, pf_dim, 1) # convolution neural units
+ self.fc_2 = nn.Conv1d(pf_dim, hidden_dim, 1) # convolution neural units
+
+ self.do = nn.Dropout(dropout)
+
+ def forward(self, x):
+ # x = [batch size, sent len, hid dim]
+ x = x.permute(0, 2, 1) # x = [batch size, hid dim, sent len]
+ x = self.do(F.relu(self.fc_1(x))) # x = [batch size, pf dim, sent len]
+ x = self.fc_2(x) # x = [batch size, hid dim, sent len]
+ x = x.permute(0, 2, 1) # x = [batch size, sent len, hid dim]
+
+ return x
+
+
+class DecoderLayer(nn.Module):
+ def __init__(self, hidden_dim, n_heads, pf_dim, self_attention, positionwise_feedforward, dropout):
+ super().__init__()
+ self.ln = nn.LayerNorm(hidden_dim)
+ self.sa = self_attention(hidden_dim, n_heads, dropout)
+ self.ea = self_attention(hidden_dim, n_heads, dropout)
+ self.pf = positionwise_feedforward(hidden_dim, pf_dim, dropout)
+ self.do = nn.Dropout(dropout)
+
+ def forward(self, trg, src, trg_mask=None, src_mask=None):
+ # trg = [batch_size, compound len, atom_dim]
+ # src = [batch_size, protein len, hidden_dim] # encoder output
+ # trg_mask = [batch size, compound sent len]
+ # src_mask = [batch size, protein len]
+ trg = self.ln(trg + self.do(self.sa(trg, trg, trg, trg_mask)))
+ trg = self.ln(trg + self.do(self.ea(trg, src, src, src_mask)))
+ trg = self.ln(trg + self.do(self.pf(trg)))
+
+ return trg
+
+
+class Decoder(nn.Module):
+ """ compound feature extraction."""
+
+ def __init__(self, atom_dim, hidden_dim, n_layers, n_heads, pf_dim, decoder_layer, self_attention,
+ positionwise_feedforward, dropout):
+ super().__init__()
+ self.ln = nn.LayerNorm(hidden_dim)
+ self.output_dim = atom_dim
+ self.hidden_dim = hidden_dim
+ self.n_layers = n_layers
+ self.n_heads = n_heads
+ self.pf_dim = pf_dim
+ self.decoder_layer = decoder_layer
+ self.self_attention = self_attention
+ self.positionwise_feedforward = positionwise_feedforward
+ self.dropout = dropout
+ self.sa = self_attention(hidden_dim, n_heads, dropout)
+ self.layers = nn.ModuleList(
+ [decoder_layer(hidden_dim, n_heads, pf_dim, self_attention, positionwise_feedforward, dropout)
+ for _ in range(n_layers)])
+ self.ft = nn.Linear(atom_dim, hidden_dim)
+ self.do = nn.Dropout(dropout)
+ self.fc_1 = nn.Linear(hidden_dim, 256)
+ self.fc_2 = nn.Linear(256, 2)
+ self.gn = nn.GroupNorm(8, 256)
+
+ def forward(self, trg, src, trg_mask=None, src_mask=None):
+ # trg = [batch_size, compound len, atom_dim]
+ # src = [batch_size, protein len, hidden_dim] # encoder output
+ trg = self.ft(trg) # trg = [batch size, compound len, hid dim]
+
+ for layer in self.layers:
+ trg = layer(trg, src, trg_mask, src_mask) # trg = [batch size, compound len, hid dim]
+ """Use norm to determine which atom is significant. """
+ norm = torch.norm(trg, dim=2) # norm = [batch size,compound len]
+ norm = F.softmax(norm, dim=1) # norm = [batch size,compound len]
+ # trg = torch.squeeze(trg,dim=0)
+ # norm = torch.squeeze(norm,dim=0)
+ sum = torch.zeros((trg.shape[0], self.hidden_dim))
+ for i in range(norm.shape[0]):
+ for j in range(norm.shape[1]):
+ v = trg[i, j,]
+ v = v * norm[i, j]
+ sum[i,] += v # sum = [batch size,hidden_dim]
+ label = F.relu(self.fc_1(sum))
+ # label = self.fc_2(label)
+ return label
diff --git a/deepscreen/models/predictors/transformer_cpi_2.py b/deepscreen/models/predictors/transformer_cpi_2.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f4d400d4466e3de5c946018ae6c824f6f31f033
--- /dev/null
+++ b/deepscreen/models/predictors/transformer_cpi_2.py
@@ -0,0 +1,103 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class TransformerCPI2(nn.Module):
+ def __init__(self, encoder, decoder, atom_dim=34):
+ super().__init__()
+ self.encoder = encoder
+ self.decoder = decoder
+ self.fc_1 = nn.Linear(atom_dim, atom_dim)
+ self.fc_2 = nn.Linear(atom_dim, 768)
+
+ # def gcn(self, compound, adj):
+ # # input = [batch, num_node, atom_dim]
+ # # adj = [batch, num_node, num_node]
+ # support = self.fc_1(compound) # support = [batch, num_node, atom_dim]
+ # output = torch.bmm(adj, support) # output = [batch, num_node, atom_dim]
+ # return output
+
+ def forward(self, compound, protein):
+ # atom_feat = [batch_size, atom_num, atom_dim]
+ # adj_mat = [batch_size, atom_num, atom_num]
+ # enc_protein = [batch_size, protein_len, 768]
+ compound, adj = compound
+
+ compound, compound_lengths = compound
+ adj, adj_lengths = adj
+ protein, protein_lengths = protein
+
+ # Add a global/master node to the compound
+ batch_size, num_node, _ = compound.shape
+ # Add the global node
+ compound = F.pad(compound, (0, 0, 1, 0), value=0)
+ # Add an identity matrix to each adjacency matrix to represent self-connections
+ adj = adj + torch.eye(num_node, device=compound.device).unsqueeze(0).expand(batch_size, -1, -1)
+ # Add global edges
+ adj = F.pad(adj, (1, 0, 1, 0), value=1)
+
+ compound_mask = torch.arange(compound.size(1), device=compound.device) >= (compound_lengths + 1).unsqueeze(1)
+ protein_mask = torch.arange(protein.size(1), device=protein.device) >= protein_lengths.unsqueeze(1)
+
+ compound = self.gcn(compound.float(), adj) # compound = [batch_size, atom_num, atom_dim]
+ compound = F.relu(self.fc_2(compound)) # compound = [batch, compound_len, 768]
+ enc_src, src_mask = self.encoder(protein, protein_mask) # enc_src = [protein_len,batch , hid_dim]
+ out = self.decoder(compound, enc_src, compound_mask, src_mask) # out = [batch_size, 2]
+
+ return out
+
+ def gcn(self, compound, adj):
+ support = self.fc_1(compound) # support = [batch, num_node, atom_dim]
+ output = torch.bmm(adj, support) # output = [batch, num_node, atom_dim]
+ return output
+
+
+class Encoder(nn.Module):
+ """protein feature extraction"""
+ def __init__(self, pretrain, n_layers):
+ super().__init__()
+ self.pretrain = pretrain
+ self.hid_dim = 768
+ self.n_layers = n_layers
+ self.encoder_layer = nn.TransformerEncoderLayer(
+ d_model=self.hid_dim, nhead=8, dim_feedforward=self.hid_dim * 4, dropout=0.1
+ )
+ self.encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=self.n_layers)
+
+ def forward(self, protein, mask):
+ # protein = [batch_size, protein_len]
+ # mask = [batch_size, protein_len] 0 for true positions, 1 for mask positions
+ with torch.no_grad():
+ protein = self.pretrain(protein.long(), mask.long())[0]
+ protein = protein.permute(1, 0, 2).contiguous() # protein = [protein_len, batch_size, 768]
+ protein = self.encoder(protein, src_key_padding_mask=mask) # protein = [protein_len, batch_size, 768]
+ return protein, mask
+
+
+class Decoder(nn.Module):
+ """compound feature extraction"""
+ def __init__(self, n_layers, dropout):
+ super().__init__()
+ self.hid_dim = 768
+ self.n_layers = n_layers
+ self.decoder_layer = nn.TransformerDecoderLayer(
+ d_model=self.hid_dim, nhead=8, dim_feedforward=self.hid_dim * 4, dropout=0.1
+ )
+ self.decoder = nn.TransformerDecoder(self.decoder_layer, num_layers=self.n_layers)
+ self.fc_1 = nn.Linear(768, 256)
+ self.fc_2 = nn.Linear(256, 2)
+ self.dropout = nn.Dropout(dropout)
+
+ def forward(self, tgt, src, tgt_mask=None, src_mask=None):
+ # tgt = [batch_size, compound len, hid_dim]
+ # src = [protein_len, batch_size, hid_dim] # encoder output
+ tgt = tgt.permute(1, 0, 2).contiguous() # tgt = [compound_len, batch_size, hid_dim]
+ # tgt_mask = tgt_mask == 1
+ tgt = self.decoder(tgt, src, tgt_key_padding_mask=tgt_mask, memory_key_padding_mask=src_mask)
+ # tgt = [compound_len, batch_size, hid_dim]
+ tgt = tgt.permute(1, 0, 2).contiguous() # tgt = [batch_size, compound_len, hid_dim]
+ x = tgt[:, 0, :]
+ label = F.relu(self.fc_1(x))
+ label = self.fc_2(label)
+ return label
diff --git a/deepscreen/models/predictors/transformer_cpi_2.py.bak b/deepscreen/models/predictors/transformer_cpi_2.py.bak
new file mode 100644
index 0000000000000000000000000000000000000000..05026247f968b8034e8ae481418c8809e5045425
--- /dev/null
+++ b/deepscreen/models/predictors/transformer_cpi_2.py.bak
@@ -0,0 +1,168 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Encoder(nn.Module):
+ """protein feature extraction"""
+
+ def __init__(self, pretrain, n_layers):
+ super().__init__()
+ self.pretrain = pretrain
+ self.hid_dim = 768
+ self.n_layers = n_layers
+ self.encoder_layer = nn.TransformerEncoderLayer(
+ d_model=self.hid_dim, nhead=8, dim_feedforward=self.hid_dim * 4, dropout=0.1
+ )
+ self.encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=self.n_layers)
+
+ def forward(self, protein, mask):
+ # protein = [batch_size, protein_len]
+ # mask = [batch_size, protein_len] 0 for true positions, 1 for mask positions
+ with torch.no_grad():
+ protein = self.pretrain(protein.long(), mask.long())[0]
+ protein = protein.permute(1, 0, 2).contiguous() # protein = [protein_len, batch_size, 768]
+ protein = self.encoder(protein, src_key_padding_mask=mask) # protein = [protein_len, batch_size, 768]
+ return protein, mask
+
+
+class Decoder(nn.Module):
+ """compound feature extraction"""
+
+ def __init__(self, n_layers, dropout):
+ super().__init__()
+ self.hid_dim = 768
+ self.n_layers = n_layers
+ self.decoder_layer = nn.TransformerDecoderLayer(
+ d_model=self.hid_dim, nhead=8, dim_feedforward=self.hid_dim * 4, dropout=0.1
+ )
+ self.decoder = nn.TransformerDecoder(self.decoder_layer, num_layers=self.n_layers)
+ self.fc_1 = nn.Linear(768, 256)
+ self.fc_2 = nn.Linear(256, 2)
+ self.dropout = nn.Dropout(dropout)
+
+ def forward(self, tgt, src, tgt_mask=None, src_mask=None):
+ # tgt = [batch_size, compound len, hid_dim]
+ # src = [protein_len, batch_size, hid_dim] # encoder output
+ tgt = tgt.permute(1, 0, 2).contiguous() # tgt = [compound_len, batch_size, hid_dim]
+ # tgt_mask = tgt_mask == 1
+ tgt = self.decoder(
+ tgt, src, tgt_key_padding_mask=tgt_mask, memory_key_padding_mask=src_mask
+ ) # tgt = [compound_len, batch_size, hid_dim]
+ tgt = tgt.permute(1, 0, 2).contiguous() # tgt = [batch_size, compound_len, hid_dim]
+ x = tgt[:, 0, :]
+ label = F.relu(self.fc_1(x))
+ label = self.fc_2(label)
+ return label
+
+
+class TransformerCPI2(nn.Module):
+ def __init__(self, encoder, decoder, atom_dim=34):
+ super().__init__()
+ self.encoder = encoder
+ self.decoder = decoder
+ self.fc_1 = nn.Linear(atom_dim, atom_dim)
+ self.fc_2 = nn.Linear(atom_dim, 768)
+
+ # def gcn(self, compound, adj):
+ # # input = [batch, num_node, atom_dim]
+ # # adj = [batch, num_node, num_node]
+ # support = self.fc_1(compound) # support = [batch, num_node, atom_dim]
+ # output = torch.bmm(adj, support) # output = [batch, num_node, atom_dim]
+ # return output
+
+ def gcn(self, compound, adj):
+ batch_size, num_node, atom_dim = compound.shape
+ # Add a global node with padding at top
+ compound = F.pad(compound, (0, 0, 1, 0), value=0)
+ # Add an identity matrix to each adjacency matrix to represent self-connections
+ adj = adj + torch.eye(num_node, device=compound.device).unsqueeze(0).expand(batch_size, -1, -1)
+ # Add global edges with padding at left and top
+ adj = F.pad(adj, (1, 0, 1, 0), value=1)
+
+ support = self.fc_1(compound) # support = [batch, num_node, atom_dim]
+ output = torch.bmm(adj, support) # output = [batch, num_node, atom_dim]
+
+ # TODO support RNN packing in collate function
+
+ return output
+
+ def forward(self, enc_drug, enc_protein):
+ # atom_feat = [batch_size, atom_num, atom_dim]
+ # adj_mat = [batch_size, atom_num, atom_num]
+ # enc_protein = [batch_size, protein_len, 768]
+ compound, adj, atom_num = enc_drug
+ protein, protein_num = enc_protein
+ compound_max_len = compound.shape[1]
+ protein_max_len = protein.shape[1]
+ device = compound.device
+
+ compound_mask, protein_mask = self.make_masks(atom_num, protein_num, compound_max_len, protein_max_len, device)
+
+ compound = self.gcn(compound, adj) # compound = [batch_size, atom_num, atom_dim]
+ compound = F.relu(self.fc_2(compound)) # compound = [batch, compound_len, 768]
+ enc_src, src_mask = self.encoder(protein, protein_mask) # enc_src = [protein_len,batch , hid_dim]
+ out = self.decoder(compound, enc_src, compound_mask, src_mask) # out = [batch_size, 2]
+
+ return out
+
+ @staticmethod
+ def make_masks(atom_num, protein_num, compound_max_len, protein_max_len, device):
+ N = len(atom_num) # batch size
+ compound_mask = torch.ones((N, compound_max_len), device=device)
+ protein_mask = torch.ones((N, protein_max_len), device=device)
+ for i in range(N):
+ compound_mask[i, :atom_num[i]] = 0
+ protein_mask[i, :protein_num[i]] = 0
+ return compound_mask, protein_mask
+
+
+def pack(batch):
+ N = len(batch)
+ atoms, adjs, proteins = zip(*[(torch.Tensor(sample['X1'][0]),
+ torch.Tensor(sample['X1'][1]),
+ torch.Tensor(sample['X2']))
+ for sample in batch])
+
+ atoms_len = 0
+ atom_num = []
+ for atom in atoms:
+ atom_num.append(atom.shape[0] + 1)
+ if atom.shape[0] >= atoms_len:
+ atoms_len = atom.shape[0]
+ atoms_len += 1
+
+ proteins_len = 0
+ protein_num = []
+ for protein in proteins:
+ protein_num.append(protein.shape[0])
+ if protein.shape[0] >= proteins_len:
+ proteins_len = protein.shape[0]
+
+ atoms_padded = torch.zeros((N, atoms_len, 34))
+ for i, atom in enumerate(atoms):
+ a_len = atom.shape[0]
+ atoms_padded[i, 1:a_len + 1, :] = atom
+
+ adjs_padded = torch.zeros((N, atoms_len, atoms_len))
+ for i, adj in enumerate(adjs):
+ adjs_padded[i, 0, :] = 1
+ adjs_padded[i, :, 0] = 1
+ a_len = adj.shape[0]
+ adj = adj + torch.eye(a_len)
+ adjs_padded[i, 1:a_len + 1, 1:a_len + 1] = adj
+
+ proteins_padded = torch.zeros((N, proteins_len))
+ for i, protein in enumerate(proteins):
+ a_len = protein.shape[0]
+ proteins_padded[i, :a_len] = protein
+
+ return {
+ 'N': torch.Tensor([sample['N'] for sample in batch]),
+ 'X1': (atoms_padded, adjs_padded, atom_num),
+ 'ID1': [sample['ID1'] for sample in batch],
+ 'X2': (proteins_padded, protein_num),
+ 'ID2': [sample['ID2'] for sample in batch],
+ 'Y': torch.Tensor([sample['Y'] for sample in batch]),
+ 'IDX': torch.Tensor([sample['IDX'] for sample in batch]),
+ }
diff --git a/deepscreen/predict.py b/deepscreen/predict.py
new file mode 100644
index 0000000000000000000000000000000000000000..19947b3dd5fcd978a2345ddae821a9113ff9bb0e
--- /dev/null
+++ b/deepscreen/predict.py
@@ -0,0 +1,73 @@
+from typing import List, Tuple
+
+import hydra
+from omegaconf import DictConfig
+from lightning import LightningDataModule, LightningModule, Trainer, Callback
+
+from deepscreen.utils.hydra import checkpoint_rerun_config
+from deepscreen.utils import get_logger, job_wrapper, instantiate_callbacks
+
+log = get_logger(__name__)
+
+
+# def fix_dict_config(cfg: DictConfig):
+# """fix all vars in the cfg config
+# this is an in-place operation"""
+# keys = list(cfg.keys())
+# for k in keys:
+# if type(cfg[k]) is DictConfig:
+# fix_dict_config(cfg[k])
+# else:
+# setattr(cfg, k, getattr(cfg, k))
+
+
+@job_wrapper(extra_utils=True)
+def predict(cfg: DictConfig) -> Tuple[list, dict]:
+ """Predict given checkpoint on a data predict set.
+
+ This method is wrapped in optional @job_wrapper decorator which applies extra utilities
+ before and after the call.
+
+ Args:
+ cfg (DictConfig): Configuration composed by Hydra.
+
+ Returns:
+ Tuple[dict, dict]: Dict with metrics and dict with all instantiated objects.
+ """
+ assert cfg.ckpt_path, "Checkpoint path (`ckpt_path`) must be specified for predicting."
+ cfg = checkpoint_rerun_config(cfg)
+
+ log.info(f"Instantiating data <{cfg.data._target_}>")
+ datamodule: LightningDataModule = hydra.utils.instantiate(cfg.data)
+
+ log.info(f"Instantiating model <{cfg.model._target_}>")
+ model: LightningModule = hydra.utils.instantiate(cfg.model)
+
+ log.info("Instantiating callbacks.")
+ callbacks: List[Callback] = instantiate_callbacks(cfg.get("callbacks"))
+
+ log.info(f"Instantiating trainer <{cfg.trainer._target_}>")
+ trainer: Trainer = hydra.utils.instantiate(cfg.trainer, logger=False, callbacks=callbacks)
+
+ object_dict = {
+ "cfg": cfg,
+ "datamodule": datamodule,
+ "model": model,
+ "callbacks": callbacks,
+ "trainer": trainer,
+ }
+
+ log.info("Start predicting.")
+ predictions = trainer.predict(model=model, datamodule=datamodule, ckpt_path=cfg.ckpt_path, return_predictions=True)
+
+ return predictions, object_dict
+
+
+@hydra.main(version_base="1.3", config_path="../configs", config_name="predict.yaml")
+def main(cfg: DictConfig):
+ predictions, _ = predict(cfg)
+ return predictions
+
+
+if __name__ == "__main__":
+ main()
diff --git a/deepscreen/test.py b/deepscreen/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e0f5212cbd63cfc2eec8c494011006f0d7d84c5
--- /dev/null
+++ b/deepscreen/test.py
@@ -0,0 +1,130 @@
+from typing import List, Tuple
+
+import hydra
+from lightning import LightningDataModule, LightningModule, Trainer, Callback
+from lightning.pytorch.loggers import Logger
+from omegaconf import DictConfig
+
+from deepscreen.utils.hydra import checkpoint_rerun_config
+from deepscreen.utils import get_logger, job_wrapper, instantiate_callbacks, instantiate_loggers, log_hyperparameters
+
+
+log = get_logger(__name__)
+
+
+# def fix_dict_config(cfg: DictConfig):
+# """fix all vars in the cfg config
+# this is an in-place operation"""
+# keys = list(cfg.keys())
+# for k in keys:
+# if type(cfg[k]) is DictConfig:
+# fix_dict_config(cfg[k])
+# else:
+# setattr(cfg, k, getattr(cfg, k))
+
+
+@job_wrapper(extra_utils=True)
+def test(cfg: DictConfig) -> Tuple[dict, dict]:
+ """Evaluates given checkpoint on a datamodule testset.
+
+ This method is wrapped in optional @job_wrapper decorator, that controls the behavior during
+ failure. Useful for multiruns, saving info about the crash, etc.
+
+ Args:
+ cfg (DictConfig): Configuration composed by Hydra.
+
+ Returns:
+ Tuple[dict, dict]: Dict with metrics and dict with all instantiated objects.
+ """
+ # fix_dict_config(cfg)
+
+ log.info(f"Instantiating datamodule <{cfg.data._target_}>")
+ datamodule: LightningDataModule = hydra.utils.instantiate(cfg.data)
+
+ log.info(f"Instantiating model <{cfg.model._target_}>")
+ model: LightningModule = hydra.utils.instantiate(cfg.model)
+
+ log.info("Instantiating callbacks.")
+ callbacks: List[Callback] = instantiate_callbacks(cfg.get("callbacks"))
+
+ log.info("Instantiating loggers.")
+ logger: List[Logger] = instantiate_loggers(cfg.get("logger"))
+
+ log.info(f"Instantiating trainer <{cfg.trainer._target_}>")
+ trainer: Trainer = hydra.utils.instantiate(cfg.trainer, logger=logger, callbacks=callbacks)
+
+ object_dict = {
+ "cfg": cfg,
+ "datamodule": datamodule,
+ "model": model,
+ "callbacks": callbacks,
+ "logger": logger,
+ "trainer": trainer,
+ }
+
+ if logger:
+ log.info("Logging hyperparameters.")
+ log_hyperparameters(object_dict)
+
+ log.info("Start testing.")
+ trainer.test(model=model, datamodule=datamodule, ckpt_path=cfg.ckpt_path)
+
+ metric_dict = trainer.callback_metrics
+
+ return metric_dict, object_dict
+
+
+@hydra.main(version_base="1.3", config_path="../configs", config_name="test.yaml")
+def main(cfg: DictConfig):
+ assert cfg.ckpt_path, "Checkpoint path (`ckpt_path`) must be specified for testing."
+ cfg = checkpoint_rerun_config(cfg)
+ # from hydra.core.hydra_config import HydraConfig
+ #
+ # hydra_cfg = HydraConfig.get()
+ # if hydra_cfg.output_subdir is not None:
+ # ckpt_cfg_path = Path(cfg.ckpt_path).parents[1] / hydra_cfg.output_subdir / 'config.yaml'
+ # ckpt_hydra_cfg_path = Path(cfg.ckpt_path).parents[1] / hydra_cfg.output_subdir / 'hydra.yaml'
+ # hydra_output = Path(hydra_cfg.runtime.output_dir) / hydra_cfg.output_subdir
+ #
+ # if ckpt_cfg_path.is_file():
+ # log.info(f"Found config file for the checkpoint at {str(ckpt_cfg_path)}; "
+ # f"merging config overrides with checkpoint config...")
+ # ckpt_cfg = OmegaConf.load(ckpt_cfg_path)
+ #
+ # if ckpt_hydra_cfg_path.is_file():
+ # ckpt_hydra_cfg = OmegaConf.load(ckpt_hydra_cfg_path)
+ # override_dirname = sanitize_path(ckpt_hydra_cfg.job.override_dirname)
+ #
+ # # Merge checkpoint config with test config by overriding specified nodes.
+ # ckpt_cfg = OmegaConf.masked_copy(ckpt_cfg, ['model', 'data', 'task'])
+ # ckpt_cfg.data = OmegaConf.masked_copy(ckpt_cfg.data, [
+ # key for key in ckpt_cfg.data.keys() if key not in ['data_file', 'split', 'train_val_test_split']
+ # ])
+ # cfg = OmegaConf.merge(ckpt_cfg, cfg)
+ #
+ # _save_config(cfg, "config.yaml", hydra_output)
+
+ # evaluate the model
+ metric_dict, _ = test(cfg)
+
+ # safely retrieve metric value for hydra-based hyperparameter optimization
+ objective_metric = cfg.get("objective_metric")
+
+ if not objective_metric:
+ return None
+
+ if objective_metric not in metric_dict:
+ raise Exception(
+ f"Unable to find `objective_metric` ({objective_metric}) in `metric_dict`.\n"
+ "Make sure `objective_metric` name in `sweep` config is correct."
+ )
+
+ metric_value = metric_dict[objective_metric].item()
+ log.info(f"Retrieved objective_metric. <{objective_metric}={metric_value}>")
+
+ # return optimized metric
+ return metric_value
+
+
+if __name__ == "__main__":
+ main()
diff --git a/deepscreen/test.py.bak b/deepscreen/test.py.bak
new file mode 100644
index 0000000000000000000000000000000000000000..b4233e955c1269626c3b6245ac068651280148fd
--- /dev/null
+++ b/deepscreen/test.py.bak
@@ -0,0 +1,102 @@
+from typing import List, Tuple
+
+import hydra
+from lightning import LightningDataModule, LightningModule, Trainer, Callback
+from lightning.pytorch.loggers import Logger
+from omegaconf import DictConfig
+
+from deepscreen.utils import get_logger, job_wrapper, instantiate_callbacks, instantiate_loggers, log_hyperparameters
+
+
+log = get_logger(__name__)
+
+
+# def fix_dict_config(cfg: DictConfig):
+# """fix all vars in the cfg config
+# this is an in-place operation"""
+# keys = list(cfg.keys())
+# for k in keys:
+# if type(cfg[k]) is DictConfig:
+# fix_dict_config(cfg[k])
+# else:
+# setattr(cfg, k, getattr(cfg, k))
+
+
+@job_wrapper(extra_utils=True)
+def test(cfg: DictConfig) -> Tuple[dict, dict]:
+ """Evaluates given checkpoint on a datamodule testset.
+
+ This method is wrapped in optional @job_wrapper decorator, that controls the behavior during
+ failure. Useful for multiruns, saving info about the crash, etc.
+
+ Args:
+ cfg (DictConfig): Configuration composed by Hydra.
+
+ Returns:
+ Tuple[dict, dict]: Dict with metrics and dict with all instantiated objects.
+ """
+ # fix_dict_config(cfg)
+ assert cfg.ckpt_path
+
+ log.info(f"Instantiating datamodule <{cfg.data._target_}>")
+ datamodule: LightningDataModule = hydra.utils.instantiate(cfg.data)
+
+ log.info(f"Instantiating model <{cfg.model._target_}>")
+ model: LightningModule = hydra.utils.instantiate(cfg.model)
+
+ log.info("Instantiating callbacks.")
+ callbacks: List[Callback] = instantiate_callbacks(cfg.get("callbacks"))
+
+ log.info("Instantiating loggers.")
+ logger: List[Logger] = instantiate_loggers(cfg.get("logger"))
+
+ log.info(f"Instantiating trainer <{cfg.trainer._target_}>")
+ trainer: Trainer = hydra.utils.instantiate(cfg.trainer, logger=logger, callbacks=callbacks)
+
+ object_dict = {
+ "cfg": cfg,
+ "datamodule": datamodule,
+ "model": model,
+ "callbacks": callbacks,
+ "logger": logger,
+ "trainer": trainer,
+ }
+
+ if logger:
+ log.info("Logging hyperparameters.")
+ log_hyperparameters(object_dict)
+
+ log.info("Start testing.")
+ trainer.test(model=model, datamodule=datamodule, ckpt_path=cfg.ckpt_path)
+
+ metric_dict = trainer.callback_metrics
+
+ return metric_dict, object_dict
+
+
+@hydra.main(version_base="1.3", config_path="../configs", config_name="test.yaml")
+def main(cfg: DictConfig) -> None:
+ # evaluate the model
+ metric_dict, _ = test(cfg)
+
+ # safely retrieve metric value for hydra-based hyperparameter optimization
+ objective_metric = cfg.get("objective_metric")
+
+ if not objective_metric:
+ return None
+
+ if objective_metric not in metric_dict:
+ raise Exception(
+ f"Unable to find `objective_metric` ({objective_metric}) in `metric_dict`.\n"
+ "Make sure `objective_metric` name in `sweep` config is correct."
+ )
+
+ metric_value = metric_dict[objective_metric].item()
+ log.info(f"Retrieved objective_metric. <{objective_metric}={metric_value}>")
+
+ # return optimized metric
+ return metric_value
+
+
+if __name__ == "__main__":
+ main()
diff --git a/deepscreen/tests/__init__.py b/deepscreen/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/deepscreen/tests/__pycache__/conftest.cpython-39-pytest-7.1.2.pyc b/deepscreen/tests/__pycache__/conftest.cpython-39-pytest-7.1.2.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1d0e3b1af5f24c1012a0382eaa0e6a1326947223
Binary files /dev/null and b/deepscreen/tests/__pycache__/conftest.cpython-39-pytest-7.1.2.pyc differ
diff --git a/deepscreen/tests/__pycache__/test_train.cpython-39-pytest-7.1.2.pyc b/deepscreen/tests/__pycache__/test_train.cpython-39-pytest-7.1.2.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a1a8ec904bd133796451936677e441242d7509fd
Binary files /dev/null and b/deepscreen/tests/__pycache__/test_train.cpython-39-pytest-7.1.2.pyc differ
diff --git a/deepscreen/tests/conftest.py b/deepscreen/tests/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8a7304d336872af3a03b7fcd8a609f3bb37314a
--- /dev/null
+++ b/deepscreen/tests/conftest.py
@@ -0,0 +1,78 @@
+import pytest
+from hydra import compose, initialize
+from hydra.core.global_hydra import GlobalHydra
+from omegaconf import DictConfig, open_dict
+
+
+@pytest.fixture(scope="package")
+def cfg_train_global() -> DictConfig:
+ with initialize(version_base="1.2", config_path="../configs"):
+ cfg = compose(config_name="train.yaml", return_hydra_config=True, overrides=[])
+
+ # set defaults for all tests
+ with open_dict(cfg):
+ # cfg.paths.root_dir = str(config_path)
+ cfg.trainer.max_epochs = 1
+ cfg.trainer.limit_train_batches = 0.01
+ cfg.trainer.limit_val_batches = 0.1
+ cfg.trainer.limit_test_batches = 0.1
+ cfg.trainer.accelerator = "cpu"
+ cfg.trainer.devices = 1
+ cfg.datamodule.num_workers = 0
+ cfg.datamodule.pin_memory = False
+ cfg.extras.print_config = False
+ cfg.extras.enforce_tags = False
+ cfg.logger = None
+
+ return cfg
+
+
+@pytest.fixture(scope="package")
+def cfg_eval_global() -> DictConfig:
+ with initialize(version_base="1.2", config_path="../configs"):
+ cfg = compose(config_name="eval.yaml", return_hydra_config=True, overrides=["ckpt_path=."])
+
+ # set defaults for all tests
+ with open_dict(cfg):
+ # cfg.paths.root_dir = str(config_path)
+ cfg.trainer.max_epochs = 1
+ cfg.trainer.limit_test_batches = 0.1
+ cfg.trainer.accelerator = "cpu"
+ cfg.trainer.devices = 1
+ cfg.datamodule.num_workers = 0
+ cfg.datamodule.pin_memory = False
+ cfg.extras.print_config = False
+ cfg.extras.enforce_tags = False
+ cfg.logger = None
+
+ return cfg
+
+
+# this is called by each test which uses `cfg_train` arg
+# each test generates its own temporary logging path
+@pytest.fixture(scope="function")
+def cfg_train(cfg_train_global, tmp_path) -> DictConfig:
+ cfg = cfg_train_global.copy()
+
+ with open_dict(cfg):
+ cfg.paths.output_dir = str(tmp_path)
+ cfg.paths.log_dir = str(tmp_path)
+
+ yield cfg
+
+ GlobalHydra.instance().clear()
+
+
+# this is called by each test which uses `cfg_eval` arg
+# each test generates its own temporary logging path
+@pytest.fixture(scope="function")
+def cfg_eval(cfg_eval_global, tmp_path) -> DictConfig:
+ cfg = cfg_eval_global.copy()
+
+ with open_dict(cfg):
+ cfg.paths.output_dir = str(tmp_path)
+ cfg.paths.log_dir = str(tmp_path)
+
+ yield cfg
+
+ GlobalHydra.instance().clear()
diff --git a/deepscreen/tests/helpers/__init__.py b/deepscreen/tests/helpers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/deepscreen/tests/helpers/package_available.py b/deepscreen/tests/helpers/package_available.py
new file mode 100644
index 0000000000000000000000000000000000000000..eab3a62740b107650f97385bf8ca2d3c1dab11d2
--- /dev/null
+++ b/deepscreen/tests/helpers/package_available.py
@@ -0,0 +1,27 @@
+import platform
+
+import pkg_resources
+from lightning.pytorch.utilities.xla_device import XLADeviceUtils
+
+
+def _package_available(package_name: str) -> bool:
+ """Check if a package is available in your environment."""
+ try:
+ return pkg_resources.require(package_name) is not None
+ except pkg_resources.DistributionNotFound:
+ return False
+
+
+_TPU_AVAILABLE = XLADeviceUtils.tpu_device_exists()
+
+_IS_WINDOWS = platform.system() == "Windows"
+
+_SH_AVAILABLE = not _IS_WINDOWS and _package_available("sh")
+
+_DEEPSPEED_AVAILABLE = not _IS_WINDOWS and _package_available("deepspeed")
+_FAIRSCALE_AVAILABLE = not _IS_WINDOWS and _package_available("fairscale")
+
+_WANDB_AVAILABLE = _package_available("wandb")
+_NEPTUNE_AVAILABLE = _package_available("neptune")
+_COMET_AVAILABLE = _package_available("comet_ml")
+_MLFLOW_AVAILABLE = _package_available("mlflow")
diff --git a/deepscreen/tests/helpers/run_if.py b/deepscreen/tests/helpers/run_if.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9de9e848308c64f3b5357d842f3546f4a6bb98c
--- /dev/null
+++ b/deepscreen/tests/helpers/run_if.py
@@ -0,0 +1,139 @@
+"""Adapted from:
+
+https://github.com/PyTorchLightning/pytorch-lightning/blob/master/tests/helpers/runif.py
+"""
+
+import sys
+from typing import Optional
+
+import pytest
+import torch
+from packaging.version import Version
+from pkg_resources import get_distribution
+
+from tests.helpers.package_available import (
+ _COMET_AVAILABLE,
+ _DEEPSPEED_AVAILABLE,
+ _FAIRSCALE_AVAILABLE,
+ _IS_WINDOWS,
+ _MLFLOW_AVAILABLE,
+ _NEPTUNE_AVAILABLE,
+ _SH_AVAILABLE,
+ _TPU_AVAILABLE,
+ _WANDB_AVAILABLE,
+)
+
+
+class RunIf:
+ """RunIf wrapper for conditional skipping of tests.
+
+ Fully compatible with `@pytest.mark`.
+
+ Example:
+
+ @RunIf(min_torch="1.8")
+ @pytest.mark.parametrize("arg1", [1.0, 2.0])
+ def test_wrapper(arg1):
+ assert arg1 > 0
+ """
+
+ def __new__(
+ self,
+ min_gpus: int = 0,
+ min_torch: Optional[str] = None,
+ max_torch: Optional[str] = None,
+ min_python: Optional[str] = None,
+ skip_windows: bool = False,
+ sh: bool = False,
+ tpu: bool = False,
+ fairscale: bool = False,
+ deepspeed: bool = False,
+ wandb: bool = False,
+ neptune: bool = False,
+ comet: bool = False,
+ mlflow: bool = False,
+ **kwargs,
+ ):
+ """
+ Args:
+ min_gpus: min number of GPUs required to run test
+ min_torch: minimum pytorch version to run test
+ max_torch: maximum pytorch version to run test
+ min_python: minimum python version required to run test
+ skip_windows: skip test for Windows platform
+ tpu: if TPU is available
+ sh: if `sh` module is required to run the test
+ fairscale: if `fairscale` module is required to run the test
+ deepspeed: if `deepspeed` module is required to run the test
+ wandb: if `wandb` module is required to run the test
+ neptune: if `neptune` module is required to run the test
+ comet: if `comet` module is required to run the test
+ mlflow: if `mlflow` module is required to run the test
+ kwargs: native pytest.mark.skipif keyword arguments
+ """
+ conditions = []
+ reasons = []
+
+ if min_gpus:
+ conditions.append(torch.cuda.device_count() < min_gpus)
+ reasons.append(f"GPUs>={min_gpus}")
+
+ if min_torch:
+ torch_version = get_distribution("torch").version
+ conditions.append(Version(torch_version) < Version(min_torch))
+ reasons.append(f"torch>={min_torch}")
+
+ if max_torch:
+ torch_version = get_distribution("torch").version
+ conditions.append(Version(torch_version) >= Version(max_torch))
+ reasons.append(f"torch<{max_torch}")
+
+ if min_python:
+ py_version = (
+ f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}"
+ )
+ conditions.append(Version(py_version) < Version(min_python))
+ reasons.append(f"python>={min_python}")
+
+ if skip_windows:
+ conditions.append(_IS_WINDOWS)
+ reasons.append("does not run on Windows")
+
+ if tpu:
+ conditions.append(not _TPU_AVAILABLE)
+ reasons.append("TPU")
+
+ if sh:
+ conditions.append(not _SH_AVAILABLE)
+ reasons.append("sh")
+
+ if fairscale:
+ conditions.append(not _FAIRSCALE_AVAILABLE)
+ reasons.append("fairscale")
+
+ if deepspeed:
+ conditions.append(not _DEEPSPEED_AVAILABLE)
+ reasons.append("deepspeed")
+
+ if wandb:
+ conditions.append(not _WANDB_AVAILABLE)
+ reasons.append("wandb")
+
+ if neptune:
+ conditions.append(not _NEPTUNE_AVAILABLE)
+ reasons.append("neptune")
+
+ if comet:
+ conditions.append(not _COMET_AVAILABLE)
+ reasons.append("comet")
+
+ if mlflow:
+ conditions.append(not _MLFLOW_AVAILABLE)
+ reasons.append("mlflow")
+
+ reasons = [rs for cond, rs in zip(conditions, reasons) if cond]
+ return pytest.mark.skipif(
+ condition=any(conditions),
+ reason=f"Requires: [{' + '.join(reasons)}]",
+ **kwargs,
+ )
diff --git a/deepscreen/tests/helpers/run_sh_command.py b/deepscreen/tests/helpers/run_sh_command.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0afcbb73288b5e94f6bd6e8518314ed70087e4f
--- /dev/null
+++ b/deepscreen/tests/helpers/run_sh_command.py
@@ -0,0 +1,19 @@
+from typing import List
+
+import pytest
+
+from tests.helpers.package_available import _SH_AVAILABLE
+
+if _SH_AVAILABLE:
+ import sh
+
+
+def run_sh_command(command: List[str]):
+ """Default method for executing shell commands with pytest and sh package."""
+ msg = None
+ try:
+ sh.python(command)
+ except sh.ErrorReturnCode as e:
+ msg = e.stderr.decode()
+ if msg:
+ pytest.fail(msg=msg)
diff --git a/deepscreen/tests/test_configs.py b/deepscreen/tests/test_configs.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f6366250739af8254185090ea7f3f36ca97e59c
--- /dev/null
+++ b/deepscreen/tests/test_configs.py
@@ -0,0 +1,29 @@
+import hydra
+from hydra.core.hydra_config import HydraConfig
+from omegaconf import DictConfig
+
+# TODO WIP
+def test_train_config(cfg_train: DictConfig):
+ assert cfg_train
+ assert cfg_train.datamodule
+ assert cfg_train.model
+ assert cfg_train.trainer
+
+ HydraConfig().set_config(cfg_train)
+
+ hydra.utils.instantiate(cfg_train.datamodule)
+ hydra.utils.instantiate(cfg_train.model)
+ hydra.utils.instantiate(cfg_train.trainer)
+
+
+def test_eval_config(cfg_eval: DictConfig):
+ assert cfg_eval
+ assert cfg_eval.datamodule
+ assert cfg_eval.model
+ assert cfg_eval.trainer
+
+ HydraConfig().set_config(cfg_eval)
+
+ hydra.utils.instantiate(cfg_eval.datamodule)
+ hydra.utils.instantiate(cfg_eval.model)
+ hydra.utils.instantiate(cfg_eval.trainer)
diff --git a/deepscreen/tests/test_entity_datamodule.py b/deepscreen/tests/test_entity_datamodule.py
new file mode 100644
index 0000000000000000000000000000000000000000..6290f99c93180af15b7c3d3ea8854e8fbd70ebaa
--- /dev/null
+++ b/deepscreen/tests/test_entity_datamodule.py
@@ -0,0 +1,33 @@
+from pathlib import Path
+
+import pytest
+import torch
+
+from deepscreen.data.entity_datamodule import EntityDataModule
+
+
+# TODO WIP
+@pytest.mark.parametrize("batch_size", [32, 128])
+def test_dti_datamodule(batch_size):
+ data_dir = "data/"
+
+ dm = EntityDataModule(data_dir=data_dir, batch_size=batch_size)
+ dm.prepare_data()
+
+ assert not dm.data_train and not dm.data_val and not dm.data_test
+ assert Path(data_dir, "DTI").exists()
+ assert Path(data_dir, "DTI", "raw").exists()
+
+ dm.setup()
+ assert dm.data_train and dm.data_val and dm.data_test
+ assert dm.train_dataloader() and dm.val_dataloader() and dm.test_dataloader()
+
+ num_datapoints = len(dm.data_train) + len(dm.data_val) + len(dm.data_test)
+ assert num_datapoints == 70_000
+
+ batch = next(iter(dm.train_dataloader()))
+ x, y = batch
+ assert len(x) == batch_size
+ assert len(y) == batch_size
+ assert x.dtype == torch.float32
+ assert y.dtype == torch.int64
diff --git a/deepscreen/tests/test_eval.py b/deepscreen/tests/test_eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b2f12826497e7ba952ed36c3c62db114c49296c
--- /dev/null
+++ b/deepscreen/tests/test_eval.py
@@ -0,0 +1,32 @@
+import os
+
+import pytest
+from hydra.core.hydra_config import HydraConfig
+from omegaconf import open_dict
+
+from deepscreen.test import evaluate
+from deepscreen.train import train
+
+# TODO WIP
+@pytest.mark.slow
+def test_train_eval(tmp_path, cfg_train, cfg_eval):
+ """Train for 1 epoch with `train.py` and evaluate with `eval.py`"""
+ assert str(tmp_path) == cfg_train.paths.output_dir == cfg_eval.paths.output_dir
+
+ with open_dict(cfg_train):
+ cfg_train.trainer.max_epochs = 1
+ cfg_train.test = True
+
+ HydraConfig().set_config(cfg_train)
+ train_metric_dict, _ = train(cfg_train)
+
+ assert "last.ckpt" in os.listdir(tmp_path / "checkpoints")
+
+ with open_dict(cfg_eval):
+ cfg_eval.ckpt_path = str(tmp_path / "checkpoints" / "last.ckpt")
+
+ HydraConfig().set_config(cfg_eval)
+ test_metric_dict, _ = evaluate(cfg_eval)
+
+ assert test_metric_dict["test/acc"] > 0.0
+ assert abs(train_metric_dict["test/acc"].item() - test_metric_dict["test/acc"].item()) < 0.001
diff --git a/deepscreen/tests/test_train.py b/deepscreen/tests/test_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ae431fdc19041af22794abd93bebd95774a214f
--- /dev/null
+++ b/deepscreen/tests/test_train.py
@@ -0,0 +1,88 @@
+import os
+
+import pytest
+from hydra.core.hydra_config import HydraConfig
+from omegaconf import open_dict
+
+from deepscreen.train import train
+from tests.helpers.run_if import RunIf
+
+# TODO WIP
+def test_train_fast_dev_run(cfg_train):
+ """Run for 1 train, val and test step."""
+ HydraConfig().set_config(cfg_train)
+ with open_dict(cfg_train):
+ cfg_train.trainer.fast_dev_run = True
+ cfg_train.trainer.accelerator = "cpu"
+ train(cfg_train)
+
+
+@RunIf(min_gpus=1)
+def test_train_fast_dev_run_gpu(cfg_train):
+ """Run for 1 train, val and test step on GPU."""
+ HydraConfig().set_config(cfg_train)
+ with open_dict(cfg_train):
+ cfg_train.trainer.fast_dev_run = True
+ cfg_train.trainer.accelerator = "gpu"
+ train(cfg_train)
+
+
+@RunIf(min_gpus=1)
+@pytest.mark.slow
+def test_train_epoch_gpu_amp(cfg_train):
+ """Train 1 epoch on GPU with mixed-precision."""
+ HydraConfig().set_config(cfg_train)
+ with open_dict(cfg_train):
+ cfg_train.trainer.max_epochs = 1
+ cfg_train.trainer.accelerator = "cpu"
+ cfg_train.trainer.precision = 16
+ train(cfg_train)
+
+
+@pytest.mark.slow
+def test_train_epoch_double_val_loop(cfg_train):
+ """Train 1 epoch with validation loop twice per epoch."""
+ HydraConfig().set_config(cfg_train)
+ with open_dict(cfg_train):
+ cfg_train.trainer.max_epochs = 1
+ cfg_train.trainer.val_check_interval = 0.5
+ train(cfg_train)
+
+
+@pytest.mark.slow
+def test_train_ddp_sim(cfg_train):
+ """Simulate DDP (Distributed Data Parallel) on 2 CPU processes."""
+ HydraConfig().set_config(cfg_train)
+ with open_dict(cfg_train):
+ cfg_train.trainer.max_epochs = 2
+ cfg_train.trainer.accelerator = "cpu"
+ cfg_train.trainer.devices = 2
+ cfg_train.trainer.strategy = "ddp_spawn"
+ train(cfg_train)
+
+
+@pytest.mark.slow
+def test_train_resume(tmp_path, cfg_train):
+ """Run 1 epoch, finish, and resume for another epoch."""
+ with open_dict(cfg_train):
+ cfg_train.trainer.max_epochs = 1
+
+ HydraConfig().set_config(cfg_train)
+ metric_dict_1, _ = train(cfg_train)
+
+ files = os.listdir(tmp_path / "checkpoints")
+ assert "last.ckpt" in files
+ assert "epoch_000.ckpt" in files
+
+ with open_dict(cfg_train):
+ cfg_train.ckpt_path = str(tmp_path / "checkpoints" / "last.ckpt")
+ cfg_train.trainer.max_epochs = 2
+
+ metric_dict_2, _ = train(cfg_train)
+
+ files = os.listdir(tmp_path / "checkpoints")
+ assert "epoch_001.ckpt" in files
+ assert "epoch_002.ckpt" not in files
+
+ assert metric_dict_1["train/acc"] < metric_dict_2["train/acc"]
+ assert metric_dict_1["val/acc"] < metric_dict_2["val/acc"]
diff --git a/deepscreen/train.py b/deepscreen/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..4cdc6698e9a1e188c10ef319ad6c9deb4e286a35
--- /dev/null
+++ b/deepscreen/train.py
@@ -0,0 +1,141 @@
+from pathlib import Path
+from typing import List, Optional, Tuple
+
+import lightning
+import hydra
+import torch
+from lightning import Callback, LightningDataModule, LightningModule, Trainer
+from lightning.pytorch.loggers import Logger
+from omegaconf import DictConfig
+
+from deepscreen.utils.hydra import checkpoint_rerun_config
+from deepscreen.utils import get_logger, job_wrapper, instantiate_callbacks, instantiate_loggers, log_hyperparameters
+
+log = get_logger(__name__)
+
+
+# def fix_dict_config(cfg: DictConfig):
+# """fix all vars in the cfg config
+# this is an in-place operation"""
+# keys = list(cfg.keys())
+# for k in keys:
+# if type(cfg[k]) is DictConfig:
+# fix_dict_config(cfg[k])
+# else:
+# setattr(cfg, k, getattr(cfg, k))
+
+
+@job_wrapper(extra_utils=True)
+def train(cfg: DictConfig) -> Tuple[dict, dict]:
+ """Trains the model. Can additionally evaluate on a testset, using best weights obtained during
+ training.
+
+ This method is wrapped in optional @job_wrapper decorator, that controls the behavior during
+ failure. Useful for multiruns, saving info about the crash, etc.
+
+ Args:
+ cfg (DictConfig): Configuration composed by Hydra.
+
+ Returns:
+ Tuple[dict, dict]: Dict with metrics and dict with all instantiated objects.
+ """
+ # fix_dict_config(cfg)
+ # set seed for random number generators in pytorch, numpy and python.random
+ if cfg.get("seed"):
+ lightning.seed_everything(cfg.seed, workers=True)
+
+ if cfg.get("ckpt_path"):
+ cfg = checkpoint_rerun_config(cfg)
+
+ log.info(f"Instantiating datamodule <{cfg.data._target_}>.")
+ datamodule: LightningDataModule = hydra.utils.instantiate(cfg.data)
+
+ log.info(f"Instantiating model <{cfg.model._target_}>.")
+ model: LightningModule = hydra.utils.instantiate(cfg.model)
+
+ log.info("Instantiating callbacks.")
+ callbacks: List[Callback] = instantiate_callbacks(cfg.get("callbacks"))
+
+ log.info("Instantiating loggers.")
+ logger: List[Logger] = instantiate_loggers(cfg.get("logger"))
+
+ log.info(f"Instantiating trainer <{cfg.trainer._target_}>.")
+ trainer: Trainer = hydra.utils.instantiate(cfg.trainer, callbacks=callbacks, logger=logger)
+
+ object_dict = {
+ "cfg": cfg,
+ "datamodule": datamodule,
+ "model": model,
+ "callbacks": callbacks,
+ "logger": logger,
+ "trainer": trainer,
+ }
+
+ # Temporary fix to explicitly initialize UninitializedParameters in LazyModules
+ # for batch in datamodule.train_dataloader():
+ # device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+ # batch = batch.to(device)
+ # model(batch)
+ # break
+
+ if logger:
+ log.info("Logging hyperparameters...")
+ log_hyperparameters(object_dict)
+
+ if cfg.get("compile"):
+ log.info("Compiling model...")
+ model = torch.compile(model)
+
+ log.info("Start training...")
+ trainer.fit(model=model, datamodule=datamodule, ckpt_path=cfg.get("ckpt_path"))
+
+ if trainer.checkpoint_callback.best_model_path:
+ ckpt_path = Path(trainer.checkpoint_callback.best_model_path).resolve()
+ log.info(f"Best checkpoint path: {ckpt_path}")
+ else:
+ ckpt_path = None
+ log.warning("Best checkpoint not saved.")
+
+ if cfg.data.train_val_test_split[2] is not None:
+ log.info("Start testing...")
+ if ckpt_path is None:
+ log.warning("Best checkpoint not found. Using current weights for testing.")
+ trainer.test(model=model, datamodule=datamodule, ckpt_path=ckpt_path)
+
+ metric_dict = trainer.callback_metrics
+ metric_dict['ckpt_path'] = ckpt_path
+
+ return metric_dict, object_dict
+
+
+@hydra.main(version_base="1.3", config_path="../configs", config_name="train.yaml")
+def main(cfg: DictConfig):
+ metric_dict, _ = train(cfg)
+ cfg.ckpt_path = metric_dict.get('ckpt_path')
+
+ objective_metrics = cfg.get("objective_metrics")
+
+ if not objective_metrics:
+ return None
+
+ else:
+ invalid_metrics = [metric for metric in objective_metrics if metric not in metric_dict]
+
+ if invalid_metrics:
+ raise ValueError(
+ f"Unable to find {invalid_metrics} (specified in `objective_metrics`) in `metric_dict`.\n"
+ "Make sure your `model.metrics` and `sweep.objective_metrics` configs are correct."
+ )
+
+ # metric_value = metric_dict[objective_metric].item()
+ metric_values = tuple([metric_dict[metric].item() for metric in objective_metrics])
+
+ for objective_metric, metric_value in zip(objective_metrics, metric_values):
+ log.info(f"Retrieved objective: {objective_metric}={metric_value}")
+
+ # return optimized metrics
+ return metric_values
+
+
+if __name__ == "__main__":
+ main()
diff --git a/deepscreen/utils/__init__.py b/deepscreen/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f105b4f422de89a9ce402c650fae4aa262e4838
--- /dev/null
+++ b/deepscreen/utils/__init__.py
@@ -0,0 +1,8 @@
+from deepscreen.utils.logging import get_logger, log_hyperparameters
+from deepscreen.utils.instantiators import instantiate_callbacks, instantiate_loggers
+from deepscreen.utils.rich import enforce_tags, print_config_tree
+from deepscreen.utils.utils import extras, job_wrapper
+
+
+def passthrough(x):
+ return x
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diff --git a/deepscreen/utils/hydra.py b/deepscreen/utils/hydra.py
new file mode 100644
index 0000000000000000000000000000000000000000..13ca3fa331e745696f900a2567046b77c3c5991a
--- /dev/null
+++ b/deepscreen/utils/hydra.py
@@ -0,0 +1,180 @@
+from pathlib import Path
+import re
+from typing import Any, Tuple
+
+import pandas as pd
+from hydra.core.hydra_config import HydraConfig
+from hydra.core.utils import _save_config
+from hydra.experimental.callbacks import Callback
+from hydra.types import RunMode
+from omegaconf import DictConfig, OmegaConf
+
+from deepscreen.utils import get_logger
+
+log = get_logger(__name__)
+
+
+class CSVExperimentSummary(Callback):
+ """On multirun end, aggregate the results from each job's metrics.csv and save them in metrics_summary.csv."""
+
+ def __init__(self, filename: str = 'experiment_summary.csv', prefix: str | Tuple[str] = 'test/'):
+ self.filename = filename
+ self.prefix = prefix if isinstance(prefix, str) else tuple(prefix)
+ self.input_experiment_summary = None
+
+ def on_multirun_start(self, config: DictConfig, **kwargs: Any) -> None:
+ if config.hydra.get('overrides'):
+ if config.hydra.overrides.task:
+ for i, override in enumerate(config.hydra.overrides.task):
+ if override.startswith("ckpt_path"):
+ ckpt_path = override.split('=', 1)[1]
+ if ckpt_path.endswith(('.csv', '.txt', '.tsv', '.ssv', '.psv')):
+ config.hydra.overrides.task[i] = self.parse_ckpt_path_from_experiment_summary(ckpt_path)
+ break
+ elif config.hydra.sweeper.get('params'):
+ if config.hydra.sweeper.params.get('ckpt_path'):
+ ckpt_path = str(config.hydra.sweeper.params.ckpt_path).strip("'\"")
+ if ckpt_path.endswith(('.csv', '.txt', '.tsv', '.ssv', '.psv')):
+ config.hydra.sweeper.params.ckpt_path = self.parse_ckpt_path_from_experiment_summary(ckpt_path)
+
+ def on_job_end(self, config: DictConfig, job_return, **kwargs: Any) -> None:
+ # Skip callback if job is DDP subprocess
+ if "ddp" in job_return.hydra_cfg.hydra.job.name:
+ return
+
+ try:
+ if config.hydra.mode == RunMode.RUN:
+ summary_file_path = Path(config.hydra.run.dir) / self.filename
+ elif config.hydra.mode == RunMode.MULTIRUN:
+ summary_file_path = Path(config.hydra.sweep.dir) / self.filename
+ else:
+ raise RuntimeError('Invalid Hydra `RunMode`.')
+
+ if summary_file_path.is_file():
+ summary_df = pd.read_csv(summary_file_path)
+ else:
+ summary_df = pd.DataFrame()
+
+ # Add job and override info
+ override_dict = dict(override.split('=', 1) for override in job_return.overrides)
+ override_dict['job_status'] = job_return.status.name
+
+ # Add checkpoint info
+ if override_dict.get('ckpt_path'):
+ override_dict['ckpt_path'] = str(override_dict['ckpt_path']).strip("'\"")
+
+ if job_return.cfg.get('ckpt_path'):
+ ckpt_path = str(job_return.cfg.ckpt_path).strip("'\"")
+ if Path(ckpt_path).is_file():
+ if override_dict.get('ckpt_path') and ckpt_path != override_dict['ckpt_path']:
+ override_dict['previous_ckpt_path'] = override_dict['ckpt_path']
+ override_dict['ckpt_path'] = ckpt_path
+
+ override_dict['epoch'] = int(re.search(r'epoch_(\d+)', override_dict['ckpt_path']).group(1))
+
+ # Add metrics info
+ output_dir = Path(config.hydra.runtime.output_dir).resolve()
+ csv_metrics_path = output_dir / config.logger.csv.name / "metrics.csv"
+ if csv_metrics_path.is_file():
+ log.info(f"Summarizing metrics with prefix `{self.prefix}` from {csv_metrics_path}")
+ # Use only columns that start with the specified prefix
+ metrics_df = pd.read_csv(csv_metrics_path)
+ # Find rows where any 'test/' column is not null and reset its epoch to the best model epoch
+ test_columns = [col for col in metrics_df.columns if col.startswith('test/')]
+ mask = metrics_df[test_columns].notna().any(axis=1)
+ metrics_df.loc[mask, 'epoch'] = override_dict['epoch']
+ # Group and filter by best epoch
+ metrics_df = metrics_df.groupby('epoch').first()
+ metrics_df = metrics_df[metrics_df.index == override_dict['epoch']]
+ else:
+ log.info(f"No metrics.csv found in {output_dir}")
+ metrics_df = pd.DataFrame()
+
+ metrics_df = metrics_df.assign(**override_dict)
+ metrics_df.index = [0]
+
+ # Add extra info from the input batch experiment summary
+ if self.input_experiment_summary is not None:
+ orig_meta = self.input_experiment_summary[
+ self.input_experiment_summary['ckpt_path'] == metrics_df['ckpt_path'][0]
+ ].head(1)
+ orig_meta.index = [0]
+ metrics_df = metrics_df.combine_first(orig_meta)
+
+ summary_df = pd.concat([summary_df, metrics_df])
+ # Drop empty columns
+ summary_df.dropna(inplace=True, axis=1, how='all')
+ summary_df.to_csv(summary_file_path, index=False, mode='w')
+ log.info(f"Experiment summary saved to {summary_file_path}")
+ except Exception as e:
+ log.exception("Unable to save the experiment summary due to an error.", exc_info=e)
+
+ def parse_ckpt_path_from_experiment_summary(self, ckpt_path):
+ try:
+ self.input_experiment_summary = pd.read_csv(
+ ckpt_path, usecols=lambda col: not col.startswith(self.prefix)
+ )
+ self.input_experiment_summary['ckpt_path'] = self.input_experiment_summary['ckpt_path'].apply(
+ lambda x: x.strip("'\"")
+ )
+ ckpt_list = list(set(self.input_experiment_summary['ckpt_path']))
+ parsed_ckpt_path = ','.join([f"'{ckpt}'" for ckpt in ckpt_list])
+ return parsed_ckpt_path
+
+ except Exception as e:
+ log.exception(
+ f'Error in parsing checkpoint paths from experiment_summary file ({ckpt_path}).',
+ exc_info=e
+ )
+
+
+def checkpoint_rerun_config(config: DictConfig):
+ hydra_cfg = HydraConfig.get()
+
+ if hydra_cfg.output_subdir is not None:
+ ckpt_cfg_path = Path(config.ckpt_path).parents[1] / hydra_cfg.output_subdir / 'config.yaml'
+ hydra_output = Path(hydra_cfg.runtime.output_dir) / hydra_cfg.output_subdir
+
+ if ckpt_cfg_path.is_file():
+ log.info(f"Found config file for the checkpoint at {str(ckpt_cfg_path)}; "
+ f"merging config overrides with checkpoint config...")
+ ckpt_cfg = OmegaConf.load(ckpt_cfg_path)
+
+ # Merge checkpoint config with test config by overriding specified nodes.
+ # ckpt_cfg = OmegaConf.masked_copy(ckpt_cfg, ['model', 'data', 'trainer', 'task'])
+ # ckpt_cfg.data = OmegaConf.masked_copy(ckpt_cfg.data, [
+ # key for key in ckpt_cfg.data.keys() if key not in ['data_file', 'split', 'train_val_test_split']
+ # ])
+ #
+ # config = OmegaConf.merge(ckpt_cfg, config)
+
+ # config = OmegaConf.masked_copy(config,
+ # [key for key in config if key not in
+ # ['task']])
+ # config.data = OmegaConf.masked_copy(config.data,
+ # [key for key in config.data if key not in
+ # ['drug_featurizer', 'protein_featurizer', 'collator']])
+ # config.model = OmegaConf.masked_copy(config.model,
+ # [key for key in config.model if key not in
+ # ['predictor']])
+ #
+ # config = OmegaConf.merge(ckpt_cfg, config)
+
+ ckpt_cfg = OmegaConf.masked_copy(ckpt_cfg, ['model', 'data', 'task', 'seed'])
+ ckpt_cfg.data = OmegaConf.masked_copy(ckpt_cfg.data, [
+ key for key in ckpt_cfg.data.keys() if key not in ['data_file', 'split', 'train_val_test_split']
+ ])
+ ckpt_override_keys = ['task',
+ 'data.drug_featurizer', 'data.protein_featurizer', 'data.collator',
+ 'model.predictor']
+
+ for key in ckpt_override_keys:
+ OmegaConf.update(config, key, OmegaConf.select(ckpt_cfg, key), force_add=True)
+
+ config = OmegaConf.merge(ckpt_cfg, config)
+
+ # OmegaConf.set_readonly(hydra_cfg, False)
+ # hydra_cfg.job.override_dirname += f"ckpt={str(Path(*Path(config.ckpt_path).parts[-4:]))}"
+ _save_config(config, "config.yaml", hydra_output)
+
+ return config
diff --git a/deepscreen/utils/instantiators.py b/deepscreen/utils/instantiators.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8afaade147cd034730ae24c2f84a1348a6a73ac
--- /dev/null
+++ b/deepscreen/utils/instantiators.py
@@ -0,0 +1,50 @@
+from typing import List
+
+import hydra
+from lightning import Callback
+from lightning.pytorch.loggers import Logger
+from omegaconf import DictConfig
+
+from deepscreen.utils import get_logger
+
+log = get_logger(__name__)
+
+
+def instantiate_callbacks(callbacks_cfg: DictConfig) -> List[Callback]:
+ """Instantiates callbacks from config."""
+
+ callbacks: List[Callback] = []
+
+ if not callbacks_cfg:
+ log.warning("No callback configs found! Skipping..")
+ return callbacks
+
+ if not isinstance(callbacks_cfg, DictConfig):
+ raise TypeError("Callbacks config must be a DictConfig!")
+
+ for _, cb_conf in callbacks_cfg.items():
+ if isinstance(cb_conf, DictConfig) and "_target_" in cb_conf:
+ log.info(f"Instantiating callback <{cb_conf._target_}>")
+ callbacks.append(hydra.utils.instantiate(cb_conf))
+
+ return callbacks
+
+
+def instantiate_loggers(logger_cfg: DictConfig) -> List[Logger]:
+ """Instantiates loggers from config."""
+
+ logger: List[Logger] = []
+
+ if not logger_cfg:
+ log.warning("No logger configs found! Skipping.")
+ return logger
+
+ if not isinstance(logger_cfg, DictConfig):
+ raise TypeError("Logger config must be a DictConfig!")
+
+ for _, lg_conf in logger_cfg.items():
+ if isinstance(lg_conf, DictConfig) and "_target_" in lg_conf:
+ log.info(f"Instantiating logger <{lg_conf._target_}>")
+ logger.append(hydra.utils.instantiate(lg_conf))
+
+ return logger
diff --git a/deepscreen/utils/lightning.py b/deepscreen/utils/lightning.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfa38591ee26d3c822f31c217b9afbeac5214b0e
--- /dev/null
+++ b/deepscreen/utils/lightning.py
@@ -0,0 +1,49 @@
+from pathlib import Path
+from typing import Literal
+
+from lightning.pytorch.callbacks import BasePredictionWriter
+import pandas as pd
+import torch
+
+from deepscreen.utils import get_logger
+
+log = get_logger(__name__)
+
+
+class CSVPredictionWriter(BasePredictionWriter):
+ def __init__(self, output_dir, write_interval: Literal["batch", "epoch"] = "batch"):
+ super().__init__(write_interval)
+ self.output_file = Path(output_dir, "predictions.csv")
+
+ def setup(self, trainer, pl_module, stage: str):
+ log.info(f"Saving predictions every {self.interval.value} for job `{stage}`.")
+
+ def write_on_batch_end(self, trainer, pl_module, outputs, batch_indices, batch, batch_idx, dataloader_idx):
+ output_df = self.outputs_to_dataframe(outputs)
+ output_df.to_csv(self.output_file,
+ mode='a',
+ index_label='N',
+ header=not self.output_file.is_file())
+
+ def write_on_epoch_end(self, trainer, pl_module, predictions, batch_indices):
+ output_df = pd.concat([self.outputs_to_dataframe(outputs) for outputs in predictions])
+ output_df.to_csv(self.output_file,
+ mode='w',
+ index_label='N',
+ header=True)
+
+ def on_test_batch_end(self, trainer, pl_module, outputs, batch, batch_idx: int, dataloader_idx: int = 0):
+ self.write_on_batch_end(trainer, pl_module, outputs, None, batch, batch_idx, dataloader_idx)
+
+ def teardown(self, trainer, pl_module, stage: str):
+ log.info(f'Predictions saved to {self.output_file}')
+
+ @staticmethod
+ def outputs_to_dataframe(prediction):
+ for key, value in prediction.items():
+ if isinstance(value, torch.Tensor):
+ prediction[key] = value.tolist()
+ else:
+ prediction[key] = list(value)
+ prediction_df = pd.DataFrame(prediction)
+ return prediction_df
diff --git a/deepscreen/utils/logging.py b/deepscreen/utils/logging.py
new file mode 100644
index 0000000000000000000000000000000000000000..e71bf747a8a41331eb71ef6b72f06a7d147d0e66
--- /dev/null
+++ b/deepscreen/utils/logging.py
@@ -0,0 +1,62 @@
+import logging
+
+from lightning.pytorch.utilities import rank_zero_only
+from lightning.pytorch.utilities.model_summary import ModelSummary
+
+
+def get_logger(name=__name__) -> logging.Logger:
+ """Initializes multi-GPU-friendly python command line logger."""
+ logger = logging.getLogger(name)
+
+ # this ensures all logging levels get marked with the rank zero decorator
+ # otherwise logs would get multiplied for each GPU process in multi-GPU setup
+ logging_levels = ("debug", "info", "warning", "error", "exception", "fatal", "critical")
+ for level in logging_levels:
+ setattr(logger, level, rank_zero_only(getattr(logger, level)))
+
+ return logger
+
+
+log = get_logger(__name__)
+
+
+@rank_zero_only
+def log_hyperparameters(object_dict: dict) -> None:
+ """Controls which config parts are saved by lightning loggers.
+
+ Additionally, saves:
+ - Number of model parameters
+ """
+
+ hparams = {}
+
+ cfg = object_dict["cfg"]
+ model = object_dict["model"]
+ trainer = object_dict["trainer"]
+
+ if not trainer.logger:
+ log.warning("Logger not found! Skipping hyperparameter logging.")
+ return
+
+ hparams["model"] = cfg["model"]
+
+ # save number of model parameters
+ model_summary = ModelSummary(model)
+ hparams["model/params/total"] = model_summary.total_parameters
+ hparams["model/params/trainable"] = model_summary.trainable_parameters
+ hparams["model/params/non_trainable"] = model_summary.total_parameters - model_summary.trainable_parameters
+
+ hparams["data"] = cfg["data"]
+ hparams["trainer"] = cfg["trainer"]
+
+ hparams["callbacks"] = cfg.get("callbacks")
+ hparams["extras"] = cfg.get("extras")
+
+ hparams["job_name"] = cfg.get("job_name")
+ hparams["tags"] = cfg.get("tags")
+ hparams["ckpt_path"] = cfg.get("ckpt_path")
+ hparams["seed"] = cfg.get("seed")
+
+ # send hparams to all loggers
+ for logger in trainer.loggers:
+ logger.log_hyperparams(hparams)
diff --git a/deepscreen/utils/rich.py b/deepscreen/utils/rich.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d57ee33a917ebd95133a21665234f19fb4d854b
--- /dev/null
+++ b/deepscreen/utils/rich.py
@@ -0,0 +1,105 @@
+from pathlib import Path
+from typing import Sequence
+
+import rich
+import rich.syntax
+import rich.tree
+from hydra.core.hydra_config import HydraConfig
+from omegaconf import DictConfig, OmegaConf, open_dict
+from lightning.pytorch.utilities import rank_zero_only
+from rich.prompt import Prompt
+
+from deepscreen.utils import get_logger
+
+log = get_logger(__name__)
+
+
+@rank_zero_only
+def print_config_tree(
+ cfg: DictConfig,
+ print_order: Sequence[str] = (
+ "data",
+ "model",
+ "callbacks",
+ "logger",
+ "trainer",
+ "paths",
+ "extras",
+ ),
+ resolve: bool = False,
+ save_to_file: bool = False,
+) -> None:
+ """Prints content of DictConfig using Rich library and its tree structure.
+
+ Args:
+ cfg (DictConfig): Configuration composed by Hydra.
+ print_order (Sequence[str], optional): Determines in what order config components are printed.
+ resolve (bool, optional): Whether to resolve reference fields of DictConfig.
+ save_to_file (bool, optional): Whether to export config to the hydra output folder.
+ """
+
+ style = "dim"
+ tree = rich.tree.Tree("CONFIG", style=style, guide_style=style)
+
+ queue = []
+
+ # add fields from `print_order` to queue
+ for field in print_order:
+ queue.append(field) if field in cfg else log.warning(
+ f"Field '{field}' not found in config. Skipping '{field}' config printing."
+ )
+
+ # add all the other fields to queue (not specified in `print_order`)
+ for field in cfg:
+ if field not in queue:
+ queue.append(field)
+
+ # generate config tree from queue
+ for field in queue:
+ branch = tree.add(field, style=style, guide_style=style)
+
+ config_group = cfg[field]
+ if isinstance(config_group, DictConfig):
+ branch_content = OmegaConf.to_yaml(config_group, resolve=resolve)
+ else:
+ branch_content = str(config_group)
+
+ branch.add(rich.syntax.Syntax(branch_content, "yaml"))
+
+ # print config tree
+ rich.print(tree)
+
+ # save config tree to file
+ if save_to_file:
+ with open(Path(cfg.paths.output_dir, "config_tree.log"), "w") as file:
+ rich.print(tree, file=file)
+
+
+@rank_zero_only
+def enforce_tags(cfg: DictConfig, save_to_file: bool = False) -> None:
+ """Prompts user to input tags from command line if no tags are provided in config."""
+
+ if not cfg.get("tags"):
+ if "id" in HydraConfig().cfg.hydra.job:
+ raise ValueError("Specify tags before launching a multirun!")
+
+ log.warning("No tags provided in config. Prompting user to input tags.")
+ tags = Prompt.ask("Enter a list of comma separated tags", default="dev")
+ tags = [t.strip() for t in tags.split(",") if t != ""]
+
+ with open_dict(cfg):
+ cfg.tags = tags
+
+ log.info(f"Tags: {cfg.tags}")
+
+ if save_to_file:
+ with open(Path(cfg.paths.output_dir, "tags.log"), "w") as file:
+ rich.print(cfg.tags, file=file)
+
+
+# if __name__ == "__main__":
+# from hydra import compose, initialize
+#
+# with initialize(version_base="1.2", config_path="../../configs"):
+# cfg = compose(config_name="train.yaml", return_hydra_config=False, overrides=[])
+# print_config_tree(cfg, resolve=False, save_to_file=False)
diff --git a/deepscreen/utils/utils.py b/deepscreen/utils/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6855160479ddf99316ee7fd8e51d2bb4e6ffb860
--- /dev/null
+++ b/deepscreen/utils/utils.py
@@ -0,0 +1,206 @@
+# import time
+# from pathlib import Path
+# from typing import Any, Dict, List
+#
+# import hydra
+# from pytorch_lightning import Callback
+# from pytorch_lightning.loggers import Logger
+# from pytorch_lightning.utilities import rank_zero_only
+
+import warnings
+from importlib.util import find_spec
+from typing import Callable
+
+from omegaconf import DictConfig
+
+from deepscreen.utils import get_logger, enforce_tags, print_config_tree
+
+log = get_logger(__name__)
+
+
+def extras(cfg: DictConfig) -> None:
+ """Applies optional utilities before a job is started.
+
+ Utilities:
+ - Ignoring python warnings
+ - Setting tags from command line
+ - Rich config printing
+ """
+
+ # return if no `extras` config
+ if not cfg.get("extras"):
+ log.warning("Extras config not found! ")
+ return
+
+ # disable python warnings
+ if cfg.extras.get("ignore_warnings"):
+ log.info("Disabling python warnings! ")
+ warnings.filterwarnings("ignore")
+
+ # prompt user to input tags from command line if none are provided in the config
+ if cfg.extras.get("enforce_tags"):
+ log.info("Enforcing tags! ")
+ enforce_tags(cfg, save_to_file=True)
+
+ # pretty print config tree using Rich library
+ if cfg.extras.get("print_config"):
+ log.info("Printing config tree with Rich! ")
+ print_config_tree(cfg, resolve=True, save_to_file=True)
+
+
+def job_wrapper(extra_utils: bool) -> Callable:
+ """Optional decorator that controls the failure behavior and extra utilities when executing a job function.
+
+ This wrapper can be used to:
+ - make sure loggers are closed even if the job function raises an exception (prevents multirun failure)
+ - save the exception to a `.log` file
+ - mark the run as failed with a dedicated file in the `logs/` folder (so we can find and rerun it later)
+ - etc. (adjust depending on your needs)
+
+ Example:
+ ```
+ @utils.job_wrapper(extra_utils)
+ def train(cfg: DictConfig) -> Tuple[dict, dict]:
+
+ .
+
+ return metric_dict, object_dict
+ ```
+ """
+ def decorator(job_func):
+ def wrapped_func(cfg: DictConfig):
+ # execute the job
+ try:
+ # apply extra utilities
+ if extra_utils:
+ extras(cfg)
+ metric_dict, object_dict = job_func(cfg=cfg)
+
+ # things to do if exception occurs
+ except Exception as ex:
+ # save exception to `.log` file
+ log.exception("")
+
+ # some hyperparameter combinations might be invalid or cause out-of-memory errors
+ # so when using hparam search plugins like Optuna, you might want to disable
+ # raising the below exception to avoid multirun failure
+ raise ex
+
+ # things to always do after either success or exception
+ finally:
+ # display output dir path in terminal
+ log.info(f"Output dir: {cfg.paths.output_dir}")
+
+ # always close wandb run (even if exception occurs so multirun won't fail)
+ if find_spec("wandb"): # check if wandb is installed
+ import wandb
+
+ if wandb.run:
+ log.info("Closing wandb!")
+ wandb.finish()
+
+ return metric_dict, object_dict
+ return wrapped_func
+ return decorator
+
+# @rank_zero_only
+# def save_file(path, content) -> None:
+# """Save file in rank zero mode (only on one process in multi-GPU setup)."""
+# with open(path, "w+") as file:
+# file.write(content)
+#
+#
+# def instantiate_callbacks(callbacks_cfg: DictConfig) -> List[Callback]:
+# """Instantiates callbacks from config."""
+# callbacks: List[Callback] = []
+#
+# if not callbacks_cfg:
+# log.warning("Callbacks config is empty.")
+# return callbacks
+#
+# if not isinstance(callbacks_cfg, DictConfig):
+# raise TypeError("Callbacks config must be a DictConfig!")
+#
+# for _, cb_conf in callbacks_cfg.items():
+# if isinstance(cb_conf, DictConfig) and "_target_" in cb_conf:
+# log.info(f"Instantiating callback <{cb_conf._target_}>")
+# callbacks.append(hydra.utils.instantiate(cb_conf))
+#
+# return callbacks
+#
+#
+# def instantiate_loggers(logger_cfg: DictConfig) -> List[Logger]:
+# """Instantiates loggers from config."""
+# logger: List[Logger] = []
+#
+# if not logger_cfg:
+# log.warning("Logger config is empty.")
+# return logger
+#
+# if not isinstance(logger_cfg, DictConfig):
+# raise TypeError("Logger config must be a DictConfig!")
+#
+# for _, lg_conf in logger_cfg.items():
+# if isinstance(lg_conf, DictConfig) and "_target_" in lg_conf:
+# log.info(f"Instantiating logger <{lg_conf._target_}>")
+# logger.append(hydra.utils.instantiate(lg_conf))
+#
+# return logger
+#
+#
+# @rank_zero_only
+# def log_hyperparameters(object_dict: Dict[str, Any]) -> None:
+# """Controls which config parts are saved by lightning loggers.
+#
+# Additionally saves:
+# - Number of model parameters
+# """
+#
+# hparams = {}
+#
+# cfg = object_dict["cfg"]
+# model = object_dict["model"]
+# trainer = object_dict["trainer"]
+#
+# if not trainer.logger:
+# log.warning("Logger not found! Skipping hyperparameter logging.")
+# return
+#
+# hparams["model"] = cfg["model"]
+#
+# # TODO Accommodation for LazyModule
+# # save number of model parameters
+# hparams["model/params/total"] = sum(p.numel() for p in model.parameters())
+# hparams["model/params/trainable"] = sum(
+# p.numel() for p in model.parameters() if p.requires_grad
+# )
+# hparams["model/params/non_trainable"] = sum(
+# p.numel() for p in model.parameters() if not p.requires_grad
+# )
+#
+# hparams["data"] = cfg["data"]
+# hparams["trainer"] = cfg["trainer"]
+#
+# hparams["callbacks"] = cfg.get("callbacks")
+# hparams["extras"] = cfg.get("extras")
+#
+# hparams["job_name"] = cfg.get("job_name")
+# hparams["tags"] = cfg.get("tags")
+# hparams["ckpt_path"] = cfg.get("ckpt_path")
+# hparams["seed"] = cfg.get("seed")
+#
+# # send hparams to all loggers
+# trainer.logger.log_hyperparams(hparams)
+
+
+# def close_loggers() -> None:
+# """Makes sure all loggers closed properly (prevents logging failure during multirun)."""
+#
+# log.info("Closing loggers.")
+#
+# if find_spec("wandb"): # if wandb is installed
+# import wandb
+#
+# if wandb.run:
+# log.info("Closing wandb!")
+# wandb.finish()
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7e51735d750d525e8a45e393147af0517a51bcc2
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,33 @@
+# deep learning
+# Install with conda
+# torch>=2.0
+# torch_geometric
+lightning>=2.0
+torchmetrics
+transformers
+scikit-learn
+dgl
+dgllife
+
+# configuration and utilities
+hydra-core
+hydra-colorlog
+hydra-optuna-sweeper
+hydra-submitit-launcher
+pandas
+pre-commit
+rich
+pytest
+tensorboard
+
+# model-specific requirements
+subword-nmt # MONN
+tape_proteins # TransformerCPI2
+fair-esm # CoaDTI-pro
+# apex # recommended by TAPE-BERT
+
+# feature extraction
+rdkit
+networkx>=3.0
+mhfp
+tmap
\ No newline at end of file
diff --git a/resources/models/esm/esm1_t6_43M_UR50S-contact-regression.pt b/resources/models/esm/esm1_t6_43M_UR50S-contact-regression.pt
new file mode 100644
index 0000000000000000000000000000000000000000..922d9cc84a08b1c199b0bec63ae79258a1a4eb17
--- /dev/null
+++ b/resources/models/esm/esm1_t6_43M_UR50S-contact-regression.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:402a0c0c68407d38c9b4758ee030364622aab6150837c4c0b1fa94d6ffb92443
+size 819
diff --git a/resources/models/tape/bert-base/config.json b/resources/models/tape/bert-base/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..5224c89fcbd766960d19ae4090f9a8a08556b695
--- /dev/null
+++ b/resources/models/tape/bert-base/config.json
@@ -0,0 +1,23 @@
+{
+ "attention_probs_dropout_prob": 0.1,
+ "base_model": "transformer",
+ "finetuning_task": null,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 768,
+ "initializer_range": 0.02,
+ "input_size": 768,
+ "intermediate_size": 3072,
+ "layer_norm_eps": 1e-12,
+ "max_position_embeddings": 8192,
+ "num_attention_heads": 12,
+ "num_hidden_layers": 12,
+ "num_labels": 2,
+ "output_attentions": false,
+ "output_hidden_states": false,
+ "output_size": 768,
+ "pruned_heads": {},
+ "torchscript": false,
+ "type_vocab_size": 1,
+ "vocab_size": 30
+}
\ No newline at end of file
diff --git a/resources/vocabs/ESPF/drug_codes_chembl.txt b/resources/vocabs/ESPF/drug_codes_chembl.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b72f2bab435aad12462a7248638e3e3da456597e
--- /dev/null
+++ b/resources/vocabs/ESPF/drug_codes_chembl.txt
@@ -0,0 +1,25189 @@
+#version: 0.2
+c c
+C C
+O )
+( =
+(= O)
+cc c
+( C
+C (=O)
+[ C
+[C @
+H ]
+c 1
+) c
+(C )
+c 2
+cc ccc
+[C@ @
+[C@ H]
+c 3
+[C@@ H]
+ccc (
+( CC
+N C(=O)
+cc (
+C(=O) N
+c (
+= C
+) C
+CC CC
+) cc
+O C
+c 4
+C N
+C O
+F )
+CC N
+c1 ccc(
+( O)
+( F)
+)c 3
+)c 2
+(=O) N
+c n
+) N
+) C(=O)N
+CC C
+(C l
+\ C
+) C(=O)
+(C) C
+cc 1
+S (=O)
+)c 4
+) n
+[ n
+ccccc 3
+cc 2
+O C(=O)
+ccccc 2
+( N
+[n H]
+O CC
+c 5
+n c(
+cc cc(
+S(=O) (=O)
+CO c1
+ccccc 1
+c1ccc( cc1
+= O)
+C 1
+C c1
++ ]
+cc cc
+C 2
+C l
+CCN (CC
+NC(=O) [C@H]
+(F) (F)
+c3 ccccc3
+c2 ccc(
+ccccc 4
+)c (
+C (=
+ccc 2
+c2 ccccc2
+n 2
+\C =C
+cc 3
+n 1
+[C@@H] (C
+CC (C)
+ccc (Cl
+c3 ccc(
+[ N
+[C@ ]
+[C@@H] 1
+[ O
+[C@H] (
+[O -
+[C@@ ]
+[C@@H] (
+[C@H] 1
+F )cc
+C 3
+[C@@H] (CC
+n n
+[N +]
+ccc 3
+CC 1
+(C N
+)c 5
+CC (=O)N
+c4 ccccc4
+CC CN
+)cc 1
+B r
+ccc(Cl )cc
+NC(=O) C
+c1 ccccc1
+= N
+[C@H] (O)
+[C@H] (C
+= O
+[C@@H] (O)
+\C=C \
+CC (=O)
+2 )c3
+[O- ]
+c (C
+2 C
+[N+] (=O)
+) c1
+) CC
+cc n
+N =C
+S(=O) (=O)N
+3 )c4
+\C (=
+)C (F)(F)
+2 CC
+3 CC
+NC(=O)[C@H] (CC
+NC(=O) N
+)c 1
+n c2
+cc 4
+c( N
+CN 1
+O =C
+)C 2
+C(= N
+ccc 1
+C )
+c (C)
+c 6
+2 )
+# N
+4 CC
+ccccc 5
+c2 n
+c4 ccc(
+ccc (C
+CCCC CCCC
+O c1
+[C@H] 2
+[C@@H] (C)
+ccc 4
+3 C
+c [nH]
+CO C(=O)
+) NC(=O)
+c2ccc( cc2
+[C@@H] 2
+(C (=O)
+ccc n
+1 )c2
+C (F)(F)
+CCCC C
+NC(=O)[C@H] (C
+% 1
+(C) (C)
+cc( OC
+CO c1ccc(cc1
+3 )
+C =C
+c1 n
+)cc 1
+ccccc 3
+Cl .
+c 1
+nc( N
+(C) CC
+\C(= C
+cc (C
+n 3
+[C@H] 3
+ccccc 4
+c( O)
+[C@H] (C)
+cc (Cl
+CCN (C
+C(=O) O)
+) \
+)C(=O)N [C@@H](CC
+c2 cc(
+CO c1ccc(
+[C@H] (CC
+CCC (CC
+) S(=O)(=O)
+) S(=O)(=O)N
+[C@@H] 3
+P (=O)
+CC OC(=O)
+)c( OC
+(C) (C)C
+O C
+N (C
+N 1
+c1 cccc(
+c2 ccc3
+c3ccc( cc3
+2 CCN(CC
+CC O
+c5 ccccc5
+C(=N )N
+c1 ccc2
+c1 cc(
+)C(=O) O
+n c3
+c s
+)C 3
+ccc (C)
+O c3
+5 CC
+CN (C
+)cc 2
+) [N+](=O)
+)c (Cl
+\ N=C
+o c(
+[O- ]
+4 C
+CN (C)
+c3 n
+N (CC
+cc (C)
+)C(F)(F) F
+(=O) O)
+S C
+COc1 cc(
+= CC
+c2 c1
+[n +]
+C (C
+4 )c5
+c (Cl
+c2 cccc(
+n c1
+CN (CC
+CC(C) (C)
+[C@] 1
+OC 1
+)C 1
+cc 5
+[C@@H] (N
+(C)C )
+ccc s
+CCN 1
+C1 =O)
+2 )C(=O)
+[C@H] (N
+cc cc2
+=C (N
+C 4
+C S
+2 CCCC
+S )N
+)c 6
+# N
+3 CCCC
+c2 cccc
+)cc 3
+2 C(=O)
+c3 cc(
+c1ccc(cc1 )c2
+)C(=O)N [C@@H](C
+)n 2
+(C) C
+CC (C)C
+C l
+(= N
+P(=O) (O)
+C(=O) O
+ccc 5
+\C(= N
+N S(=O)(=O)
+n cn
+ccc (CN
+O=C (N
+CN C(=N)N
+3 )C
+c3 ccccc3
+O [C@H](C
+n 4
+)c3 ccc(
+)c (C
+[C@@H]( O
+C2 =O)
+c 7
+NC(=O) c2
+[C@@H] 4
+c3 cccc
+CC 2
+ccccc 2
+c 2
+O c4
+cc(Cl )
+) NC(=O)[C@H](CC
+c3 cccc(
+[C@H]( NC(=O)
+COc1 ccc2
+C c2
+[C@@] 2
+C n1
+2 )C(=O)N
+( O
+c1 )c2
+- ]
+(C) C(=O)
+c 3
+)c3 ccccc3
+n 1
+1 )
+C c1ccc(
+c3 ccccc2
+4 )
+=C (C
+=N \
+ccc( F)cc
+ccccc 5
+[C@H] 4
+( /
+2 )N
+)c 2
+c1 cn
+cc( O)
+)c 3
+COc1 ccccc1
+2 )C
+C(= S)N
+c3 ccc4
+C1 =O
+\C=C /
+2 CCC
+1 CC
+ccccc 6
+[C@@] 3
+. OC(=O)
+(O) C
+[C@] 2
+[C@] 3
+OC(=O) C
+3 CCN(CC
+3 )C(=O)
+C c1ccc(cc1
+F )c(
+c3 ccc(Cl)cc
+N C
+c (CC
+= CC(=O)
+[C@@] 1
+n 2
+\C(=C /
+c4 ccccc4
+c4 ccccc3
+1 )C(=O)
+O) C(=O)N
+c2 cn
+\N=C \
+cc s
+C(F)(F) F
+)[N+](=O) [O-]
+CC C(=O)N
+[O-] [N+](=O)
+1 C
+C [C@@H]1
+\C(=C \
+c2 c1
+3CC OCC
+)c4 ccccc4
+(CC (=O)N
+c( OC
+)cc 2
+)C(F)(F) F
+(= C
+N c1
+[C@@H]( NC(=O)
+OCC (=O)N
+c4ccc( cc4
+) NC(=O)[C@H](C
+COc1 cc2
+O) cc
+cn c2
+c5 ccc(
+)C #N
+C(= C
+F C(F)(F)
+O=C 1
+c2 ccc(Cl)cc
+) O
+c3 c2
+)c3 ccccc3
+)c4 ccccc4
+COc1 cccc(
+)C(=O)N C
+cccc (C
+c3ccc( F)cc
+c2cccc( c2
+c3 cn
+\C =N\
+)C(F)(F) F)
+2 =O)
+)[N+](=O) [O-]
+C(=O)N C
+C NC(=O)
+Cc1 cc(
+3 CCC
+3 )N
+CC NC(=O)
+-] .
+1 )N
+CC c1
+(C)(C) C
+c(N )n
+n n2
+n1 )c2
+) s
+c 4
+)C(=O) O)
+C(=O)N (C
+4CC OCC
+=C(N )N
+=C (
+3 )n
+OC(=O) C)
+[C@@H] 5
+F )C
+cccc (Cl
++] .
+O [C@H]
+)c 4
+)n 3
+[N+](=O) [O-]
+c3 o
+4 )C
+%1 0
+c1ccc( N
+S CC(=O)N
+n c4
+[C@@H](O) [C@H](O)
+CCC 1
+cn c3
+CC Oc1
+# C
+)C(=O) N
+CCN(CC 1)c2
+( NC(=O)
+3 C(=O)
+c2 ccccc1
+\C(=N /
+)c (O)
+C(=O) O
+OC O
+c2 c(
+F )c2
+n c(C
+c1cccc( c1
+[C@] 4
+4 CCCC
+CCCN 1
+% 2
+[C@@] 4
+)cc 3
+( OC
+c (CN
+C(=O)N [C@@H](CC
+O) C
+OC (C
+c2ccc( F)cc
+C [C@H]1
+O[C@H](C O)
+Cc1 ccccc1
+[C@@H] 2C
+)c4 ccc(
+O C(=O)N
+c2 ccccc2
+CCCC N
+CC 3
+c6 ccccc6
+C #N
+C1 =C
+c2 nc(
+O P(=O)(O)
+[N a
+ccc(Cl )c(Cl
+4 )n
+c3 c(
+[C@H] 5
+c4 cccc(
+[Na +].
+c1 ccc(C
+)C #N
+cc(OC )c(OC
+c1 2
+2)c3 ccc(
+CCCC C1
+c4 n
+NC(=O)[C@H] (C)
+)c (N
+) \C
+C(=O) C
+ccc( cc2
+) cc(
+4 CCCCC
+2 CCC(CC
+(C (=O)N
+O CCN
+[C@H] 2C
+3 )C(=O)N
+cc(OC )c1
+Cc1 cccc(
+C3 =O)
+o nc(
+F )c3
+) ccc1
+[C@@H]1 C
+)cc 4
+)c (C)
+CCCN (C
+[ S
+[C@@H]1 CC
+CC1 (C)
+(CC C
+C(=O)N [C@@H](C
+CC OCC
+NC(=O) C2
+C2 =O
+c2ccc( O)
+1 =O)
+)C(=O) C
+n 3
+c4 ccc(Cl)cc
+)n 1
+cc (CN
+c4ccc( F)cc
+CCN CC
+)CC 1
+[nH] c(
+(CC N
+) NC(=O)C
+CC [C@H](C)
+)c2 c1
+c4 cc(
+CCC 2
+n 5
+1 )C
+cccn c3
+)CC 2
+[C@H]( O
+.OC(=O) C(F)(F)F
+c3ccc( OC
+)C(=O) OC
+[C@@H] 3C
+S(=O)(=O) C
+2 /
+c1 cc2
+\C=C (/
+( [O-]
+ccc nc2
+c2ccc( OC
+NC(=O) CN
+ccn cc
+(C)C )C(=O)N
+C(=O) OC
+C(=O)N 1
+c2 c[nH]
+c 8
+[N+](=O) [O-]
+N c1n
+[C@H]1 C
+2CC OCC
+(C)C )C
+2CCN(CC 2)c3
+n cc(
+[C@@] 5
+CCCC CC
+CN =C(N)N
+c( c1
+[C@] (C)
+[C@H]1 CC
+c3cccc( c3
+[C@@] (C)
+=C 1
+n c(C)
+n2 )c3
+N 2
+o ccc
+\C(=N \
+cn 2
+[C@H]( NC(=O)[C@H](CC
+ccc(C) cc
+4 CCC
+3)c4 ccccc4
+c5 ccccc4
+NC(=O)C S
+[C@H](O) C
+CC n1
+c4 ccc5
+= CC(=O)N
+CC (N
+3 )cc1
+c3ccc( O)
+1 )C(=O)N
+Cc1 ccc2
+n (C
+c2 cc1
+n [nH]
+c(Cl )cc
+[C@@H] 6
+c2ccccc2 )
+COc1 ccc(C
+3CCCC C3
+[C@@H](C O)
+( S
+c2cccc 3
+c1 c[nH]
+3 =O)
+c1ccc(cc1 )N
+CC (C
+[C@@H] 2CC
+[C@@H](N )C
+cc cc1
+)c( c1
+[C@H]1 O
+[C@@H] 3CC
+s c(
+c2 o
+N c2n
+)c 7
+CC(=O) O
+C OC
+5 )
+NC(=O) c1
+O) cc1
+O) N
+2CCCC C2
+2CCN(CC 2)C(=O)
+O=C (C
+cn c(
+3)c4 ccc(
+(C )c2
+[C@@H](O) C
+c2 cc3
+cc 6
+)C 4
+cs c(
+c5 ccccc5
+NC(=O) CC
+CN (C)C
+N CC
+COc1ccc(cc1 )c2
+CN1 C(=O)
+)C2 =O)
+)c3ccc( cc3
+[C@H] 3C
+C(F)(F) F
+C) \
+[C@H] 3CC
+c %1
+)c5 ccccc5
+CCN (C)CC
+)C1 =O
+O) C(=O)
+CCN(CC 1)C(=O)
+OC (C)(C)C
+(F) (
+4 )cc1
+4 CCN(CC
+cccc 3
+C S(=O)(=O)
+cc (CC
+5CC OCC
+5 )c6
+cc(C (=O)N
+c1ccc(cc1 )C(=O)N
+)C(=O)N[C@@H](CC CCN
+C (N
+3)c4 ccccc4
+c3 c[nH]
+C1 =N
+cc 1
+c1 nc(
+( c3ccccc3
+[n+] 1
+4 )N
+OC )
+cccc 4
+c4 cn
+Oc1 ccc2
+c4 cccc
+\C=C\ 2/
+)CC 2
+Br )cc
+[C@H] (CN
+[C@H] 6
+)C (C)(C)C
+nc(N )n
+2) S(=O)(=O)
+( c2ccccc2
+OC Oc4
+c1ccccc1 )c2
+Cl c1ccc(
+Cc1 n
+O c2
+5 C
+n 4
+)C(=O)N[C@@H](CC CNC(=N)N
+S )
+)cc1 OC
+)c5 ccccc5
+N C(=N
+ccccc 7
+c2ccc(O) cc2
+N (C(=O)
+(C) C(=O)N
+OC 2
+O c1ccc(
+CC1 =C
+CN (
+c1ccc(cc1 )C2
+ccccc1 3
+\C=C \C
+O) c2
+cn cn
+[C@H]( OC(=O)
+CCOC(=O) c1
+% 3
+[C@] 5
+c4 c3
+c2 s
+c( F)cc
+O S(=O)(=O)
+CCCN (CC
+3 CCC(CC
+c o
+c3 ccn
+COc1ccc(cc1 OC
+ccc 6
+c1ccc(cc1 )S(=O)(=O)N
+c2 3
+O CCCC
+C2 =N
+) ccc2
+\C=C\ C(=O)
+cccc (CN
+[C@@H]( [C@@H](C)
+n (
+O) \
+N C(=S)N
+c4ccccc3 4
+CC (=
+/ C
+)C (=N
+Cl c1
+)n 2
+(O) (C
+1 CCC
+[C@@H]( NC(=O)[C@H](CC
+F )c4
+c3ccc(Cl)cc 3
+OCC O
+CC (
+c2 nn
+CCCCC 3
+c2ccc(Cl)cc 2
+COc1cc( cc(OC)c1
+[C@H]( NC(=O)[C@H](C
+c3 cccs
+O CCC
+\N=C (/
+CCCCCCCC CCCC
+n n1
+ccc(C) cc3
+COc1cc( ccc1
+cc(OC)c(OC )c(OC
+S CC
+C3 CC
+)c2 cc1
+(CC (C
+N (
+F c1ccc(
+[C@]4 (C)
+c3cccc 4
+cc( c1
+c3 cccnc3
+ccccc 6
+N1 C(=O)
+C (
+)C1 =O)
+[C@@H] 7
+C2 CCCC
+)C2 =O
+2)c3 ccccc3
+ccc(Cl)cc 1
+OC Oc3
+[C@@H] 5CC
+=C 2
+C 5
+N C(=N)N
+C [C@]1
+cc nc(
+CN (C)CC
+[ Br
+cc( F)
+)N 2
+OCO c5
+[Br -].
+C OC1
+[C@H](C O)
+c2cc( ccc2
+N1 C
+2 )C3
+nc( s
+cc( N
+n2 C
+nn (
+cn c(N
+F )c
+c3ccc(O) cc3
+(C O)
+C (CC
+CN (C(=O)
+COc1cccc( c1
+4CCOCC 4
+Cc1 ccc(C
+COc1 cc(OC
+)C (=
+(C )c3
+CN(C) C(=O)
+(C) c1
+(CC CC
+c3ccc(F)cc 3
+[C@@H](N )
+c2c[nH] c3ccccc2
+[C@H]( [C@H](O)
+5 CCCC
+OC 3
+C3 CCCC
+6 CC
+cccn c4
+c2 ccn
+cccc( c3
+c( cn
+s 1
+\C=C\ C(=O)N
+CC (O)
+)cc 4
+c1cccc( c1)c2
+3CCOCC 3
+) /
+ccc(C) cc2
+= C
+(C)C )N
+c6 ccc(
+4CC 4
+c2 ccc(C
+O[C@H](CO) [C@@H](O)[C@H](O)
+(C) N
+(F)(F) F)cc
+C(= C1
+COc1 ccc(CN
+c4ccc( O)
+B r
+c1ccc( O)cc1
+c4ccc( OC
+)n c(
+/C /
+cccn c1
+Cc1 o
+OCC OCC
+O c3ccccc3
+4 C(=O)
+Cl. Cl.
+5 CCCCC
+c1cc( ccc1
+Cc1 cc(C)
+CCCCCCCC CCCCCCCC
+c 5
+[S +]
+CC S
+c3 s
+4 )C(=O)
+N (C)C(=O)
+[C@H] 7
+[C@@H] 4CC
+)\ N
+cc(Cl) ccc3
+c3 cc4
+[C@H]( NC(=O)C
+N=C (N
+(CC (=O)O)
+2)c3 ccccc3
+O 1
+c3cccc (Cl
+C2 =C
+)c( c2
+cc(Cl) ccc2
+c1 nc2
+CCOC(=O) C1
+ccccc1 4
+c2ccc(cc2 )c3
+) ccc3
+C3 =C
+c1ccc(cc1 )C
+) OC
+n o
+c(N C
+C #N
+c1 o
+nc( cs
+3CCN(CC 3)c4
+c1ccc( OC
+2 )c4
+C(=O) C=C
+C [C@@H](O)
+5 )n
+F c1
+)C2 =N
+c3o ccc3
+) C
+[C@H]( OC(=O)C)
+(F)( F)C
+c2 c(C
+CCC (C
+CCCC 1
+cc( cn
+CCN(CC )
+3 )c1
+)c4ccc( cc4
+(Cl )c2
+c2 oc(
+[C@@]2 (C)
+c2ccc3 ccccc3
+c2 c(C)
+CCN(CC 1
+ncn c(N
+[C@@H] 4C
+c1 nn
+[C@H] 2CC
+[C@@H] (C)C
+c5ccc( cc5
+CN C
+5 )C
+c3ccc( Br
+(C)C) NC(=O)[C@H](CC
+1 CCCCC1
+cc( NC(=O)
+c1c[nH] c2ccccc1
+cc(Cl )cc
+cc( ccc2
+c4cccc( c4
+)n 4
+C [C@H](N
+c3 c2
+c2ccc( N
+CC(=O)N 1
+1 =C
+4 )C(=O)N
+c2 C
+cc (C(=O)
+Cc1 cc(C
+CCC (C)
+nc( O)
+CCC NC(=O)
+cc nc(N
+c4ccccc3 4
+) N
+cn c4
+c4 ccccc2
+(CC N(CC
+COC(=O) c1
+c4ccc(F)cc 4
+N 2C(=O)
+C S(=O)(=O)N
+O [C@@H]1
+[C@H](O) [C@@H](O)
+c2ccc(F)cc 2
+(CC CCC
+)n c3
+c4 cc5
+%1 1
+( \
+n nc(
+)n 1
+ccc( OC
+[C@@H]1 O)
+2 CCN(C
+O c1ccccc1
+O)C(=O)N [C@@H](CC
+cc( Br
+[C@H] 4C
+c2ccccc2 )c3
+)C (C)(C)C
+c3 ccc(C)cc3
+)N (C
+c3ccccc3 )
+F c1ccccc1
+ccccc 1
+c 9
+N 3
+[C@]2 (C)
+)C(=O) O
+COc1cc(cc(OC)c1 OC
+CCCN1 C(=O)
+c1 cccc2
+[nH] n
+(CC (=O)
+O)cc 1
+c2n c3ccccc3
+5 CCC
+n2 )N
+C(=O)N CC
+Cc1 c(
+[Na+]. [Na+].
+C3 =O
+Cl c1ccccc1
+N C1
+[C@]3 (C)
+)C(=O)N[C@@H](CC (C)C
+c2ccc( Br
+OCC CN
+[n+] 2
+(CC 1
+COc1 cccc2
+[C l
+C(=O) N
+c2ccc( NC(=O)
+2 =C
+[C@@]3 4
+OC [C@H]1O
+ccn c2
+[Cl -].
+)c1 C
+ccc( N
+O) c(O)
+n1 C
+)c( F
+N c2
+CCCC (=O)N
+Cl c1ccc2
+n1 )N
+2 )cc1
+cn 3
+) [C@@H](O)
+[C@@H]( NC(=O)[C@H](C
+(=O) =O)
+)C(=O)N CC
+[C@]1 2
+O) c3
+nc2 c1
+c7 ccccc7
+[C@H] (C)C
+)C(F)(F) F)cc
+2)c3ccc( cc3
+c1 c(
+c1ccc( O
+(C)C )C(=O)
+cc n2
+CN1 CCN(CC1)c2
+c3ccccc2 3
+\C=C(/ C)\
+(O) CC
+c2 cccnc2
+S SC
+[C@@H](C N
+CC(C) (C)C
+4CC 4
+c1ccc( NC(=O)
+c4 o
+4 =O)
+cc [n+]
+nn 3
+3) S(=O)(=O)
+)cc1 Cl
+N 2CCN(CC
+O 2
+COc1ccc( OC
+c5ccccc4 5
+= [N+]
+(C)C )NC(=O)
+c4 ccn
+C(=O)N (CC
+CN 2
+1 )c3
+c2ccc3 OCOc3
+c3 c(C)
+OC(=O) C
+[C@@H]( OC(=O)
+c1ccccc1 )N
+2) NC(=O)
+)c2 c1
+O c3ccc(
+c1ccccc1 )NC(=O)
+CCCN 2C(=O)
+OCC (=O)
+CC(=O) O)
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+=CC (=N
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+) [C@@H](C)
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+=C 3
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+=C (C)C
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+=C (C(=O)
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+=CC (=
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+C \C(=N/
+C2 CCN(CC
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+CC C(=O)
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+[C@H](C) O
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+O c2ccc(cc2
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+C [N+]
+=C(C (=O)N
+\ N
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+(O) (
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+C c3ccccc3
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+2 =N
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+F )c(Cl
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+1 CCC(CC
+( \C
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+3) S(=O)(=O)N
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+)cc 2)
+3 CCN(C
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+(C c3ccccc3
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+[Si ]
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+(CC =C
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+) S
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+) [C@@H]2
+3 CCCCC3
+)C (
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+(C(=O) O)
+F )c5
+2C =C
+c( nc2
+=CC =C
+[C@]12 C
+)N (CC
+#N )
+( OCC
+NC(=O)N (C
+2CCC 3(CC
+=[N+] =
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+NS(=O)(=O) C
+CCN (
+CC1 (C)CC
+N C1=N
+c2ccc3OCOc3 c2
+[C@@H]( NC(=O)C
+CCC 3
+O P(=O)
+1)c2 ccc(cc2
+1 CCCC
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+)C (C
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+(= S)
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+2 C(=O)N
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+)C(=O)N[C@@H](CC (=O)O)
+cc o
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+(N )
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+) cc(OC
+O= S(=O)
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+c3 ccc(Cl)c(Cl
+NC(=O) COC(=O)
+)c2cc1 OC
+=O) N
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+CC CO
+C 1
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+)c3ccc( F)cc
+)c3 c2
+(C) C(=O)NC
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+)C(=O)N [C@@H]([C@@H](C)
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+[C@@]34 C)
+1)c2 ccc(
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+c4ccc(Cl)cc 4
+c1n nc(
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+CCN(CC )C(=O)
+c2 nc(N
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+COc1ccc(cc1 )S(=O)(=O)N
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+O) c4
+[C@H](C (=O)N
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+Cc1 nc(
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+[C@H]( OC
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+(CC CN
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+C(= S)
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+3 /
+1 CCN(CC1)c2
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+(c3ccccc3 )c4ccccc4
+S \C(=C/
+O) C(=O)O
+CCN (C(=O)
+C(=O)N C(=N
+COc1cccc( c1)c2
+Oc3 c2
+5 CCN(CC
+Br )cc1
+)c2 n
+O) cc5
+c3ccccc3 )NC(=O)
+)C #C
+[nH] 3
+[C@@H]5 C
+6 )
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+[C@@](C) (C
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+2 \
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+C [C@H](NC(=O)
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+S S
+O[C@H] (C)C
+n cccn
+4)c5 ccc(
+C4 CCCCC
+2CCC [C@H]2
+N #
+CCC (N
+)c2 ccc(cc2
+O O
+=N /
+OCC (O)
+OCC Oc4
+)S(=O)(=O) N
+CC(=O) OC
+COc1 cccc(C
+O [C@@H](
+( c4ccccc4
+(= CC
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+(=C (N
+S 2
+[n+] ([O-]
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+) [C@H](O)
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+C )C(=O)N
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+4 CCC(CC
+c2n cn
+Cl c1ccc(C
+[C@@H]2 CCCN2C(=O)
+(C c4ccccc4
+F )c3
+(C 1
+2 )n3
+(Cl )
+c3 [nH]
+F c1ccc2
+ccccc1 5
+c3ccc( N
+=C(C #N
+[C@@H] 9
+OCC CO
+3)C 4
+\C(=C \C
+6 )C
+n1 )C(=O)N
+c3ccc(cc3 )N
+) ccc4
+c3ccccc3 Cl
+c5 cc(
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+OC1 =O)
+OC 3=O)
+3)n 4
+c2 ccc(Cl)c(Cl
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+(=O) =O
+n (CC
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+=[N+]= [N-]
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+(C)C)N (C)C(=O)
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+CC (NC(=O)
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+)C =C
+c3 oc(
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+OC [C@H]1
+C [C@@H](
+)C(=O)N[C@@H](CC CN=C(N)N
+c4 s
+OC1 =O
+[C@]3 4
+NC(=O) C3
+c4 ccc(C)cc4
+C \C(=C/
+CO CCN1
+[C@H] 9
+CC =C
+[C@@H](O) [C@@H](O
+)c(C) c1
+Cc1 nn
+cc 7
+c1ccccc1 )C(=O)
+)\ C
+OC )c3
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+CCN C
+CCC (=
+CC NC(=O)N
+cc2 c1
+3CC4CC (CC(C
+[C@@H]4 CCCN
+) [C@H]2
+C2=O) cc1
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+O=C(N C1
+C#N )\
+O c2ccc3
+.OC(=O) C(F)(F)F
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+1 C(=O)
+CO [C@@H]1
+3 =C
+)c6 ccccc6
+OC )N
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+c2 [nH]
+[C@H](CC (=O)N
+C) \C
+)C(=O)N [C@H](C
+(C 2)
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+)c3ccc( OC
+(N )C
+(=N )N
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+[C@H] %1
+[ P
+2) \
+CN1 CCC(CC
+)C N
+[C@H](C) [C@@H](O)
+CC[C@H]1 OC(=O)
+NC(=O)[C@H](C) NC(=O)
+CN S(=O)(=O)
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+C [C@H](C
+[C@@H]1O) n2cnc3
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+c3 ccccc13
+C 6
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+O)N (C)C)
+O [C@@H](C
+NC(=O)[C@H](CC C(=O)N
+C [C@H](CO)
+c2cc1 OC
+Cc1cc( N
+c oc(
+C [n+]1
+ccn cc1
+[C@@H]5CC [C@@]34C)
+[O-] )
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+Nc1n cnc2
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+(F)( F
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+3CC4CC(CC(C 4)C2
+CO [C@H]1
+CN CC
+s 1
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+)c1 O
+O[C@H](C)C [C@@H](
+CCCN 2
+5 )C3
+[C@@H]1 NC(=O)
+(C)C) NC(=O)[C@H](C
+)c2 cc(
+cc1 2
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+C=C (C(=O)
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+c4 nn
+CC(C)C [C@H](NC(=O)[C@H](CC
+3 )NC(=O)
+4 (CC
+[C@@H](CC (=O)N
+3CCC [C@H]3
+S C(=N
+O [C@H]([C@H](O)
+COc1ccc(cc1 )C(=O)N
+c(N)n cn
+C [C@H](
+O c2ccc(
+(=O)O) NC(=O)
+O [C@@H]3
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+C=C (C)C(=O)NC
+)cc1 2
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+F c1cc(
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+1 )NC(=O)
+F c1ccc(C
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+5 C(=O)
+OC(=O) \C=C\
+c(O) c3
+(CC )
+C2 =
+2 )C(=O)O
+F)c( F)c(
+F)cc 3
+c2ccccc2 Cl
+C (C)C
+c3cccc( F)c3
+NC(=O) \C(=C\
+C [C@@H](N
+( NC(=O)N
+/C/ 1=C
+n 3C
+c3ccccc3 )C(=O)N
+[N+] (C)(C)C
+c4ccc5 ccccc5
+c3ccc(cc3 )c4
+S CC(=O)
+)\ N=C
+c1n cn
+[C@H] (C)CC
+(CC )C
+c5 cn
+[C@](C) (O)
+CN 2C(=O)
+c1 C(=O)N
+1 \
+n1 )C2
+c2c1 )c3
+C =
+c2 cc(OC
+CCCC N(CC
+(CC N(C
+O 1
+ccc(C)cc 4
+[nH] 1
+CCO P(=O)
+) NC(=O)[C@H]
+[C@H]3 O)
+n n1)c2
+[C@@]2 3
+OC )c2
+Cc1 ccc(CN
+N 2C
+CCN 2C(=O)
+c3ccccc3 c2
+cc( cc1
+O) cc3
+ccc 7
+2 C1=O)
+c3ccc(cc3 )S(=O)(=O)N
+c1 c(C)
+c3 cc(Cl)ccc3
+c2c[nH]c3ccccc2 3)
+c2ccccc2 C1=O)
+c1cn (
+)C (C)
+c3 cccnc3
+CC S(=O)(=O)
+cc(Cl )c(
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+CC(C) [C@H](NC(=O)
+O)cc 2
+\N=C\ 1/
+n 3)
+CCN 2
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+N 4
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+NC(=O)C =C
+F c1ccc(cc1)c2
+(F) F)
+OC )c1
+c1cn c(
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+2 =O
+F c1cccc(
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+c2 cc(Cl)ccc2
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+[nH] nc(
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+Cc1cccc (C)c1
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+c5ccc( O)cc5
+ccn c4
+CC C(=O)O)
+C3 C
+C=C 3
+CCOc1 ccc2
+CCO C(=O)N
+c(O) c2
+) CC1
+CN(C) S(=O)(=O)
+C )c3
+6 )c7
+ccc(C (=O)N
+c3ccccc3 )C(=O)
+OC C
+O [C@@H](O
+Cc2 ccccc2
+\C(=N\ O)\
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+O) [C@H](O)
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+c5 c[nH]
+c1 2)c3
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+)n (
+N C(C
+CCC S
+cc(C #N
+CN 3
+[C@H](N )C(=O)N
+[C@H](N )C
+[C@@H](C) C(=O)N[C@@H](C
+)N (C)C
+c1 N
+CCCN (CCC
+(F)(F) F
+2CC 3CC
+N1C [C@H](C)
+cc2 Cl
+e ]
+[C@H]3 [C@@H](CC
+COc1 cccc(CN
+)C(=O)N (C
+)C (N
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+=C(C(=O) O)C(=O)
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+=CC =CC
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+=C (O
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+=N N(C
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+F) ccc2
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+3 )C1
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+1 CC1
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+( OC(=O)
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+[Si] (C)(C)C
+=CC(=O) C=C
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+3 CCN(C)CC
+2)c3 n
+Cc1ccc( N
+F )c2
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+) OC(=O)
+O [C@H]3
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+3 )C(=O)NC
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+)\ C(=O)
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+COc1cc( N
+)\ C(=O)N
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+CCCC CCN
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+1 )C2
+(C(=O) C
+F) ccc3
+c7 ccccc6
+[ 1
+[O-] C(=O)
+)C2=O) cc1
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+) [C@H](C)
+c( c3
+CC NC(=O)C
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+[C@H]( O[C@H]
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+[S e]
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+2)C(=O)N C1=O
+)C 3CC
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+)cc2 3
+c3cc( Br
+nn n[nH]
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+Cc1onc(C) c1
+N 1
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+4CC CO
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+C NC(=O)C
+cc(OC)c(OC)c(OC )c2
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+c1 ccc(C)cc1
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+C(=O)O .
+ccn 3
+4CC 4)n
+)c( O
+c3ccc(Cl)cc3 Cl
+)c( n2
+c4ccccc4 Cl
+c3ccccc3 C2
+[C@@H]2 O)
+)C3 =C
+)C(=O)N [C@H](CC
+) (C
+C2 =C(C
+3)C #N
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+c( cc(
+[C@H] %10
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+2) [N+](=O)[O-]
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+5 CCN(C)CC
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+c2 )C(=O)N
+(CC 2
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+[C@]2 3C
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+[C@@H]2 C1
+[C@@]3 (C
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+[Br-]. [Br-].
+Cl )S(=O)(=O)N
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+(Cl)C l
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+c(Cl )
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+n c(CN
+c2ccccc2 C(=O)N
+CC1 =CC
+[n+] (C
+c 6
+=C( S)N
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+[C@](C) (C
+Oc1 c(
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+NC(=O) C(=O)N
+F )c(N
+2 CCN
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+O CCN1
+C [C@H]3
+(C)C )C(=O)N[C@@H](C
+c( cc1
+[C@@H]( OC(=O)C
+) cc(OC)c1OC
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+c2 c3ccccc3
+COc1ccc(OC )c(
+C) C
+ccn cc4
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+s 3
+c3c(F)cc cc3
+=C( S
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+( OC(=O)C
+[C@H]( NC(=O)N
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+[C@@]5 (C)
+[C@@H]1 O)N
+2)C(=O) C
+1 \C
+) \C=C\
+[ K
+C1 =
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+\C =N/
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+( OC(=O)C)
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+[K +].
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+) [C@H]3
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+n3 )N
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+O) C2
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+CC (C(=O)N
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+c3n cn
+\C=C/C \C=C/C
+F)c (F)
+Nc1n cn
+NC(=O)C2 =C
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+C(=O) c2c1
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+(CC C(=O)O)
+NC(=O)[C@H] (NC(=O)
+c1ccc( F)cc
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+CC(C) N(C
+4 =N
+[C@@H]( NC(=O)N
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+)c4 cn
+(CC C(=O)N
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+\C=C\ C(=O)O)
+C [C@@H](NC(=O)
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+1) O
+[ 2
+n1 2
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+)c (CN
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+C3 CCN(C
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+[C@@H]5 CCCN
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+3 CCN
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+CC1 )c2
+)c( n1
+)c (C(=O)
+c2c(C 1)
+3CCC (C
+C[C@H]1 C
+CCCC NC(=O)
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+) CC(=O)N
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+[C@H]1 N
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+(O c2c1
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+3) [N+](=O)[O-]
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+c4 ccc(Cl)c(Cl
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+(C c2ccccc2
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+( Br
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+c2n ccs
+)\N=C \
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+3 C(=O)N
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+(CC (C)(C)
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+C(=O)N 3
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+C[C@H]1 CC
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+CO c2ccccc2
+5 )N6
+[C@]2 (C
+CCN(CC1 )C2
+)C(=O)N[C@@H]([C@@H](C) CC
+n1 )
+c4cc5OCOc5 cc4
+c3ccc(Br )cc3
+COc1ccc(OC )c(c1
+)c2 C1=O
+NS(=O)(=O) c1ccc(
+C =CC(=O)
+)n 2C
+c1ccc2 ccccc2c1
+c1ccc2 OCO
+ccccc2 5
+c2oc( nn2
+CC1 CN(CC
+S C2
+COc1cc(OC )cc(
+)c1 C#N
+)c(F )c2
+c5 cccnc5
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+OC 5
+n2 )c1
+[C@H]( F)C
+c3cccc(Cl )c3
+CC(=O)N 1CC
+c2ccccc2 )c3ccccc3
+c( SCC(=O)N
+S c3ccccc3
+= S
+c5ccc(F)cc 5
+Oc1 nc2
+2CC (C
+c2ccc( NC(=O)N
+c1ccc(cc1 )S(=O)(=O)
+[C@H](O)[C@@H](O) [C@@H]1O
+[C@@]4 5
+F c1ccc(cc1)N
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+c2 csc(
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+CCCCCCCC CCC
+(C 1=O)
+)C(= S)N
+[C@@H]3 O
+c4ccc( cc3
+[P +]
+N(CC C
+)c4 c3
+[Na+].[Na+]. [Na+].[Na+].
+Cc1ccc(cc1 )C(=O)N
+C[C@@H](O) [C@@H]1
+c2cn c[nH]
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+O[C@H](CO) [C@H](O)[C@H](O)
+[C@@] (C
+N =N
+)C(=O)N[C@@H](CCCNC(=N)N )C(=O)N
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+c2ccc(cc2 )C#N
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+3 CCNCC
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+CC c1cc(
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+c(C (=O)O)
+CCCCCCCC (=O)
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+)C 4
+cccc(Cl )c5
+C) S(=O)(=O)
+[N H
+NC(=O) CN(CC
+)c5 cc6
+\N=C(/ C)\
+CCC S(=O)(=O)N
+CC 4
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+[C@@H](CC (=O)O)
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+COC(=O) [C@H](C
+7 CCC
+[C@@H]2 O
+CN(C)C 1
+CCCC 2
+c1 ccncc1
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+)C(=O)N[C@@H](CCCNC(=N)N )C(=O)N[C@@H](C
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+O [C@@H]1C
+) CCC
+(CC(=O)N C
+OC c3ccccc3
+N2 CCN(C
+c5 [nH]
+OS(=O)(=O) O)
+ccncc 5
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+C(= C3
+c3ccccc3 )c4ccccc4
+c2ccc( Oc3ccccc3
+c1ccc(cc1)C2 =N
+OC(= CC(=O)
+6 C(=O)
+F)c 5
+CCN(CC )C
+CCCCCCCC CCCCCC
+c2 cc(OC)c(OC)c(OC)c2
+c( O
+CCN1 CC
+CCN1 C
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+c2c1 )C(=O)N
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+COc1cc(OC )cc(c1
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+O=C (CN
+)c4 cccnc4
+[C@H](O) [C@@H](CO)
+F) C(=O)
+C4CCCCC 4
+C1=O) N
+)N S(=O)(=O)
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+c2 3
+C(=O) [O-]
+5CC 6CC
+c4ccc(cc4 )C#N
+4 )C(F)(F)F
+cc( NC(=O)C
+[C@@]2 (C
+Cc1 csc(
+\C=C\ 1/
+CCC2(CC 1)C
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+FC (F)(F)C
+)c6 ccc(
+OC )C(=O)N
+CCc1 cc2
+nc2 ccccc1
+[C@@H](CC N
+Nc1ncn c2c1
+n1)c2 ccccc2
+c2ccc( OCC
+CCN )NC(=O)[C@H](CO)
+C2 CC2
+[C@H](CC (=O)O)
+FC(F)(F) c1ccccc1
+c2ccc(cc2 )C(F)(F)F)
+C(=O)N[C@@H](CC CCN
+NC(=O)C NC(=O)[C@H](C
+COc1ccc( CCN
+c2cc( cc(c2
+c1 4
+[C@H](C)[C@@H](O) [C@]1(C)
+C=C(C(=O) O)C(=O)
+5CCC [C@H]5
+CCC =C
+n 4C
+Cl )C(=O)
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+c4 ncn
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+NC(=O) c1ccccc1
+S 3
+)c(Cl )c2
+c4ccccc4 )c3
+c3 c(N
+nc1 3
+[C@]3 4C
+N C1=O
+C(=O) OCC(=O)N
+Cl c1ccc(N
+C3 (CC
+CCCC (C
+O c2c1
+CCCC [C@H](NC(=O)
+cccc n2
+\N=C(/ S
+CC(C) C
+3)C 2
+/C/1=C \
+c(N)n cnc2
+[C@@H]2CC [C@H](CC
+CSCC [C@H](NC(=O)
+4C l
+c3 nc(cs
+CCCC (=O)O)
+4)n 2
+n3 )C
+N CCN
+CC #
+c2ccccc2 )NC(=O)[C@H](CC
+CC1 C
+C(=O)N (C)C
+nc1 2)N
+Nc2n cn
+CN1 CCC
+CC[C@H]1OC(=O) [C@H](C)[C@@H](O
+n1 C
+)C(=O)N[C@@H](CCCCN )C(=O)N
+O=C1 N(CC
+CN1 N(C(=O)C(=C1
+CC (CN
+(O)C (O)C
+c2ccc3 c(c2
+COc1ccc( \C=C\C(=O)
+3CCC(CC 3)
+c5ccccc3 5
+c4ccccc4 )CC
+c(OC )c1
+CCN1 CCN(CC1)c2
+5 )c4
+n cc4
+c1 [nH]
+[C@@H](NC(=O) OC
+1 )S(=O)(=O)
+)c(Cl )c3
+n2 cc(C
+n2 )C(=O)N
+c3cccc (C
+)c7 ccccc7
+)c5 cn
+c3 c(OC
+[C@] 8
+Cc2 occc2
+COc1ccc( CCNC(=O)
+CCC1 =C
+s c(N
+[C@@]1 (C
+)n 1C
+c6ccc(O)cc 6
+)c n2
+cc(OC)c(OC )cc2
+c3n 1
+c3cc(C) ccc3
+N (C)CC
+ccccc %1
+ccc [n+]
+C(=O) C(=C
+c4ccc(Br )cc4
+OC (F)(F)
+)n [nH]
+Cn1 nc(
+2) ccc1
+)c3n c4ccccc4
+c3ccc(Cl)cc 3)
+Cc1 nn(
+)C(=O)N[C@@H](C O)C(=O)N
+nn [nH]
+cc(OC)c(OC )cc3
+c3ccc( Oc4ccccc4
+c1ccc( nc1
+) [C@@H](C
+oc( nn
+n1)c2 ccc(
+c(C S
+c( cccc2
+c2ccn c3
+c2 cs
+4 )C(=O)O
+c5 ccccc15
+C [C@H](NC(=O)C
+)n2 )cc1
+)c( F)c2
+c6 c[nH]
+\C(=C (\
+c6 cccc(
+)cc 2C
+)c( NC(=O)
+c2ccc(cc2 )n3
+[C@H]4 O)
+)\C )C
+n cccc3
+[O-] [n+]1
+C[C@@H]1CC [C@H]2
+CN CCO
+3)C(=O) C
+2CC 2
+)C 6
+CCC 5
+(= C1
+[C@]5 (O)C
+O [C@@H]5
+COc1ccc(cc1 )N2
+C(=S) S\C(=C/
+cccs 2
+c3ccc(OC )c(OC)c3
+c( SC
+ccn c3
+OC )c4
+CCOc1 ccc(C
+CCCN 1CC
+CCCC CCC(=O)N
+\C=C\ c2ccccc2
+CCOC(=O) c1cn
+)NC(=O) [C@@H](NC(=O)[C@H](CC
+c4 cc(OC
+c2cccc (CN
+=C 4C
+OP(=O)(O) OP(=O)(O)
+2)C N
+cc(Cl )c(N
+\C=C\ C(=O)O
+CCCC O
+c2ccc( NS(=O)(=O)
+c2 cccnc2
+NC(=O)c1 cccc2
+5 )C(=O)N
+O) cc4
+c5ccc6 ccccc6
+NC(=O) c1ccc2
+)c(c1 )C(=O)N
+) O
+c4 c(C
+ccc3 4
+c6 n
+c2c1 )C(=O)
+C3CC 3
+CC(=O)N c1ccc(
+\C(=N \C
+(=C (C)
+CCCN (C)C
+[C@]2 (C)CC
+F)cc 4
+)C(=O)OC C
+(Cl)C l
+\C=N\ NC(=O)c2
+(CC )CC
+c2cccs 2
+COc1ccc( \C=C\2/
+8 CC
+cc3 c2
+c3oc( cc3
+)cc1 3
+cc( [nH]
+N1 CC
+COc1ccc2 [nH]
+2C (C
+cc(ccc2 c1
+[C@]1 2CC
+NC(=O) C(=C1
+CC1(C) CC(=O)
+4C c5ccccc5
+CN 2CCC
+c2s c1
+[C@@H]2 CN(C
+NC(=O) C
+c2ccc3ccccc3 c2
+6CC 6
+)c3 ccc(Cl)cc3
+c3c2 )cc1
+COc1ccc( CC
+nc4 c3
+c2c(C 1
+COc1ccc( \C=C\
+3)C(=O) N
+c(C =O)
+c( n
+Cc1ccc(cc1 )C2
+CCCCCCCC (=O)N
+4CCOCC 4)c5
+CCOc1 cccc(
+CCCN 3C(=O)
+cc[n+] (C
+(S )O
+c3cc( ccc1
+C (C)C)C
+c4 cc(Cl)ccc4
+Nc2n cc3
+c2ccccc2 )\
+c2ccc(cc2 )C3
+[C@H](C)O 2)
+5 CCC(CC
+[C@H](C) N
+OC (=C
+CN=C(N)N )NC(=O)
+c1ccccc1 )S(=O)(=O)
+cc1 C(=O)N
+OC(=O) C(C
+)c3 4
+(O) CO
+c3cc( F)ccc3
+COC(=O) c1ccc(
+CC3 =O)
+3)c4 o
+c3cc(OC)c(OC)c(OC )c3
+c1 onc(
+S(=O)(=O) C1
+C=C3 N(C
+=CC(=O) CC
+c2cccc (Cl
+COc1cc2 ncnc(N
+C=C 2
+Oc1 ccc(Cl)cc1
+c( s
+c3n cccc3
+O=C 1C
+)C(=O)N O
+cn c[nH]
+c4ccccc4 )C(=O)
+\C=C(/ C)
+[C@@]2(CC [C@]3(C)C(=CC
+C3 CC3
+FC(F)(F) c1cccc(
+)c3 cc(
+) [C@@H]3
+c3cccc4 ccccc34
+c1 cc(C
+c(N S(=O)(=O)
+[C@](C) (
+NC(=O)N C1
+c2n c(C)
+CO CCn1
+CCN 3
+CC OC1
+[C@@H]1 CCCN1
+CCN 3C(=O)
+Cl. CC(C)
+)C(F)(F)F )c2
+c3ccccc2 3)cc1
+c2 ccnc(N
+c1 C
+CN(C 1
+c6 cccc
+c4 cs
+[C@H]1CC[C@H]2 [C@@H]3
+C[N+] (C)(C)
+c4ccccc4 C3=O
+[C@H]2 O
+)c( c4
+c2n nc(s
+COc1ccccc1 C(=O)N
+)N 5CCOCC5
+) ccc5
+nc( nc3
+Oc3 cc(O)
+c( cccc1
+3) [N+](=O)[O-]
+n1 cnc2
+[C@]4 5
+COc1cc( \C=C\C(=O)
+CC1=CN( [C@H]2C
+(C 2
+nn c3
+c3o nc(
+CC(C)(C) N
+ncn (C
+ccc(Cl)cc 5
+\C=C (\C)/
+O 1)
+C2=O) N
+[C@@H]2 CCN(C
+O [C@@H](CO)
+NC(=O) CCC
+ccc3 c2
+OCC (C
+=CC 2
+Cl )C
+c4ccccc4 N
+c2cccc3ccccc2 3
+NC(=O)c2 occc2
+Cc1 cnc(
+COc1 ccc(C)cc1
+6 =O)
+nc( nc(N
+c(CC (=O)N
+)c s1
+)C(=O)N C2=O
+CC (=C
+3 \
+)C(=O)N[C@@H](C O)C(=O)N[C@@H](CC
+OC(=O) CCC
+[C@H](N S(=O)(=O)
+Nc2n ccc(
+(c1ccccc1)c2 ccccc2
+NC(=O)C NC(=O)[C@H](CC
+)C(=O) CN
+(C)(C) O)
+cn 1)C(=O)N
+c%1 3
+[C@H](OC(=O) c4ccccc4)
+7 )c8
+[C@H](C OC3=O)
+C )C
+(c4ccccc4 )c5ccccc5
+(= C2
+n c4ccccc4
+c6ccccc5 6
+c3cccc( O)c3
+S C1
+O) [C@@H](O)
+c3cccc( OC)c3
+Oc3 cc(
+C2=O) C(=O)N
+)c1 F
+5)c6 ccccc6
+) [C@@H](O
+[C@]23 C
+=C1 C=C3N(C
+)\C 1=O
+COc1 ncc(
+c1 2
+[C@@H]5CC [C@@]4(C)
+CC (O
+O=C 1CC
+Br c1ccccc1
+4 )c3
+c3ccccc3 )c2
+CC [N+]
+Cl) CCCl
+C [C@@H](O
+)C(=O)OC (C)(C)C
+(C)C 1
+c3n cnc(N
+[C@H](O) [C@H](C
+NC(=N)N CCC
+CC(C) c1ccc(
+C(= CC(=O)O
+)\C 2=O)
+c3cc( cc(c3
+c2ccc(cc2 )[N+](=O)[O-]
+COc1cc(OC )c2
+CCc1 nc2
+c(O) c(
+N c1ccc2
+c2cc( F)ccc2
+o nc1
+[n+] 4
+= CCN
+2)C #N
+)C(=O) CC
+ncn (
+[C@](C) (CC
+2CC3CC (CC(C
+c( OCC
+[C@]1 3
+nc( NC(=O)C
+c4ccccc4C 3
+c3ccc( Oc4
+[nH]c( cc2
+N(C (C
+N c1ccc(
+5 )N
+2CCN(C c3ccccc3
+c6 ccc7
+[C@@H] %11
+/C/1 =N\
+cccc 6
+c2ccc(OC )c(OC)c2
+cc( cc(
+c2cccc( O)c2
+(C)C )C(=O)O
+[C@@H] %2
+3C =C
+c1ccc(cc1)N 2C(=O)
+c(Cl )c(
+[C@H]( NC(=O)[C@H]
+C[C@@H]1 CN(C
+CC(C) CCC
+CC(= C)[C@@H]1CC
+c4ccc(OC )cc4
+CSCC [C@H](NC(=O)[C@H](CC
+=C( Br
+)c(c1 )[N+](=O)[O-]
+ccc3 4
+c2ccc3OCC Oc3c2
+c2 sc(
+C=C (
+onc( n2)c3
+ccc1 3
+c2cccc(Cl )c2
+3) \C
+c2ccc(Cl )c(c2
+C2 CCCCC2
+[C@H]2C [C@@](C)(OC
+cc(F) cc(
+cc( nc(
+c3cc4 OCOc4
+5 )c1
+n cc(C(=O)N
+Cc1oc( nc1
+CCN(C c2
+c3cccc( Br)c3
+c1cccc2 ccccc1
+NC(=O)[C@H](C) N
+[C@H]2 O)N(C)C)
+)\C #N
+c3ccccc3 )c(
+OCC2 =C1C=C3N(C
+Cc1 n[nH]
+Cc1 cccc(CN
+CC(C) NC(=O)
+C(=O)O 1
+c2cn n3
+c1cc2 ccccc2
+cc (CCN
+C2 =CC(=O)
+c3ccccc3 )cc2
+[Si](C)(C)C (C)(C)C
+COc1ccc(cc1 )C2=N
+C O)
+Cc1o ccc1
+COc1ccc(C (=O)
+CCCC [C@@H]1
+CC(C) [C@H]1
+c(C )c(s
+c %4
+[C@H](C)O2) [C@H](C)[C@@H](O
+c4ccccc4 )C
+c(C )c(O)
+c4 c(F)cccc4
+[C@@]2 (C)C
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+- ]
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+) [C@H](C
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+=C1 N
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+=CC (=C
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+(= C)
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+5)c6 ccc(
+) OC
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+[C@@H](N)CCCN C(=N)N
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+/C/1=C /
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+Cc1 ccnc(
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+. CS(=O)(=O)
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+[C@@H]( Oc1c2
+CCOC(=O) c1cc(
+=C 1)
+= CC1=O)
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+[C@@] 8
+NC(=O) c5
+C(= CC
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+3)N 4CCOCC4
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+c2cccc( Br)c2
+F) N
+. [O-]
+n ccc1
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+[ s
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+\C=C\ c3ccccc3
+[N+] (C)(C)C
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+Cn1 cccc1
+COc1cc(cc(OC)c1 O)
+C4CCCC 4
+c3cc( F)c(
+c2ccccc2 )NC(=O)[C@H](C
+C(=O) [O-]
+3)N 4C
+[C@@]2(C) [C@H]1CC
+Cc1 ccccc1
+9 CC
+CCN 2C
+)cc3 4
+Nc1 nc(O)
+F c1ccc(cc1)C(=O)
+c3 cc(Cl)cc(Cl
+c2c(F)cc cc2
+COC(=O) c1cc2
+CC(C)(C)OC(=O)N [C@@H](C
+ccccc1 6
+[C@H](CC 1CCCCC1
+C[C@]1 2C
+)c4 cc5
+n4 ccnc4
+c(OC )c2
+[C@H]3 CCC
+CC(C) N(CC
+1CC S(=O)(=O)C1
+)n c12
+c3s ccc3
+[nH] ccc5
+NC(=O)C2 CCCN(C
+N=C (S
+3 N
+(C) CC(=O)N
+OS(=O)(=O) O
+O CCN(C
+C[C@@H]1 O[C@@H](O
+(C (=C(C)
+c4ccc(cc4 )[N+](=O)[O-]
+c4 nc(
+[C@H]2 CO
+[C@@H](C) O
+N=C 1C
+N=C (CC1
+1)c2 ccc(N
+)c2 n1
+)C(=O)N [C@H]1
+F) S(=O)(=O)N
+Clc1ccc2 c(N
+COc1ccccc1 CN
+CCC2 =O
+c4cc( F)ccc4
+c2n oc(
+c2ccc( I
+c1ccccc1)c2 ccccc2
+[C@@H]3 CN(C
+O) c(c1
+cc( s
+c3c(C) onc3
+c2ccc(cc2 )C(=O)O)
+[C@H]2 CCC
+O \N=C\
+2 )CC
+CCCN 1C
+nc(N S(=O)(=O)
+ccc( n1)c2
+c3ccccc3 )c1
+[C@@H](C) O)C(=O)N
+C3 =
+(CC #C
+c4cn (C
+c3ccc( I
+c2ccccc2 OC
+c( I
+CC(C)C[C@H](NC(=O)[C@H](CC CCN)
+C(=O) \C(=C\
+3 )cc1
+(CC)C =CCN
+(CC C1
+s 2)cc1
+nc34 )C2=O
+n5 cn
+c2s c3
+c2 ccncc2
+CC[C@H](C) [C@@H]1NC(=O)
+5CC6CC (CC(C
+ccc(Cl )s
+cc(C) cc2
+[C@H]4 [C@@H]5CC
+[C@H](OC(=O)c4ccccc4) [C@]5(O)C[C@H](OC(=O)
+ccccc2 4)cc1
+c( NC(=O)N
+COc1cccc(c1 )C2
+CO \N=C(/
+)s c2c1
+)[C@@H](C) O)
+ccc( cc
+c5o ccc5
+c4 ccsc4
+c2cccc( OC
+NC(=O)[C@H](CC (=O)N
+3CCN(CC 3)S(=O)(=O)
+cccc(C )c3
+c4ccccc4 F
+c1ccc( Br)cc1
+[C@@H]2 CCCC
+Cl c1ccc(cc1)C
+Cc1ccc( NC(=O)N
+COc1cc2 CCN(C
+3)c4 cn
+3)c4 ccc(F)cc4
+)cc cc4
+)c4 ccncc4
+)C(=O)N C1=O
+CO [C@@H]1C
+)S(=O)(=O) O
+CCCCCCCC \C=C/
+CC1 CCCCC1
+CC(=O)O [C@@H]1
+c2ccccc2 C(=O)N1
+[C@@]3 (CC
+[C@@] (O)
+COC(=O) c1ccc2
+CCN1 C=C(C(=O)O)C(=O)
+5CC6CC(CC(C 6)C
+2 CCCN(CC
+)n cc1
+nc( cc3
+c2 c(CC
+[C@@H](O) [C@@H]1
+OC(C)(C)C =C
+CCC O)
+)C(=O)N[C@@H](CCCN=C(N)N )C(=O)N
+%1 8
+n c[nH]
+cc(O) c1
+c3cccc(c3 )C(F)(F)F)
+c3ccc(F)cc 3)n
+c( n[nH]
+2CCC 3
+1 CCN(C
+cc(ccc2 3)S(=O)(=O)N
+[N+] #
+CN(C c1ccccc1)C(=O)
+CC(C) CN
+CC(=CCC \C(=C\CC
+1CCC(CC 1)N
+)C(=O)N[C@@H](C c2ccccc2)
+[C@@H]6 C
+P(=O) (O
+N CC(=O)N
+Cc1 ccc(Cl)cc1
+[nH] cn
+[O-][N+](=O) c1ccc(N
+[N+]# [C
+FC(F)(F) c1ccc2
+) [C@@H]1
+cc(C) cc(C
+c5ccc6 OCOc6
+CCN)NC(=O)[C@H](CO) NC(=O)[C@H](CCC(=O)N)NC(=O)[C@H](C
+c3ccc(cc3 )C(F)(F)F)
+[C@H](CC CCN
+O[C@H](CO) [C@@H](O
+OC c1
+NC(=O)c2 cccnc2
+CCCN1C(=O)N(CCC )c2
+CC(=O)N[C@@H](CC CCN
+2) \C
+) Oc3
+(C(=O) O
+%3 0
+c2nc( O)
+[C@H]1 2
+ccc3 4)
+CCCCCCCCCCCCCCCC (=O)N
+(O) C(=O)N
+c3cc(ccc2 3)S(=O)(=O)N
+[C@@H](C) O)C
+Cc1 c(Cl)cccc1
+=CC(=O)N C
+(F)(F )C1
+c1ccc( F)cc1
+F C1
+C(= CN(C
+)C(=O)N 2C
+\N=C \C
+)c4 nc5
+nn 2
+cccn c1)c2
+ccccc4 c3
+3 =O
+(CC C4
+sc( NC(=O)
+c2ccc(cc2 )C(=O)
+Br )cc1
+2CCN(CC (=O)N
+(=N N2
+(=N )N
+cn( nn
+c3oc( nn
+O) ccc1
+CC[C@H](C) [C@H](NC(=O)C
+)NC(=O) [C@@H](NC(=O)
+n1 )C(=O)
+c1cn (C
+c(N)n cnc23
+)n c3
+(C)N C(=C(C
+n1)c2 ccc(cc2
+c2cccc3 cccnc2
+c2cc(ccc2 N
+CC c3ccccc3
+s ccc1
+c4ccc(Cl)c(Cl )c4
+\C=C/C \C=C/
+CS(=O)(=O) c1ccc(cc1)c2
+C(F)(F) F)c2
+o n2
+ncn c4
+c4ccccc4 )cc3
+c3ccc(F)c( F)c3
+F c1ccc(cc1)S(=O)(=O)N
+2CCCC 2)c3
+)c4 ccccc24
+)C(=O) c2c1
+C =O)
+3 C2=O)
+s 1)c3
+c3c(Cl)cc cc3Cl
+c( cn2
+c%1 4
+C[C@H]3 N(CC
+c3ccccc3C 2
+\N=C(\ N
+Cc1ccc(cc1 )C(=O)
+CC( NC(=O)C
+2=C (O)
+)c(OC )c2
+%1 6
+COc1ccc( NS(=O)(=O)
+CN(C 1CCCCC1
+CC(=O) OC1
+) \C(=N\
+) NC(=O)N
+c3n c(C)
+c1ccc( O)c(O)
+[C@@H]1CCCN1C(=O) [C@H](CC
+CC1 CCN(CC1)c2
+CC(=O)N C[C@H]1CN(C(=O)O
+cccn c6
+c3ccccc3 S
+[C@@H]1 N
+CC(C)CCC [C@@H](C)
+)c5 ccc6
+)c2 ccc(OC
+(C)(C) CCC
+OCOc4 c3
+C c3ccccc3
+4 )C3
+)n cnc23
+n2 cncn
+cc (CC(=O)N
+C2CCN(CC 2)C(=O)
+2)C3 =N
+cc(C )c(
+c2cc(C) ccc2
+c1nc( nc2
+c1cccc( F)c1
+\C(=N\O)\ N
+)C(=O) C=C
+[nH]c2 C1=O)
+CC(=O) c1cccc(
+3CCOCC 3)
+nn c(C
+n (CC(=O)N
+cc3 4
+[C@@H](C) O)C(=O)N[C@@H](CC
+OC)N (C)
+CC( OC(=O)
+c3ccccc3 n2
+c2 occc2
+[C@H](C)[C@@H](O[C@@H]2O[C@H](C)C[C@@H]( [C@H]2O)N(C)C)
+[C@@H]4[C@@]5(C)CC [C@H](O)C(C)(C)
+O)C (C)(C)
+COc1cc(cc(OC)c1OC )C(=O)
+)c3 ccccc2
+)NC(=O)[C@H](CO) NC(=O)[C@H](C
+o c2
+cccn 4
+c2o c(C
+OC 4=O)
+Nc1ncnc2 c1ncn2
+4)c5 ccc(F)cc5
+)c( I
+c(CC CC
+[C@@H]([C@@H](C) O)C(=O)N
+F) \
+CN(C) c1ccc(cc1)c2
+C(C O)
+C(C 1
+(O)C(=O) OCC2=C1C=C3N(C
+(C)(C) C(=O)O)
+cc(Cl)c(Cl )cc2
+c3n [nH]
+C(=O)O)NC(=O)[C@H](C c1ccccc1)NC(=O)[C@H](Cc2ccc(O)cc2
+(O) CN
+c3ccc(cc3 )C(=O)O)
+c3ccc( Br)cc
+c2c1 )C3
+c2 3)C
+O 3
+NC(=O) [C@@H](NC(=O)[C@H](CC
+ccc [nH]
+c1 ccc(Cl)c(Cl
+CO c4ccccc4
+CN1C =N
+CCCCCCCC C
+c4nccs 4
+c4cc( cc(c4
+c2ccc(cc2 [N+](=O)[O-]
+7 CCOCC
+4CCCO 4
+4CCC (C
+c2 ccnc(
+c1n cccc1
+[C@@H](NC(=O)[C@H](C c3ccccc3)NC(=O)
+2 CCCN(C
+)c2 C
+c2cc(ccc1 2)S(=O)(=O)N
+[nH] c1
+[C@@H](O) CN
+Cc1 cn(
+CCN 3C
+C(=O)O) \
+cn c%3
+ccc( cc1
+c6 ccc(F)cc
+c4ccccc4 )CC3
+[O-][N+](=O) c1cc(ccc1
+[C@@]2(CC [C@]3(C)[C@H](CC
+CN(CC 1
+C(= Cc2
+s 1)c2
+cc( F)c(
+C4 =N
+)c(C )c3
+n2cc( nn2
+c1ccc( NC(=O)C
+Cn1cc(cn 1)c2
+= S
+c3ccccc3 OC
+c3ccc(Cl)cc 3)n
+F)c(F)c( F)c(
+cccc(Cl )c5
+c4ccccc4 )c5ccccc5
+c3cc( O)c(O)
+c( F)c3
+OC 6
+O) C
+ccn (C
+[C@]12C )
+N(C=C(C(=O)O)C(=O) c3cc2
+CN C1
+CC(=O)N c1ccc(cc1)S(=O)(=O)N
+ccc(C(=O)N S(=O)(=O)
+c3cccc(c3 )C(F)(F)F
+O) c4
+Cc1 c[nH]
+c2c[nH] cn2
+c(nc2 ccccc1
+OC (CN1
+c(Cl )c3
+C(=C (C(=O)
+)cc3 C1=O
+)cc (C
+) CCCC
+(C)C 2=O)
+[O-][N+](=O) c1cccc(
+[C@H](O [C@@H]6
+Cc1n oc(
+CC (O)C
+3) S(=O)(=O)C
+)cc 3C
+)c(C) cc1
+c3ccc(O) c(O)c3
+c(cn n2
+OCO c7
+O [C@@H]2C
+N=C 4
+CN [C@@H](C)C(=O)N
+CCOc1 cc(ccc1
+C2 =C(N
+c1n cc2
+[C@]34 C)
+[C@@]6 (C)CC
+S C
+Br )cc2
+c8 ccc(
+c4cc( Br
+S(=O)(=O) [O-])
+O=C(N c1ccccc1
+N c1cc(
+c3ccccc3 C
+c(N)n cnc1
+C23CC4CC(CC(C4)C2 )C3
+.OC(=O)\C=C/ C(=O)O
+)cc1 S(=O)(=O)N
+cc3 C
+c1cnc( nc1
+CS C1=N
+C#N )S(=O)(=O)N
+4CCOCC 4)
+c2c(C) onc2
+[C@H]2 CN(C
+C(=O)O) cc1
+cc(O) cc(O)
+\C=C\2/ SC(=S)N
+[O-] [S+]
+COc1ccc(cc1 )S(=O)(=O)
+= CC1=O
+4CCN(CC 4)c5ccccc5
+4)n 1
+)CC 5
+nn 3
+ncn c3
+c3c( cn
+\C=C \C(=C\
+[ B
+Oc3 cccc4
+O) c5
+@ +]
+cc( cc2
+c2cccc(c2 )C(F)(F)F
+[N+] 1
+CC(C O)
+[S+]([O-] )C
+[C@]2 (C)C
+O [C@@H]7
+NC(=O)[C@H](C c2ccccc2)NC(=O)
+CNC(=N)N )NC(=O)C
+CCC(= C(C
+CC [n+]1
+C1 O
+2)C(=O)N CC
+[C@@] (C2)
+S CC2
+C (S
+=C (C)
+o nc4
+c2ccc3OCC Oc3c2
+[C@H]3 [C@@H]4CC
+[C@H](C)[C@@H](O)[C@]1(C) O
+Oc2 ccc(C
+CCC(CC 1)c2
+3CCC4(CC 3)
+)c(C )c2
+) [C@H]2C
+) OCC
+nc(N )s
+c3ccc( cc1
+n 3)c4ccccc4
+cc(C) ccc3
+c4 ccc(CN
+c1ccc2 ccccc2
+[C@H]3 O
+NC(=O)[C@H](CC CCN)NC(=O)
+CN [C@@H](C
+c3cn c[nH]
+c(cn n1
+O P(=S)(O)
+c(F)cc cc5
+c(C) n2
+O) [N+](=O)[O-]
+C4CCCCC 4
+C4 CCC
+C(= CN
+C [C@@H]3
+7 )
+)cc (Cl
+c2ccccc2 )n
+[C@]6(C) [C@H]5CC
+[C@H]1 C(=O)N
+[C@@H](N) CC(=O)
+OCC (O)C
+N=C (C(=O)N
+CS c1ccccc1
+)CC (O)
+c3o c4ccccc4
+c3cccc4ccccc3 4
+[C@H]5 C(C)(C)
+[C@@]4 (O)
+[C@@H]5CC[C@@]34C) [C@@H]1
+C=C(C)C(=O)NC %2
+)C(F)(F)F) S(=O)(=O)
+s 1)C(=O)N
+c2cc(Br )ccc2
+[C@H]3OC [C@@]3
+[C@@]1 (CC
+NC(=O) c3
+CC1=CC(=O) Oc2cc(
+([O-] )O
+c4ccccc4 )NC(=O)
+c1 F
+c( cc5
+SSC [C@H](NC(=O)[C@H](CC
+S3 (=O)=O)
+NC(=O) CCCC
+CCCN 2C
+C OC(C
+2 C3
+c3cn c(N
+[nH] cc(
+[C@]2(C) [C@H]3CC
+[C@@H]2 CO
+N=C 3C
+Cc1 s
+CC(=O)O [C@H]1C
+)c(Br )c1
+cocc 4
+cc4 ccccc4
+Oc3 cccc(c3
+CS c1
+1 CCN(CC
+c1 2)
+Cc1 nc(s
+C(=O)C=C (Oc2c1
+c(O) cc2
+[N+](=O)[O-] )C(=O)N
+P(=O) ([O-])
+OC(=O) c1cccc(
+3CC c4ccccc4C3
+1CCS(=O)(=O)C1 )C(=O)
+)cc2) \
+4 /
+)C1 (C)C
+c5 6
+c4n 3
+FC (F)(F)C1
+Cc1 c2
+COc1cc2 nc(nc(N
+CCO c1ccc(cc1)N
+cc( NC
+\C=C/ 1\
+O=C(N Cc1ccccc1
+5)C 6
+\N=C/ 1\
+NC(=O)[C@H](C)NC(=O)[C@H](CC CNC(=N)N)NC(=O)[C@H](CC
+CC1 =C(
+CC(=O)N CC
+C(=O)O) C(=O)N
+)c4 ccccn4
+c1 cc(C(=O)N
+c(CC C
+Oc1 cc(O)
+CCOC(=O)C1 =C(N
+C (CN1
+ccs c3
+c3ccccc3 )\
+c3ccccc3 )CC
+c3ccc(cc3 )C(F)(F)F
+c2ccc3 nc(
+[C@@]4 5C
+[C@@H](C)C(=O)N[C@@H](CC CCN
+OC(=O) C(F)(F)F
+[C@@H]2 N
+NC(=O) C(=O)
+)NC1=O) C(=O)N
+(CC CC1
+(CC C#N
+c2c( nc3
+CCCC [C@H](NC(=O)[C@H](C
+CC(C) (N
+C) [N+](=O)[O-]
+c6 c5
+NC(=O)[C@H](CC (C)C)NC(=O)[C@H](CC
+COc1 nc(
+CN1 CCN(CC1
+CC1 (C
+4 )S(=O)(=O)
+3 )C2
+(CCN 3CC
+c6 c7
+OC(=O)C1=C N(C
+OC )C
+O c4ccc(
+Cc1ccc(cc1 C
+Cc1ccc( nc1
+CC1=C N(C
+n2 )c3ccc(cc3
+c2c[nH]c3ccccc2 3
+c( c(C)
+c( F)c1
+[C@H](CN 1C(=O)
+[C@@H]2 [C@H]1
+CS c1nc(
+CCOC(=O) \C=C\
+CC2 (CCN(CC
+CC(=O) OCC1
+=C )
+5CCNCC 5
+(C)C )c5
+OC c1ccccc1)C(=O)N
+C(=O)N (
+nc(N CC
+c6ccc( OC
+c(C)c1 )S(=O)(=O)N
+NC(=O)C (CC
+C[C@@H]1CC[C@H]2 [C@@H](C)
+CC [n+]3
+.OC(=O) C(F)(F)F.
+)C4 =O
+c2cn c(N
+c( no
+NC(=O)CS c2nnc(
+3)c4 ccc(Cl)cc
+2CCOCC 2)
+2 C
+)C(F)(F)F) C(=O)
+(Cl) (Cl)Cl
+c3cn ccn
+c3c[nH] c4ccccc34)
+[C@@]3 6
+CCN1 CCCC
+4C(=O) c5ccccc5
+3CCCC CC3
+cnn(C )c5
+c1c[nH]cn 1)
+Oc1 ccc(CN
+Oc1 n
+CCOC(=O) c1ccc(
+CC(C) CC
+2CCC3(CC 2)C
+)O 1
+c3ccc4OCCOc4 c3
+c2ccc(F)c( c2
+[C@] (O)
+3)C 1
+)c5 ccccc15
+)C(=O)NC (CC
+[C@@](C) (
+[C@@H]7 CCCN
+cc1 2)N
+O) c6
+Clc1 cc(Cl)
+)nc( n2)N
+)C(=O)N[C@@H](C c4ccccc4
+cccc n2
+[C@@]4 (C)C
+N )N
+3)C 4CC4
+)\C 2=O
+ccc(F)cc 2
+CCO c1ccc(N
+C =O
+4CC c5ccccc5
+1 CCN(CC1)C(=O)
+)n c4
+(C)N (C)N(C
+cc(C )c(C)
+c2c[nH]c3ccccc23) NC(=O)[C@H](C
+[C@]2 (CC
+[C@@H]3 C2
+NC(=O)c2 cccs
+Cc1ccc(C )c(
+ccc(Cl)c(Cl )c4
+OC (O
+Cc1 cs
+C[C@@H](O)[C@@H]1 [C@H]2
+CCc1 c[nH]
+)N 4
+cc(F) ccc2
+c2ccccc2 C1
+c2ccccc2 )c(
+O)N(C)C) [C@](C)(O)C[C@@H](C)
+NC(=O)[C@H](CC CNC(=N)N)NC(=O)
+n cc(C#N
+cccc( NC(=O)
+c4ccc(cc4 )C(=O)N
+c2n ccc(
+[C@H]2 3
+[C@@H]1 CN(C
+CC(C)(C) NC(=O)
+CC [C@H](NC(=O)
+Br )c2
+)C(=O)N (CC
+ccn c2
+O[C@H] 5
+O 3)
+COc1 cccc(CC
+nc( nc1
+S(=O)(=O) O
+CC1 =C(N
+C [C@@H](CC
+(CC 3
+cc3 ccccc3
+c1 cc(Cl)ccc1
+c(Cl) ccc(
+OC )cc1
+O=C1 C=C
+CC l
+(C)C)C(=O)N 1CCC[C@H]1
+# C)
+[C@H]3CC [C@@H](C
+NC(=O)C (C)(C)
+8 )c9
+c3ccc(cc3 )C(C)(C)C
+c2c[nH]c3ccccc2 3
+n ccc3
+c1ccccc1 )n2
+NC(=O)[C@H] (N
+Cl c1ccc(cc1)N
+COc1ccc2 cc(ccc2c1
+)cc 7
+(= S
+[C@H]( OC(=O)N
+[C@@]2 3CC
+OC [C@@H](O)
+= CCN(CC
+)N2 CCN(C
+nn 3C
+n2 c3ccccc3
+[nH]c(nc2 C1=O)
+[C@@H](N)CCCN =C(N)N
+[C@@H](N C1=O)
+Cc1cccc( N
+Cc1cc(C) n(
+COc1cccc2 C(=O)c3c(O)
+c4n c5
+c4 n3
+c1n nc(s
+SCC(=O)N C
+O .OC(=O)C(F)(F)F
+Cc1ccccc1 C(=O)N
+CCN1 CCN(CC
+CCN(CC) c1ccc(
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+. F
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+-] (F)(F)
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+CC1(C)CC [C@@]2(CC[C@]3(C)C(=CC
+=C 2C(=O)
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+2C=C (C)C(=O)N
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+\C=C(/C#N)\ C(=O)N
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+[C@@H](C N1
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+CC(=O)N 2
+3CCN(C)CC 3
+1 3
+ccccc5 6
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+o ccc5
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+=CC(=NC %2
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+c1o c(C
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+)C(=O)N[C@@H](CC(=O)O) C(=O)N
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+. OC
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+/C/ 2
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+)\N=C\ 2/
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+[L i
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+C[C@H](CO)N1C[C@H](C) [C@H](CN
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+) OC(=O)C
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+6 =C
+O c4ccc(F)cc4
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+4 C=C
+3CCNCC 3
+1)C(=O)N 2CC
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+[C@H]8 9
+S )cc1
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+4 )C(=O)O)
+)cc 2Cl
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+c(C(=O)N C
+[O-][N+](=O) c1cc(
+[C@H]8 O)
+P(=O) (OCC
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+4CCC 4
+)c(Cl )c4
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+\C=C(/C)\ CCC=C
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+CCN (C)C(=O)
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+C2CCCC 2
+2)N (C
+)NC(=O)C NC(=O)[C@H](C
+(C)(C) O
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+[C@@H](N S(=O)(=O)
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+C[C@@H](CO) N1C[C@@H](C)[C@H](CN
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+4 C(=O)N
+/C/2 =N\
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+c(C) s
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+c1 5
+[Li +].
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+) S
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+(N =C(N
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+) CCN
+(CC l
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+S CCN
+C) OC
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+ccc(Cl)c( c4
+c1nn c2
+[C@@]5 (C
+[C@@]4 (CC
+NC [C@H](O)
+N .
+CC[C@H](C) [C@H](N
+CCC [C@@H](C
+2CCCC(C 2)N
+1CCC[C@H]1 C(=O)N
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+)c4ccccc4 )cc1
+)C(=C1 C5(C)C)C
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+C(=O)N 1CC
+7 CCCCC
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+(O CCC
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+[C@H](C S
+[C@@]6 (C)
+O S(=O)(=O)N
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+CCN CC1
+CCCNC(=O) C1
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+7 CCCC
+c5 cccs
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+[C@H]3 CN
+[C@@H](O) [C@@H](C)
+OCC Oc3
+N C3=O)
+CO [C@@H](
+C(=O)N CCN
+9CC [C@]67
+3 )cc1OC
+(Cl )C(=O)N
+(CO) CC
+c5ccccc5 )CC4
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+[n+] 5
+OC (C)
+O [C@]1
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+Br )cc(
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+oc( cc1
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+[C@@H]3C [C@@H](C
+Cc1ccc2 nc(
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+c3ccccc3 C(=O)N
+c1cn 2
+[C@@H]1 OC(=O)C
+O[C@H](CO)[C@@H](O)[C@H](O) [C@H]6
+Cc2 cn
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+CCOC(=O)C1 CCN(CC1)C(=O)
+)c3ccccc3 Cl
+)C(=C (
+)C (=C)C
+ccc3 n2
+c5ccc( F)c(
+c2nc3ccccc3 [nH]
+c(CC N
+O B(O)
+COc1ccc( O
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+c3c([nH]c4ccccc34) [C@@](C2)(C(=O)OC)c5cc6c(cc5OC)N(C)[C@H]7
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+CCOC(=O)c1 [nH]
+CCCCCCCCCCCC (=O)N
+C23CC4CC(CC(C4)C2 )C3
+Br. Br.
+)c4ccc(cc4 )c5
+c3ccccc3 )C(=O)O
+c3ccc( OCC(=O)N
+c2cccc( Oc3ccccc3
+c%1 7
+[C@]8 (CC)C=CCN
+SCC(=O)N 2
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+n3 nn
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+c3cc(C) cc(C)c3
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+OC c5ccccc5
+Cc1c( s
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+CCOc1 ccc(CN
+C #
+Br )c4
+/C/1 =N/
+)n 2CC
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+)C(=O)N[C@@H](CC(C)C )C(=O)N[C@@H](CC(C)C
+(=C (O)
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+c4cc( ccc3
+c2ccccc2 [N+](=O)[O-]
+c2ccc( c(F)c2
+\C(=N\ N
+O. O.
+C 4C
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+)C(=O)N CCN
+o c3C
+ncn c4[nH]
+c7 ccc8
+c1cn n2
+\ C
+Cc2 c[nH]
+Cc1 nc(cs
+CCCCCCCCCCCCCCCC (=O)
+C(=O)C(= CN(C
+3)c4ccccc4 )cc1
+1)c2 nc3
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+S C(=O)
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+CC COC(=O)
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+)n 1CC
+)c(Cl )c1
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+n1 ccnc1
+c1cccc (C)c1
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+)c4ccc( O)cc4
+)C(F)(F)F )C(F)(F)F
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+c1cn n(
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+[C@@]1 3
+[C@@H]2C SSC
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+OCCN S(=O)(=O)
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+CCC(CC )
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+2)c3 occc3
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+(C 1)C
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+c3c4 CCCC
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+NC(=O) c1cc2
+)c3 occc3
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+c1[nH] cnc1
+] .
+[C@@H]3 CCCC
+[C@@H]2CC [C@H]1
+CCc1 ccc(C
+CCC c3c2
+=C(N )N
+3CC4CC(CC(C4)C2 )C3
+2)c3n c4ccccc4
+(CN C
+cc(C O)
+c4ccccc3 4)cc1
+c3ccc4ccccc4 n3
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+(CN (CC
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+c5ccc(cc5 )c6
+c(OC )cc
+[K+]. [K+].
+[C@]34 C)[C@@H]1CC
+[C@H]2 [C@H]3
+[C@@H]2 [C@H](C
+O 1)C
+C\C(=N/ NC(=O)
+C[C@H](CO) N1C[C@@H](C)[C@H](CN
+CC1(C)CC(=O) C2=C(C
+=CC(=O) OC
+) [C@H]4
+n2 c3
+c3 ccc(Cl)c
+c1ccc(cc1 )C#N
+NC(=O)C(=C1 N
+N=C N(C
+CC(C)C n1
+)cn2C )cc1C(=O)N
+)c(OC )c1
+\C=C\3/ SC(=S)N
+[C@H](N)C [C@H](N)
+N #C
+CCOc1 cc(CN
+5)C 4
+)c4 5
+)c s
+n( c2
+cc(OC)c1 O
+c4 csc(
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+Nc1ncnc2 c1ncn2[C@@H]3O[C@H](C
+NC(=O) c3ccccc3
+CCN(CC) CCCN
+CC(= CCc1
+C4 (CC
+C(=O)N [C@H](C)
+=NCCN 3
+2) C
+)c4 cnn(C
+)c4 ccc(F)cc4
+(CC(=O)O) C(=O)O
+(C)C )c(O)
+[C@H]1 O[C@H](C
+S 4
+O[C@@H]1 [C@@H](C
+Cc2 ccncc2
+C[C@@H]1CC [C@@]2(CC[C@]3(C)C(=CC
+CN1 C(=N)N
+CC 3C
+3)n n2
+2CCCC CC
+(C(=O) OCC
+n3 ccnc3
+n ccc(N
+cccc(Cl )c2
+c4cc(OC)c(OC)c(OC )c4
+c1n [nH]
+CCCN(CC C)
+CC1(C)CCC(=C(C1)c2ccc(Cl)cc2)CN3CCN(CC3)c4ccc(C(=O)NS(=O)(=O) c5ccc(
+CC [C@@H](NC(=O)
+CC [C@@H](C
+C 4CC4
+4CCOCC 4)cc1
+)ccc1 Cl
+)cc(OC )c1
+)c n1
+)C(=O)N C3=O
+)C c4ccccc4
+(CC CCN
+cc4 5
+cc(C(=O) O)
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+CCCN 8
+CC [C@@H](N
+C2 C3
+C(=N c3ccccc3C2=O)
+5 )O
+3)C =C
+)n c5
+)NC(=O)[C@H](CC CNC(=N)N)NC(=O)
+)C(=O) c4ccccc4
+ccnc2 cc(Cl)ccc1
+c4c[nH] c5ccccc45
+c2ccc(cc2 )c3ccc(
+OC1 (C
+CO CCN(C
+CCC [C@H](NC(=O)
+CC(=O) CC
+)n1 C
+)c4ccccc4 Cl
+)c4 ccccc3
+)C(=O)N 1C
+)C(=O) C2
+) Oc4
+(=C (
+c5ccccc4 5)n
+c5cc6 OCOc6
+c3ccc(cc3 )C4
+c 7
+\C(=C/ C=C/
+CC(C)(C) O)
+CC(=O)N1 CCC(CC
+C3=C (O)
+C(=O)NC(=N c%2
+2=O) cc1
+)C(=O) NC(=O)N
+(O)C (C)O
+s c(C(=O)N
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+c4ccc( cn
+c2cn ccc2
+[C@@]1 (C)C
+[C@@H](NC(=O)[C@H](CC (=O)N
+Cc1 cc(OC
+C[C@@H]( NC(=O)N
+CC(C) (O)C
+C NC(=O)[C@H](C
+3CCOCC 3)C(=O)
+2CC OC
+)C(=O)N[C@@H](C S
+(C)C) OC(=O)
+(=O)O) NC(=O)C
+ncccn 4
+n2c( nc3
+n2 )C
+c8 ccc(O)cc
+c2ccccc2 )c3ccc(cc3
+c1cnc[nH] 1
+c(F)cc cc2
+[C@H]1 CN
+[C@@H]1 CO
+O) c7
+Nc2 c3ccccc3nc4
+NC1=NC(=O)N(C =C1)
+Cc1c( o
+COc1cc2 CCN(CC
+3CCCO 3
+)c4 cncn
+)\ CC
+o c2C
+n5cn c6
+ccccc5 6
+cc5 c4
+cc(O) ccc5
+c5cc( ccc5
+c4ccc( NC(=O)C
+c3 cc(OC)c(OC)cc3
+c( c4
+\C=C(/ O)\
+[C@H](C) [C@H]3
+[C@H] (F)
+O=C(C 1
+Cc2 c([nH]
+COc1cc( O)
+COC(=O)N[C@@H](C (C)C)C(=O)N1CCC[C@H]1
+C3 CN(C
+4CC 4)C
+)c1 C#N
+)c( nc2
+)C3CC 3
+( [C@H]2
+ccc4 5
+ccc(Cl)c(Cl )c5
+c4ccc(cc4 )C#N
+c1ccc(cc1 )[N+](=O)[O-]
+[C@H](O) \C=C\
+COc1ncc( cc1
+(N )CC
+c2nn c3
+c2c1 )S(=O)(=O)
+[I-]. [I-].
+[C@@] 9
+O=C1 NC(=N
+O CCCCC
+Nc1nc( nc2
+C[C@H]3N(CC4CC4)CC[C@@]56 [C@@H](Oc1c2
+CCCC CCO
+n2 ncc3
+n1 )c4ccccc4
+cn 1)C
+c3ccccc2 3)C(=O)
+c2cn n(C
+[C@@H]5 [C@@]6(C)CC
+[C@@H]1CCCN1 C(=O)C
+OC (OC
+O)\ C
+O) c3ccccc3
+Cl) CCCl
+COc1ccc2c(c1 )c
+CN(C) c1
+CN(C(=O) C2
+CC3 =N
+CC(C) S
+Br c1ccc(cc1)c2
+2CCCC 2)
+)C#N )cc1
+)C 2C
+(S CC2=O)
+c6 c5
+[C@@]2( OC1
+[ 3
+OC(C S
+OC (C)C
+CNC(=O) c1c(o
+CN(C) c1ccc2
+CN( [C@@H](C
+C2 =C1
+2)C #C
+)cc1 )N
+n2)c3 cccc(
+cn ccc2
+ccc 9
+cc(OC)c(OC )cc1
+cc(Cl )c1
+c(O) c2
+\C )\C
+[C@H]3 CCCC
+OC(=O)C) [C@@H](OC(=O)C)
+4CC4 )c3c2
+cc( n1)c2
+[C@@H](CC C(=O)N
+OCC 4
+OC c5ccccc5
+Fc1 cccc2
+CS c2n
+COc1ccc(cc1)c2 nc3
+2)c3 nn
+cccc (C)c1
+c2nc3ccccc3 s2
+\C=C\C 2
+[P -](F)(F)
+[Na+]. [O-]C(=O)
+[C@H]( [C@H]1
+[C@@H]4 O[C@H](CO)[C@@H](O)
+O 4
+CCCN1C(=O)N(CCC)c2 [nH]c(nc2C1=O)
+CC1CN(CC (C)O
+CC(C) S(=O)(=O)
+2)c3 cccs3
+)c(c2 )[N+](=O)[O-]
+(C)C )CC
+n4 )
+n2 )c5ccccc5
+ccc n1
+c3cccc(c3 )[N+](=O)[O-]
+[B -]
+S(=O)(=O) c3ccccc3
+S c2n
+OP(=O) (N
+OC2 =C
+COc1cc( cc2
+C c4ccccc4
+4 )cc2
+3) Oc4
+2)c3ccccc3 )c4ccccc4
+2)c3 cccnc3
+2)C 1
+)c5 ccc(OC
+cc(Cl)ccc1 3
+c7 c[nH]
+c4ccc(OC )c(OC)c4
+[nH]c( cc1
+OC(=O) CN1
+NC(=N)c1 ccc(C
+Clc1 cccc(CN
+C[C@]1 (C
+4)N 5
+)cccc3 C1=O
+cccc(C )c3
+c2cc( ncn
+c1ccc( \C=C\
+[O-][N+](=O)c1cn 2C[C@@H](C
+[C@@](C) (CCC
+[C@@H]3C [C@@H](O)
+CCCC 3
+C1 (C)C
+2 N
+)n c(N)n
+c6 cc(
+c2ccc(OC c3ccccc3
+c1n c2ccc(cc2
+[C@H](C O)N
+COc1 c(O)
+CCC(=O)N (C
+4CCCCC4 )cc3
+4)C 1
+2)c3 cc(
+)C(=O)C 2
+n1 ccc2
+c1 c(F)cccc1
+[C@H]1 3
+NC(=O)[C@H](C) NC(=O)C
+Cc1 cc(CN
+CS c1nc(N
+CN1CCN(CC1)c2 ccc(N
+CC(=O)N 1C
+C2CCCC 2
+C(=O)N \N=C\
+C(=O)C=C (N
+=C4C [C@@H](O)CC
+=C(C#N )C(=O)N
+2) s1
+)c4 cc(
+)c1 ccccc1
+cc(OC )ccc2
+cc( cc3
+c2ccc(O)cc2 )NC(=O)
+[C@H]1CC[C@@]2(C) [C@@H](CC
+Fc1 cccc(CN
+C (OC
+5 =O
+1CCC(CC 1)
+)c(F)cc3 C1=O
+)C(=O)N [C@@H](C)C(=O)N
+(=O)O) NC(=O)[C@H](CO)NC(=O)
+cn nc1
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+c(C) [nH]
+[C@H]( [C@@H](O)
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+(O)C (C)C
+(CC)C C
+(C)c2 cc1
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+S(=O)(=O) [O-]
+CCO c2ccccc2
+CC1 N(CC
+3CCN(CC 3)C(=O)N
+2)c3 cccnc3
+)C(C)(C)C )C(C)(C)C
+(C)C)N (C)C)N
+c4ccccc4 )C(=O)O
+c2cnc[nH] 2)
+c2 3)cc1
+\C=C\ c3ccccc3
+OC(= C(C#N
+OC (F)F
+FC(F)(F) c1ccc(N
+Cc1 cc(CC
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+C2 4
+(CC (C)C)C
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+[O-][N+](=O)c1cn2C[C@@H](C Oc2
+[C@H]2C (C)(C)
+[C@H](N 1
+[C@@H]4 O
+OC(=O) [C@H]1
+Cc1ccc(cc1 S(=O)(=O)N
+CC(C) S(=O)(=O)N
+CC(C) (O
+C(=O)NC =N
+=CC 3
+3CCC [C@@H]3
+)C(F)(F)F)cc 3
+oc( cc3
+c3ccccc2 3)c4ccccc4
+c3ccccc2 3)C
+c1ccccc1)c2 ccc3
+c1C )c2
+CCN CC3
+2 C(=O)N(C
+1CCOCC 1)N
+) [C@@H]3CC
+%2 7
+s 2)c4
+c5 ccc(C
+c4 cc(Cl)cc(Cl
+c4 c(cn
+c3ccc(Cl)cc 3)cc1
+c(C) cc4
+I ]
+I .
+Cc1cc( F)ccc1
+CC[C@H]1OC(=O)[C@H](C)[C@@H](O[C@H]2C[C@@](C)(OC)[C@@H](O) [C@H](C)O2)[C@H](C)[C@@H](O[C@@H]3O[C@H](C)C[C@@H]([C@H]3
+CCN 3CC
+CC(C) CN(CC
+3) s1
+1CCN(CC1)c2 ccccc2
+)S(=O)(=O) c4ccccc4
+c5ccccc5 c4
+c5 cc(Cl)ccc4
+c2s c3CCCC
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+[C@H](CC CCN)NC(=O)
+[C@@H]1 C(=O)N
+NC #N
+CCC NC(=O)C
+3) O
+)c5 cccnc5
+)c3 ccc(C)cc3
+)C(=O)N[C@@H](C c5ccccc5
+)C(=O) c3ccccc3
+(CC N1
+cc(Cl )c3
+cc(C) nc2
+c2n onc2
+c2cc3 OCOc3cc2
+\C=C\2/ S
+[C@H]6 C
+Oc1 cc(O)c2
+Clc1ccc( c(Cl)c1
+CN1 C=C
+CCc1 o
+CCNCC 4
+CCCCN 2C(=O)
+CC(C) Oc1ccccc1
+2 CC(=O)N
+)c nc2
+(C (F)(F)F)C(F)(F)F
+c7ccccc7 )c8
+c6 7
+c5 c(
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+c4 cnc(
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+c4 ccccc1
+[C@@]2(C) [C@@H]3CC
+OCCOCC OCC
+NC(=O)c2 c(C)
+NC(=O)[C@H](C)NC(=O) [C@@H](N)C
+N(C) S(=O)(=O)C
+CN2 C(=O)N
+CCC 2
+CC(C) (CO)
+7 =C
+5CCC 5
+4CCCCC 4)cc1
+)n4 ccnc4
+cc[n+] ([O-]
+c5ccccn5 )n[nH]
+c4ccc(cc4 )C5
+c4cc( F)c(
+c2cccc(c2 )n3
+c2c3 CCCC
+[C@]2 3CC
+[C@](O)( [C@H](OC(=O)C)
+[C@H]3 CN(C
+CCN(CC)c1 ccc2
+)\CCC [C@]12C
+(O)C =O)
+%2 9
+c4ccc(cc4 )[N+](=O)[O-]
+c3ccc(cc3 )C(=O)O
+c2ccccc2)c3 ccccc13
+c2ccc( OCC(=O)N
+c%1 8
+[C@H](O [C@H]2
+Cc1ccc( S
+COc1 n
+C1 CN(CCO
+5CC6CC(CC(C6)C4)C 5
+3 CCCN3C(=O)
+1) C
+1 )C3
+)n2 n1
+)cc2 )C(=O)O
+)cc(c1 )C(F)(F)F
+ccc1 4
+cc4 OCOc4
+c3ccc4 [nH]n
+c2cc( ccn2
+F )c(OC
+Cl (=O)(=O)=O
+CCN CC2
+CCC NC(=O)c1
+3)n 1
+)C(=O) CO
+c4nc5ccccc5 [nH]4
+c3ccccc3 c2
+c3 N
+c2onc( n2)c3
+c2ccc(cc2 )C(F)(F)F
+c1cc( nc(C)
+[C@@]2 (O)C
+[C@@H](N (C
+CS c1nc2
+COc1cc(ccc1 Nc2n
+CCC \C(=C/
+c4cccc(c4 )C#N
+c4ccc(Cl)cc 4)c5
+[nH] c4
+Cc1ccc( NS(=O)(=O)
+CCC(=O)N [C@@H](C
+CC1 Cc2ccccc2
+5) ccc4
+o c1
+nc(OC )s
+c3cn( nn
+c2c( cccc2
+c(Cl )c1
+\C=C\C(=O) \C=C\
+[C@@H]3 O
+[C@@H]1CC [C@@H](C
+[C@@H]1 [C@@]2
+[C@@H]( [C@H]3
+[18F ]
+S C=C
+C [C@H](CN
+2CCC(CC 2)C
+2)c3 ccccc13
+)\C #N
+c5 ccc(C)cc5
+c3ccccc3 )c1
+[C@H]3 N
+[C@@H]2 CN(CC
+[C@@H](C) OC(=O)
+[C@@H]( NC(=O)[C@H]
+Cc1n ccn1
+Cc1cc( cc2
+CC(=O)N 1CCC
+CC n2
+C2=O) C(=O)
+3CC 3)C
+3)c4 cccnc4
+)C(=O)N[C@@H](CC(=O)N )C(=O)N
+c3cccc (CN
+c2n c3ccc(cc3
+c1)c2 ccc(
+S c4ccccc4
+C(F)(F) F)cc1
+2 CCCN
+) P(=O)(O)O
+nc( nc4
+n nc(N
+c6ccc7 ccccc7
+c(Cl )nc3
+[C@H]2 CCCC
+OC c2ccccc2)
+F c1cccc(c1)C(=O)N
+COc1ccc( \C=C/
+COc1cc(ccc1 Nc2ncc3
+CC(C)N(C (C)C)C(=O)
+=CC(= [N+]
+)n2 cnc3
+)c3ccc(cc3 )C(=O)N
+)c(c1 )S(=O)(=O)N
+)N =C(N
+)C(=O)O) cc2
+)C(=O) [O-]
+(O)C(O) C1O
+c4s ccc4
+c3cn (CC
+c2ccc( cn2
+c1ccc(Cl)cc 1)
+[C@H]5 O
+[3 H]
+N2C(=O)C=C c3cc(ccc23)S(=O)(=O)N
+CN CCN
+=CC (C
+3) C
+2CCOCC 2)C
+2CCCC CC2
+2CCCC 3
+1 4
+%1 0
+oc( SCC(=O)N
+cs c(N)n
+c3 4)N
+[C@H](O) CNC
+[C@H](C)[C@@H](O)[C@]1(C) O)
+Cn1 c(C
+Cl C1
+C5 CCCCC
+=CC=C N(C
+3)c4 ccccn4
+2CC c3c(C
+2)c3ccc(OC )cc3
+2)c3 ccncc3
+ncn c2
+[C@@]2(C) [C@@H](CC
+O 5
+F )C2
+COc1cc(cc(OC)c1O) [C@H]2[C@@H]3[C@H](COC3=O)
+CN(C [C@@H](O
+C1 CC1
+C(=O)N (C)
+3)c4 c[nH]
+)c3 nn
+n2 )c5
+n1 cncn
+c3ccc(C )c(C)c3
+c(OC )cc(
+OC(=O) CCNC(=O)
+O)C(=O)N C1=O
+CO [C@@H]1CC
+= N
+.F [P-](F)(F)
+)C(=O)N[C@@H](CCCNC(=N)N )C(=O)O
+)C(=O)C [n+]2
+n (C)c1
+c2cc( nc(
+c2 C
+S C3
+CCC2 (CC1
+C(CC (=O)O)
+C \C\1=C
+)c3 cc2
+) [C@H]1
+(= C3
+oc( cc4
+n cc(C(=O)
+c3s c2
+c3ccc(N )cc3
+c2ccccc2 )O
+c2ccc( NS(=O)(=O)C
+c1n oc(
+[nH] nc(N
+OC(C)(C)C )C(=O)N
+O=C(C Oc1ccccc1
+N C2=O)
+Cc1 cccs
+C[C@H](N )C
+CN1 C(C
+CC(=O)N [C@H](C
+=CC =C2
+1)c2 ccc(OC
+(C 2CC
+ncc( cn
+cc(C(=O) C(=O)N
+c4cccc( OC
+c3cc( NC(=O)
+c2ccccc2 )N(C
+c( nn2
+\C=N\ NC(=N)N
+[C@H]4 CCC
+[C@H](C (C)C)N(C)C(=O)
+O[C@H](CO)[C@@H](O)[C@H](O) [C@H]2O)
+NC(=O)c2 ccncc2
+Cc1 ccncc1
+CC[C@H]1OC(=O)[C@H](C)C(=O) [C@H](C)[C@@H](O[C@@H]2O[C@H](C)C[C@@H]([C@H]2O)N(C)C)
+CC(C)C [C@@H]1NC(=O)
+C)\ C
+C c3occc3
+)c4ccc5 OCOc5c4
+sc( cc2
+cccc3 ccccc23
+c4ccc(cc3 4)c5
+c4ccc(F)cc 3
+c2cn c3
+\C=N\ NC(=O)C
+O c2cc3
+NC(=O)c2 ccccc2
+Cl )C(=O)O
+CCOC(=O) c1ccc(N
+4CCC 3CC
+3) (C
+2 CCCCC2
+)s 2
+)cc4 )cc1
+c2ccc(I )cc2
+c(C)c(C )c2
+[C@@H]3[C@@H]2CC [C@@]4(C)
+Cc1 ccn2
+CCN1 CCC
+)n c(C)
+(CC#N) (C
+nc(N)n c(
+n3 c4ccccc4
+cc(Cl)cc(Cl )c1
+c5ccccc5 )cc1
+c5[nH] ccc5
+c3nc( oc3C
+c2ccc3ccccc3 n2
+[C@H](C [C@@H]1
+[C@@H]4[C@@]5(C)CC [C@H](OC(=O)
+[1 2
+OC(=O)C1=CN(C 2CC2)c3
+NS(=O)(=O) c1ccccc1
+N(C c3ccccc3)C(=O)
+CCCN (
+CCC(= C)
+5 )C#N
+)ccc2 n1
+)O [C@H]3C
+)NC1=O) C(=O)N
+s 1)C(=O)
+c3ccc(Cl)cc 3)C(=O)N
+[nH]c( nc3
+[n+]([O-] )c3
+O[C@H] 2CC
+O c7
+N3 CCCCC3
+Cc1nc(s c1
+CN1C(=O)N (C)C(=O)
+CCOC(=O) c1ccccc1
+CCCN(C 3)C(=O)
+C5 CCCC
++ 2
+)c(c5 )[N+](=O)[O-])c(O
+)C(=O)N[C@@H](CC(C)C )C(=O)O
+ccc( O)c(
+c8ccc(O)cc 8
+c1cccc2 c(cccc1
+c1c[nH]c2ccccc1 2)N
+[C@@H]3CCCN 3
+NC(=O) [C@@H]2CCCN2C(=O)
+Cl )[N+](=O)[O-]
+COc1ccc(CN 2C(=O)
+COc1ccc( NC(=O)C2
+CO CCOc1
+CCC1 (CC
+CCC c1c(
+C1 )C(=O)N
+3CCN(CC3)c4 cccc(Cl)c4
+2CCN(CC2)c3 cccc(Cl)c3
+2)C (C)C
+)C(=O)N[C@@H](CCC(=O)O) C(=O)N[C@@H](CCC(=O)O)
+n2nn nc2
+n2 nc(
+co cc5
+c7c[nH] c8ccccc78
+c3s c4
+c2cc(O) cc(O)c2
+[C@@H]3 [C@H](
+P(=O) ([O-]
+OC(=O) CN
+O=C(CN1 C(=O)
+F c1cc2
+Cl c1cccc(c1)C(=O)N
+C(N (C(=O)
+3 CCCN
+.[O-] Cl(=O)(=O)=O
+)N 3CC
+c3ccccc3 )n2
+c2c(C 1=O)
+c2CCCC c2sc1
+NC(=O)[C@H](CC CCN)NC(=O)[C@H](CC
+Cl )S(=O)(=O)
+CCc1 nn(C
+CCN(CC1)c2 ccccc2
+CC(C) CC(N
+3 N=C
+)c2 N
+(CC(C)(C) CC
+%2 6
+s2 )c1
+nnnn 4C
+cn c%4
+cccc( c7
+ccc3 c2
+c4ccc5ccccc5 n4
+c4 cocc4
+[C@]6(C)[C@H]5CC [C@]4(C)[C@H]3
+[C@@H](C c2ccccc2)NC(=O)
+OC[C@H]1 OC([C@H](O)
+O)C (O)C
+O \C(=C/
+N3 C(=N
+N c1cnc(cn
+CO CCN1C(=O)
+C(C)C)C (C)C
+=C( OC
+4CCCC CC4
+2CCC3(CC 2)CC
+) (CC
+c3ccc(cc3 )S(=O)(=O)
+c2cn (CC
+c2ccc(cc2)c3 o
+c2ccc(Cl)cc2 )cc1
+[C@H](CC (C)C)C(=O)N
+[C@@]3 (O
+[C@@H]2 3
+S )NC(=O)
+N C2=N
+FC1 (F)
+COc1cc2c( Oc3ccc(
+COc1 cnc(
+CC1 CCN(CC1)S(=O)(=O)
+CC(=O) O)cc1
+C(=O)N[C@@H](CC (C)C)C(=O)N
+C C
+(F)(F)F )c4
+n2c( cc3
+c2onc( c2)C(=O)N
+c1nc( O)
+Cc1cc( nn1
+CN(C \C=C\
+CCOc1cc2 ncnc(N
+CCN1C(=O) c2cccc3
+C(=O)O [C@H]1C
+2 )c5
+)c2 c(
+)C(=O) c4ccccc4
+n (CCN
+cc7 OCOc7
+c2ccc( NC
+c1cn cc(c1)c2
+c1ccc( NS(=O)(=O)
+c(Cl)cc(Cl )cc2
+\C(=N\C #N
+[C@]1 (CC
+S(=O)(=O) c4ccccc4
+Cl. NC(=N)N
+COc1ccc2 [nH]c3
+COc1ccc(cc1 )[C@@H]2
+CN1 C=C(C
+CC1 (O)
+C3=O) CC2
+5 CCN(C
+4CCCC 4)
+3CCC (Cc4ccccc4
+c4c(C) onc4
+c3 cc(CN
+c2ccc(F)cc2 )c(
+[C@@H]2 OC
+S(=O)(=O) C)
+O[C@H] 6
+N1 C(=O)C
+Cn1 cc2
+C(=O) NC(=O)N(C
+3CCC 4
+)C(=O)N[C@@H](C c2ccc(O)cc2
+) [C@H](O)[C@@H](O)[C@@H]1O
+cccn c1
+ccc3 ccccc2
+c5 ccccc25
+c2ccc( SC
+c1ccc(O[C@@H]2CCN(C 2)c3
+[C@H]2[C@@H]3 CCC4
+[C@@]2 (C)CC
+[C@@H]7 CC
+OC )\C
+O c2ccc(N
+NC(=O)c2 ccc(Cl)cc2
+COc1ccccc1 C
+COc1ccc(cc1)S(=O)(=O)N (C
+CCc1n nc(
+CCCCC1 =N
+2CCOCC 2)C(=O)
+2CCC (=CC
+)c4 ccc(Cl)cc
+)c(O) c(Br
+)N(CC Cl)CCCl
+onc( c2
+ncc( nc4c3
+ncc( cc2
+cccn c6
+c5 c(O)
+c4ccc(cc4 )C(=O)O
+c3ccc4c(\C=C\ c5ccccn5)n[nH]
+c2c[nH]c3ccccc2 3)C(=O)N
+c [n+](C
+c %5
+[C@H]( [C@@H](C
+Oc1 c(Br)cc(
+COc1cc2 CC
+CCCC c5
+CC(O) CN
+CC(=O)N C(C
+C \C=C(/C)\
+)cc2 )n1
+)c3 4
+(CC Cl)CCCl
+%1 1C
+nc(C) cc(C)
+cc5 ccccc5
+cc(O) ccc4
+c4ccc(Cl)cc 4)n
+c4 ccc
+c3ccc(O) c(OC)c3
+c3c(C) cccc3
+c2cn( nn2
+c%1 9
+[C@@H](O)C [C@H]3OC[C@@]3(OC(=O)C)[C@H]2
+Oc1ccc( Br)cc1
+NC(=O) c3occc3
+N S(=O)(=O)N
+CS c1nn
+5 )c7
+)C1=O) C(=O)N
+ccc( cc3
+c( F)c(
+[C@H](O) CO
+[C@@H]2 C(=O)N
+Cc1cc(C)c( c(C)c1)S(=O)(=O)N
+COc1cc( \C=C\C(=O)N
+COC(=O)C1=C (C)
+CC(=O)O [C@@H]1C
+C3CCCC 3
+3 \C
+2)S(=O)(=O) C
+)cc( c2
+c9 ccc(O)cc
+c2ccccc2 C(=O)
+[C@@H](CC (C)C)C(=O)N
+NC(=O)[C@H](CC C(=O)O)NC(=O)[C@H](CC
+COc1ccc( cn1)c2
+CN(C) C(=O)C
+=C 6
+4CCCN 4C(=O)
+)c3ccc( Br)cc3
+)c2 nc(
+ccn c5
+N=C (CC
+F C(
+CN(C) CCCNC(=O)
+2CCOCC 2)N
+1CC CO
+)c5 cncn
+)S(=O)(=O)N 3CCOCC3
+)NC(=O) OC(C)(C)C
+)NC(=O) CC
+( [C@H]1
+n2 nc(cc2
+n c5ccccc5
+c5ccccc5 )c4
+c4ccccc4 C
+c2 ccc
+c(N)n c(N)n
+[C@]4 (O)
+[C@H](CC N
+[C@@H]2 [C@H]3
+O CCC1
+Cn1nc( cc1
+Cn1 c2ccccc2
+CO P(=O)(OC
+CN C(=N
+CCO c1ccc(cc1)C(=O)N
+CC1 CCCC(C)
+CC(C) [C@H](
+CC(C) CC(=O)N
+C2 (C)C
+)n4 cn
+)NC(=O)CNC(=O)[C@H](CC(=O)O)NC(=O) [C@@H](NC(=O)[C@H](Cc3ccccc3)NC(=O)
+)C(=O)N[C@@H](CC(=O)N )C(=O)N[C@@H](C
+)C(=O) O)C(=O)N
+c2ccccc2 )n1
+c1nc(cs 1)c2
+\C=C/C\C=C/C \C=C/C\C=C/
+[C@]5(C)[C@H]4CC [C@]23C
+[C@@H]5CC[C@@]34C)[C@@H]2 [C@H]1C
+OC(C(O) C(=O)O)C(=O)O
+O)C (O)
+Nc1nc( O)c2
+NC(=O)CC l
+F )c(C(=O)N
+Cc1cccc(c1 )n2
+CCOC(=O) \C(=C\
+6 )C(=O)N
+)c3ccc( O)cc3
+nc(C) nc2
+n4 C
+cccs 4
+cc(CC (=O)O)
+c4ccc(Cl)cc4 Cl
+c3ccc(cc3 )C(F)(F)F
+c3ccc(OC )c(OC)c3
+NC(=O)c1 nn(
+Cl. COc1cc2
+Cl c1cn
+COc1ccc2 nc(
+CN(C) C
+CC(C) CCN1
+3 CCCN(CC
+2 CCNCC
+ccc(C)cc 1
+cc( ncn
+cc (CCC
+c4ccccc4 F)
+c3ccc(cc2 3)S(=O)(=O)N
+c3cc( nn3
+c1ncn 2C
+[C@H](CC CNC(=N)N
+[C@@H](O) [C@H]3
+Cc1cc(O) cc(C)c1
+CCCC CCN1
+=C(C 1
+)C(=O)N[C@@H](CCCNC(=N)N )C(=O)N[C@@H](CCCCN
+nn 1
+nc(C) nc3
+nc( c1
+n2 n1
+cccn 2
+c2ccccc2 O
+c2ccc(C )c(C)c2
+\N=C\ 3/
+S(=O)(=O) c2ccc(C)cc2
+OC [C@H](O)[C@H](O)
+NC(=O)C SC
+N(C=C(C(=O)O)C(=O)c3cc2 F)C
+F)c(F) c1
+Cl. CCN(CC)CC
+CS(=O)(=O)N 1
+CN1 C(=O)O
+CCn1 c(SCC(=O)N
+CCN(C) CC1
+C=C (NC(=O)
+C4 =
+2)c3 nc4
+2 +]
+c3ccc(cc3 )S(=O)(=O)C
+c1ccc( OC2
+[C@H]3 OCC
+[C@@H]4CC [C@H](CC
+[C@@H](CC CNC(=N)N
+Oc3cc(O) cc(O)c3
+O[C@H]([C@H](O) [C@@H]1O)n2cnc3
+O=C(N \N=C\
+CCn1 cc(
+CC(=O) c1ccc(cc1)N
+C(F)(F) F)cc
+C#N )cc1
+2CC3 CN(CC
+2)c3ccc(F)cc 3
+)N CC
+)C2 =CC(=O)
+)C(= S)
+(C)CC 2
+n3 c(
+c4ccc(N )nc4
+c3c(F)cccc3 F)
+c2nc( nc3ccccc2
+c(OC )
+[C@]3(C)C (=
+[C@@] (O)(CC
+[C@@H]1 OC(=O)C
+C[C@]12CC[C@H]3[C@@H](CC c4cc(O)ccc34)
+COC(=O) CNC(=O)
+)O 2
+(F)(F) CC
+(C c2occc2
+cc5 OCOc5
+[C@H]2 C1
+[C@@H]2CC [C@@H](CC
+O=C1N c2ccccc2
+COc1ccc(cc1 O)
+CO \N=C\
+C5 =O
+C(=C (N)
+C 4CC
+Br )c1
+=C 1
+5 =N
+)c4ccccc4 n2
+)c2 ccccc12
+(F)(F) F)c(
+c3cccc( NC(=O)
+c3 c2c1
+c2ccc(F)cc 2)C(=O)
+c1ccc( CC
+[C@H]1 C
+[C@@H](C S
+S CCNC(=O)
+OC(=O) C(=O)
+COc1ccccc1 NC(=O)N
+CCOC(=O)c1 c2
+CCC c1nc2
+CC(N (C
+CC(=O) c1c(C)
+CC(=O) C1=C
+C )c1
+3)S(=O)(=O) C
+)cc1 OC
+)C(C)C)C (C)C
+# CCN
+nn1 )
+ccc(Cl)c(Cl )c5
+c4c[nH] cn
+c3ccc(Cl )nc3
+c3 C=C
+[C@H]4C (C)(C)
+[C@@H]3C =C
+S C(=N)N
+C[C@@H](CO) N1C[C@@H](C)
+CN1 CCN(CC1)S(=O)(=O)
+CCN(CC)CC .
+CCCCN (C(=O)
+4 )c2c1
+cn 2
+cc(OC)c(OC)c(OC )c2
+c6ccccc6 )
+c2cn cc(c2
+\C=C\ 4/
+[NH 2+]
+Oc3ccc( NS(=O)(=O)
+O[C@@H](C NCC
+OP(=O) (S)OC
+OCOc5 c4
+O CCCCCCCC
+Cn1c( cc2
+Clc1ccccc1 C(=O)N
+COC(=O)C1=C(C [C@@H]2CC[C@H]1
+CCn1 c(nc2
+CCCCN 3C(=O)
+2)C (C)(C)C
+)C(=O) c3
+nc(N CCOCCOCCOCC
+nc(NCCOCCOCCOCC #C
+n3 n2
+n( c4
+cc(C )c4
+c4CCCC c4c3
+c3ccc(OC )cc
+c2c(C) cc(C)cc2
+c(Cl )cc2
+[C@H]3 CCN(C
+[C@H]2 C(=O)NC
+[C@@]23 OO
+[C@@H]1 C
+OCOc3 c2
+OCCN 2C(=O)
+COc1cccc( N
+CN1C2 CCC1
+CC1(C) [C@@H]2CC
+2) (
+)n3 ccnc3
+(NC(=O)N C3=O)
+c2 ccc(Cl)c
+c1ccccc1 N
+[C@@H](N 1
+C[C@@]1 2CC
+COc1cccc( NC(=O)N
+CN1 C(=O)N(CC
+C1 CCN(CC1)c2
+2)c3 cccs
+2 )c4ccccc4
+)cc (C)
+)C(=O)N CC(=O)N[C@@H](CC
+(C)C )c(
+(=C(N )O
+nn1 C
+ccc(Cl )cc1
+c5 cccs5
+c3ccccc3 )NC(=O)[C@H](C
+[n+] 2CC
+[I-]. C[n+]1
+[C@]1 (O)
+[C@](C) (N
+[C@@H] (F)
+C[C@H]1 COCCN1
+COc1ccc2 C(=O)
+C2 )
+C) [N+](=O)[O-]
+4 )[N+](=O)[O-]
+)c5 c4
+)c3 ccccn3
+)N =C(
+(C 3=O)
+nc( nn1
+c4cn c(N
+c2sc3CCCC c3c2
+c2nc3ccccc3 [nH]2
+c2CCCC c2nc3
+[C@@H]8 CCCN8
+[C@@H]4[C@@]5(C)CC [C@H](O[C@@H]6
+OCCOc4 c3
+O 1)c3
+CCCN CCCCN
+CCCN 2CC
+C(=O)c4 c(O)
+=C2 N
+2 =C(N
+)N(C (C(=O)N
+)C(=O)N 1
+cc(C =O)
+c4ccccc3 4)C
+c2cc( O)c3
+c(C) nc4
+[C@]4(C) [C@H]3C
+[C@H]( [C@H](C
+O [C@@H](CN
+FC(F)(F) Oc1ccc(
+CC1=CC(=O) Oc2
+CC(C) [C@@H]1CC
+C1 )c2
+C (C)C)
+1 =N
+)cc3 Cl
+)N6CCOCC 6
+)C(=O)NC c4ccccc4
+nnnc1 C(N
+ncn c(
+n5 )c6
+cnccn 3
+cc(C) o
+c2c1 O
+c(C) n(
+c( cccc4
+[C@@H]4 [C@H]5
+[C@@H]( [C@H](O)
+SC 4=N
+OCCCC CO
+OCC 4CC4
+NC(=O)N (CC(=O)N
+Cl. CC
+CNC(=O)c1c(o c2cc(
+= O.
+3=C (CC
+2CC n3
+n4cncn4)c5ccc(F)cc5 F
+ccccc3 c2
+c5ccc( N
+c4cn ccn
+c2ccc( F)c(Cl
+c1ccc2 OCCO
+c1 2)c4
+\C(=C/1\C(C(=C(C) N=C1C
+[C@]2 4
+P(=O)(O)O) P(=O)(O)O
+OC [C@@H](C
+I )c2
+CCCN(CCC )c1
+CCC 6
+CC2 (CC
+C3=C 2C(=O)
+2 S(=O)(=O)
+1 2
+)c3ccc(cc3 )C(F)(F)F
+(F) F)C
+(Cc3ccccc3 )C(=O)NC2=O
+(C)C )S(=O)(=O)
+coc( n1)c2
+cn n(
+cccc(Cl )c2
+c3nc4ccccc4 s
+c3ccc(O) c(O)c3
+c2c( c1
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+[P @
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+[C@@H]1C c2ccccc2
+NC(=O)C )
+NC(=O) C(=C
+F) S(=O)(=O)
+CC(C)(C) NCC(O)
+C=C(C)C(=O)NC %3
+C3 4CC5CC(CC(C5)C3)C4
+)c( nc2c1
+)C 4CCCCC4
+)C (CC(=O)O)
+(C)C)NC(=O)[C@H](CC (C)C)NC(=O)[C@H](CC
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+ccc2 F
+c4ccc(cc4 )C(C)(C)C
+c2cccc 3C(=O)
+c(Cl )c(C
+[nH]c(cc2 c1)C(=O)N
+[nH] c12
+OCC n1
+O=C(N c1ccccc1)N
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+3) CC1
+)C(=O)N[C@@H](CC(C)C )C(=O)N
+cnc( nc3
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+C[C@@H](CO)N1C[C@H](C) [C@H](CN
+COc1ccc(cc1 )C(C
+CCOC(=O) C(C)
+CCCC Nc1
+3) ccc12
+2)c3ccc( NC(=O)N
+)nnc1 SCC(=O)N
+)c n5
+(C) O)
+nc3 4
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+ccccc %11
+cccc 3
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+c3ccc(cc3 c2
+c3c([nH] c4ccccc34
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+c2ccc(cc2 )
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+c1cc2 [nH]nc(
+[C@H](O) C3
+[C@@H](OC(=O) \C=C\
+O=C(C OC(=O)
+Nc2n c3ccccc3
+NC(=O)C NC(=O)C
+CN1 C2CCC
+CN (N
+CCCCCCCC CCN
+CCCC c1nc2
+2CCC (CN
+1 )C(=O)O
+)cc1 )C
+)c4ccc(OC )cc4
+)C (NC(=O)
+(C) (O)
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+cc 9
+c(C)c (C(=O)
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+CCc1 nn
+CCN(Cc2 3)C(=O)
+CC1(C) CCC
+CC(C)(C) C
+C(=O)OC (C)(C)C
+C(=CC(=O)O c2c1
+3CCN(C)CC 3
+)cc cc12
+)c3 nc(
+(CC C)
+n1 )N2
+c3ccc(Br )cc2
+c2nn c(C
+c2ccc(O) c(O)c2
+[C@H](CCC(=O)O) NC(=O)c2cc(
+FC(F)(F) c1ccc(O
+CN c1
+4CCCCC 4)c5
+3CCCCC3 )cc1
+2 [C@H](C
+)c4n ccs
+)C(=O)N[C@@H](C c2ccccc2)C(=O)N
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+[C@@H]([C@@H](C) O)C(=O)N[C@@H](C
+[C@@H](O)[C@@H](O [C@@H]1
+O c1ccc(cc1)C(=O)
+N C(=
+C[N+]1 (C)
+CN(C) c1n
+CCCC (N
+CC1 CCC
+CC(C)(C) N1
+C(=O)C (C)(C)C
+3)c4 ccncc4
+)C(= C)
+(C) cc1
+%2 1
+cc3 c2
+c3ccc( NS(=O)(=O)C
+c2c[nH] nc2
+c1ccccc1 CO
+c1cc( F)
+O c6ccccc6
+C1CN =C(N
+4 CCN
+3) OC(=O)
+2) CC1
+)s 2)cc1
+)C (OC
+c4ccc(O)c(O) c4
+c2ccc( S
+c( ccc1
+\C(=C \C=C\
+[2H] )(
+OC(=O) c1ccc(N
+Cc1cc(C) nc(
+Cc1 c(CC
+C[C@@H]1 CN(CC
+C1 CCC
+3) \N
+2CCCCC2 )C(=O)N
+cnc( nc2
+c4 cc(OC)c(OC)cc4
+c3ccc(Cl)cc 3)C(=O)
+c2cccc(c2 )C(=O)O)
+c2cc( sc2
+c1ccc(OC c2ccccc2)cc1
+c1ccc(O)c(O) c1
+[C@@H]7 C
+S \C(=C\
+Cc1cc( o
+C[C@@H]1CC [C@H](CC
+CSCC [C@H](NC(=O)[C@H](C
+CCc1ncnc( c2ccc(C(=O)N
+C(=O)OC )/
+C(=O)NC (C)(C)C
+C(=O)N 3CC
+)S(=O)(=O)N C
+(F)(F )C(F)(F)F
+(C)C )cc1
+c2c1 [nH]
+[C@H]2 SCC
+N 4C(=O)
+Cc1cccc( n1)c2
+Cc1 c(C)c2
+CN (c2ccccc2
+CCN c1n
+=NN(C (C
+4CCCCC 4)C(=O)
+3CC 3)C(=O)
+)c4ccc(cc4 )C#N
+)C=C 1
+occc 6
+csc( NC(=O)
+ccn 3C
+cc(Cl )c(OC
+c5cn c[nH]
+c3ccc(F)cc3 F
+c1ncnc2 c1ncn2
+[C@@H]2C [C@@H](O)
+[C@@H](C OP(=O)(O)
+N2C(=O) C=C(C
+COc1cc(ccc1 OC
+CCN C[C@H](O)
+CCCCCCCC CCCCN
+CCC(= CC1
+2C [C@@H]3
+2)c3 ccccn3
+. OS(=O)(=O)O
+)c4ccc( O)cc4
+nc3 c2
+cc(Br )ccc4
+c3cn cn3C
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+c1ccc(cc1 [N+](=O)[O-]
+c1c( nc2
+OC(C O)
+Nc1n ccn2
+Cc1n c(CN
+COc1cccc(c1 )n2
+CC(C) c1nc2
+CC(C) c1ccc(C
+@ @+]
+)n n4
+(=C(N2C1=O) C(=O)[O-]
+nc1 C
+cocc 5
+ccs c5
+c3cccc (C)
+c2nc( oc2C
+c2 3)C(=O)
+[C@]4(C)C 3CC
+[C@]1(C) CCC
+[C@H](O)[C@@H](C OP(=O)(O)
+[C@H](O S(=O)(=O)O)
+[C@H](CC =C
+[C@H](C) C(=O)
+[C@@]7 (C)CC
+[C@@](C)(C=C )[C@@H](O)
+[C@@H](C)C(=O)N[C@@H](CC CN=C(N)N
+OC[C@H]1O[C@H]([C@H](O) [C@@H]1O)n2cnc3
+OCC(=O)N C
+COc1ccc2 CN(C
+CCN(CC) CCN1
+CCCCN CCCCN
+CCCC C(=O)
+CC(=O)N1 N=C(CC1
+C2CC 2
+3)C (C)C
+)n (C)c3
+)\ C(=O)O
+(F)(F) F)C
+(C)C1=O) N
+(C)C(=O) C
+(C)C 3=O)
+s 5
+cn c8
+cc(O) cc3
+c3n nc(s
+c3cccc( Oc4ccccc4
+c2cc( OCC
+c1ccc(cc1)c2 cn
+c1cc( nc(
+c(c1 )C(=O)N
+c(Cl )nc2
+CN[C@@H](C)C(=O)N [C@H]1
+CCc1 cccc(CC
+CC(C)C[C@H](NC(=O)[C@H](CCCCN)NC(=O)[C@H](C)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC (C)C)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](Cc1ccccc1)NC(=O)[C@H](Cc2ccc(O)cc2
+CC(C)C[C@@H]1 NC(=O)[C@H](CC
+CC(=CCC \C(=C\
+Br )c(
+3CCN(C c4ccccc4)CC3
+)n (C)c2
+)c(C) n1
+cnc2 c(N
+cn 6
+c3ccccc3 OC
+c2n cn3
+c2ccn 3
+[C@H](C) [C@H]1O)
+O C(=O)N(C
+N(C c3ccccc3
+Cc1c( nn(
+COc1ccc(cc1)c2 cc(
+CCN(CC 1)C(=O)N
+)cc(OC )c3
+)c1 N
+)C(F)(F)F)cc 2
+n 3CC
+c5 c(C)
+c3ccc(C )c(c3
+c3 ccnc(N
+c1cc( nc2
+[C@H](OC(=O)C )C(C)(C)
+[C@@H](NC(=O)OC )C(C)C
+C[C@H](O) CC
+COc1ccc2nc3 cc(Cl)ccc3
+CCN c1nc(N
+=C 5
+4CCCCC 4)n
+(CC(=O)N CC
+n3 cc(nn
+c5c[nH] cn5
+c2ccc( Oc3ccc(cc3
+c(C )c1
+[C@H]3C =C
+[2H])( [2H]
+OC(=O) c3ccccc3
+Cn1 c(S
+Cl )[N+](=O)[O-]
+Cc1ccc( F)cc1
+COc1ccc(cc1)N 2CCN(CC
+CC1 =C(C(=O)N
+Br )C
+5CCC[C@H]5 C(=O)N
+3CCCCC3 )N
+1)c2 c[nH]
+s 1)N
+cccnc1 3
+c9ccc(O)cc 9
+c(Cl )c(N
+[C@H]9 O)
+[C@H]1 C3=CC
+[C@@H](C c1ccccc1)NC(=O)
+Oc3ccc(cc3 )[C@H](C)
+OC(=O) \C=C/
+O) [N+](=O)[O-]
+O \N=C/
+Nc1n [nH]
+Cc1cc(ccn1)c2 n[nH]
+COc1cc(OC )c(cc1
+C3 CCCCCC
+5)N6 CCOCC
+3CCOCC 3)N
+2CCC3(CC 2)N
+)O [C@H](
+)C(F)(F)F)cc 2
+c4ccccc4 )n3
+c4cccc(c4 )[N+](=O)[O-]
+c4CCCC c4
+c3 cco
+c2nc3ccccc3 n2C
+c2ccc(Oc3ccccc3 )cc2
+c1cccc n1
+[n+]([O-] )c2
+[C@@H](C)O)C(=O)N[C@@H](CC CCN
+O c2ccc(Cl)cc2
+Nc1 cc2
+NC(=O) [C@@H]1
+NC(=N) c1ccc(
+N1 )C(=O)N
+Cc1ccc(cc1 )
+CS C1
+COc1cc2nc(nc(N )c2cc1OC)N
+CCC1 =O)
+CC(C)(C) OC
+4 )C(=O)N
+)ncnc2 cc1
+)c3ccc4 OCOc4c3
+)C(=O) CNC(=O)
+ccccc1 2)C(=O)N
+cccc(Cl )c1
+c3onc( c3
+c3cc(F)cc( F)c3
+c2cc(ccc2 N1C(=O)
+c2c(O) c1
+c2c(C) nc3
+c1ccc2 nc(
+c(N )c(
+[C@@H](O)[C@H](C c2ccccc2)NC(=O)
+S(=O)(=O)C 3
+S CCC
+OP(=O)(O)OP(=O)(O) OP(=O)(O)O)
+OC(=O)C [C@H](NC(=O)
+COc1cc(ccc1 O)C2
+CC 2CC
+C(=O)O) cc2
+)c3ccccc3 )c4ccccc4
+)c2 ccc(Cl)cc2
+)[C@@H](O) [C@@H]1O
+)C(=O) O1
+ccccc2 6
+cc( \C=C\
+[C@]2 (O)C
+[C@H]4 CCCC
+[C@H](CO) [C@@H](O)
+[C@@H]2 3)C
+[C@@H]( [C@@H](O)
+O [C@@]1
+Cl C1=C
+CCn1 c(CN
+CCC2 =
+CC(=O) C
+3C=CC(=O)N C3=O)
+2)c3 ncccn
+nc(C )c(
+ccc(C (=O)
+c6ccc(F)cc 6
+c4ccc(cc4 )C(=O)O)
+c4ccc( Br)cc
+c3n c(C)cc(C)
+c2ccccc2) (c3ccccc3)c4ccccc4
+c2ccccc2 CC
+c1cccc(c1 )C(=O)
+\C(=C\ 2/
+[C@@H]2CSSC [C@@H]3
+[C@@H](C)C(=O)N[C@@H](CC C(=O)N
+[C@@H]( NC(=O)[C@H](C)
+OC(F)(F) F)c3
+OC )c1
+Cn1cc(NC(=O) c2cc(NC(=O)c3cc(
+C[C@@H](O)[C@@H]1[C@H]2[C@@H](C)C (=C(N2C1=O)C(=O)O)
+CN1CCN(CC1 )C2=N
+CCCN =C(N)N
+C) ccc1
+4 )NC(=O)
+)ncn c12
+)C(=O)NC (C)(C)C
+ncc(C#N )c(N
+ccc( \C=C\
+cc 5CC
+c4ccc(F)cc 4)c5
+c3n oc(
+c3n c[nH]
+c2cn [nH]c2
+c2ccc(N )cc2
+\C=N \C
+[C@@H]2C [C@H]1
+NC(=O) c2cccc(
+CC2 (CCCCC
+C(=O)N 2CCC
+C#N )c3
+3CCC[C@H]3 C(=O)N[C@@H](C
+3 )C1=O
+)c5cn 6
+(CC 1=O)
+(C 1)
+nnnn 3C
+ccs c2
+cc1 O
+c5ccc(OC )cc5
+c3 5
+c2 3)c5
+[nH]c( cc2c1
+[C@@H](O)[C@@H]1 O)c2
+Oc3 ccccc13
+OC(=O)C )C
+O=C1 NC
+N(CC (=O)
+COc1ccc( OCC(=O)N
+CO \N=C(\
+CCCC 1)N
+CCC c4
+CC1 CCCN(C
+CC(=O)N \N=C\
+=CC(=N C2=O)N
+=C (C)N
+2 CC(C)
+)c2 cn1
+cccnc4 5
+cc(O) ccc3
+c4 cc(C
+Oc1 cccc(CN
+O [C@@H]%10
+CO CCCNC(=O)
+8CCC [C@H]8
+(N C(=S)N
+(=C )CC
+ccccc2 5)cc1
+cc( F)c3
+c3 nc(O)
+c2ccc(cc1 2)S(=O)(=O)N
+c2ccc( F)c3
+c2 n1C
+\ O
+[O-][N+](=O) c1cccc(c1)c2
+[C@@]2(OC1 )O[C@H]3C
+[C@@H]3 N
+[C@@H]3 CO
+[C@@H](O)[C@@H](O [C@@H]%2
+S(=O)(=O) C3
+NC (C)C
+F) n(
+Cc1 ccnc(N
+COc1ccc(cc1OC )C2=N
+COC1 =C(O
+CCOC(=O)C1=C N(C
+CC c1ccc(cc1)c2
+C )C2
+7CCOCC 7
+3(CC N(C
+)C #C
+(F)(F) F)c2
+o 1
+cc(OC )ccc4
+c3ccc(Cl)cc3 )c2
+[nH] c(C
+[C@H](C 1)N
+O [C@@H]3C
+NC(=O)c2cc( o
+NC(=O)CN(CCNC(=O)CN(CC NC(=O)CN(CCNC(=O)CN(CC
+F) n
+CCOC(=O) [C@H]1
+CCNC(=O) C1
+C(=N 1)
+=CC(=N C2=O)
+)c n4
+)NC(=O)CNC(=O)[C@H](CC CCN)NC(=O)[C@H](CO)NC(=O)[C@H](CCC(=O)N)NC(=O)[C@H](C
+(C)C3 =CC
+n(CC 2
+c2nn (
+OC1 (CC
+O=C1 NC(=O)\C(=C\
+O c1ccc(cc1)C(=O)N
+NC(=O)[C@H](CC (=O)O)NC(=O)[C@H](CC
+CCC(= CC1)c2
+CC(C)(C)OC(=O)N 1
+CC C(=O)OC
+=C 3C
+2CCCCC2 )C(=O)
+2CCCC(C 2)c3
+1=C (O)
+)c5 ccncc5
+(C (C(=O)N
+oc( nn2)c3
+c4cn (
+c2n cc3
+[nH] cc(CC
+[C@] (C2)
+[C@H](OC(=O)C) [C@H](OC(=O)C)
+[C@@H]( [C@H]2
+O[C@H](CN )[C@@H](O)[C@H](O)
+OCC1 OC(C
+NC(=O)CC S(=O)(=O)
+C[C@H](N c1n
+C[C@@H](O) [C@H](NC(=O)
+COc1cccc( NC(=O)
+COc1ccc2 ccccc2c1
+COc1ccc(CN 2C
+CCCN (CC(=O)N
+CC(C)(C) c1ccc(C
+C3CC4CC (CC2C
+C [C@H]2
+3CCCC3 =O
+)n 3CC
+)NC(=O) OC
+nn[nH] n5
+csc( n2)N
+cc(Br )ccc2
+c2cn ccn2
+c2ccc(F)cc2 )cc1
+[C@H](OC) [C@@H](OC(=O)N
+O CCCCCC
+NC(=O)[C@H](CCC(=O)N )NC(=O)
+N (CN
+COc1 c(N
+CN1C(=O)C=C(N =C1
+CCOC(=O) C1=N
+CCOC(=O) C(C
+CCCN 4
+CCCC Oc1ccc(
+CC(=O) OC[C@H]1O
+CC (=N)N
+=C(N)N (Cc4ccccc4
+)c4ccc( Br)cc4
+)c(OC )c(OC
+)C(=O)N[C@@H](C c3ccc(O)cc3
+(O) CCC
+c5 ccn
+c3occc3 )C(=O)
+c3 4)
+c2ccccc2 )n3
+c2cccc3 ns
+c2ccc(F)cc 2)C(=O)N
+c1cccc(c1 )n2
+[C@H]3 [C@@H](C
+[C@@H]2 [C@H](CC
+N=C /
+Cc1 c(CN
+CN [C@@H](C)C(=O)N[C@@H](C
+CCOC(=O) [C@@H]1
+CCCCN1C(=O) NC(=O)C(=C1N
+CCC (C)C(=O)
+=C(N 2
+4CCCC 4)n
+2CCC(CC2)N 3CC
+. OC(C(O)C(=O)O)C(=O)O
+)c3 ccc(Cl)cc3
+(CC) (CC
+c3nc4ccccc4 [nH]3
+c2ccccc2 C
+c1ccc(Cl)cc 1)c2
+c1cc(ccc1 N
+c(O) c1)c2
+c(O [C@@H]3C
+[C@H](NC(=O)[C@H](CC CNC(=N)N)NC(=O)
+Oc3 ccc(Cl)cc
+Cc2 c(CN
+C[C@H](NC(=O)C) c1ccc(O[C@@H]2CCN(C2)c3
+CCOC(=O) c1cccc(
+CC(C) c1cc2
+C(=O)c3ccccc3 C(=O)
+=CC=C N3
+=C 1C
+6 )O
+2CCCC 2)C(=O)N
+2)c3 ccncc3
+)cc1 N
+(= CC1=O)
+s c5
+nc(C )c
+n2 3)c4
+cc(OC)c(OC )cc5
+c3ccc( c(F)c3
+c1c(C) cccc1
+[C@H](O) [C@H](CC
+[C@@H]1C [C@@H](C
+S CCC(=O)N
+OCC NC(=O)
+Cc1cccc2 nc([nH]
+CCC[C@]2(C) [C@@H]1CC
+CCCN (C(=O)
+CCCC NC(=O)N
+C4 CCN(C
+3)N 4CC
+2)S(=O)(=O) c3ccccc3
+1CCC(CC 1)C(=O)N
+) [C@H](N
+[C@H](C O)C
+[C@@H]3 CN
+OC1 (C)C
+O [N+](=O)[O-]
+CC(= C1
+4C 3
+4=O) N
+4)C (C)(C)C
+2CCOCC 2)cc1
+2)C(=O) C
+2)C (C)(C)C
+1) \C
+)nc1 N
+)c( n3
+(C)C 1=O
+cocc 3
+ccc(OC )cc
+c1ccc2OCO c2c1
+c1ccc(cc1)C 2CC
+c1c[nH]c2ccccc12) NC(=O)[C@H](C
+\C=N\ NC(=O)N
+[C@@H]7 O[C@H](CO)[C@@H](O)[C@H](O)[C@H]7
+OC1 =C(O
+NCCCC [C@H](NC(=O)
+NC(=O) c3nc(
+CN1 CCCC1
+CN 5
+5CCCCC5 )cc4
+4C 5
+2CCN(CC2)C(=O) c3occc3
+2CCC(CC 2)C(=O)
+)c(C )c(C)
+(C)C)C (C)C)
+cccc1 3
+c3ccc4 [nH]
+c2o c3ccccc3c2
+c2nc( nc(
+c2cc(OC )cc(OC
+c2c[nH]cn 2)C(=O)N
+c2 cc(CN
+\C(=N/ NC(=S)N
+[C@H](O) [C@@H](NC(=O)C
+[ A
+Oc3 c(C)c(O)
+OC(=O) C2
+NC(=O)C n1
+NC(=O) c1ccc(N
+Cc1cc(C) nc(N
+COc1cc(OC)cc( c1)c2
+CN1 CCC[C@H]1
+CN(C 1)C(=O)
+C2 =CC(=O)N
+C(=O)N S(=O)(=O)C
+4 )CC2
+3CCN(CC 3)c4ccc(cc4
+)N (C(=O)
+)CC 5
+cc(C) nc3
+c2cccc(C )c2
+c2cccc (C)
+c1ccccc1)NC(=O)[C@H](C c2ccccc2)NC(=O)
+[C@]3 (
+[C@H](CC CN=C(N)N
+N(C=C(C(=O)O)C(=O) c4cc3
+F) N3C
+Cc1ccc(cc1)n2 nc(cc2
+Cc1 nc(C)
+CCCC (CCC
+CC1(C) S
+C3 (C)C
+3) s2
+)c2cc1 OC
+)\C )\C
+)N =C2
+(=S) S
+n2cn nc2
+c5 cc(F)ccc5
+c4nc5ccccc5 s4
+\C(=C \C=C
+[C@H]2CC [C@@H](C
+OC [C@H](O)
+O=C(N CCN1
+O P
+N =CC(=O)N
+Cn1cnc(c1 )S(=O)(=O)N
+COc1ccc(cc1)c2 o
+CC1=C(C (
+4)c5 cccc(
+4)C 5CC5
+2C=CC(=O)N C2=O)
+)C C
+(C)(C) O
+oc( nc2
+cc(Cl)cc(Cl )c2
+c4c1 )c5cn6
+c3cc( F)ccc2
+[C@]3 (CCN
+[C@H](C) C(=O)N
+[C@@H](O)[C@H](O) [C@@H](O)
+Oc1 ccc(Cl)cc
+OC1 =C(C(=O)N
+NC(=O)C2 CC
+COc1ccc(CN 2CC
+CCCCN(CCCC )C(=O)
+CC NC(=O)c2
+C=C (C)
+4) O
++2 ].
+)n 4
+)ccc1 OC
+)c4ccc5 ccccc5
+) CC(=O)
+n2cn c(C)c2
+ccc( O)c3
+c5ccc(Br)cc 5
+c2c(C) nn(
+[C@H]( [C@H](
+NC(=O)[C@H](CC (C)C)NC(=O)
+NC(=O)C2CCCN(C 2)S(=O)(=O)
+N \C(=N/O)\
+Cl C
+C[C@H](CO) N1C[C@@H](C)
+CO CCN(CC
+CCOC(=O)c1 sc(
+CCN C3
+CCC1 =C(C
+2CCN(CC2)c3 ccccn3
+1)c2 nc(
+)C (C)C)C(=O)N
+#N )N
+nn n1
+cn (CC
+c4ccc(Cl)cc 2
+c2c(C1) sc(
+[C@H]5 O
+[C@@H](O)[C@H](C c1ccccc1)NC(=O)
+[C@@H](N)CC C(=O)O)
+[C@@H]( OC3
+[C@@H]( NC(=O)[C@H](NC(=O)
+OC (CC1
+CN1 N=C(C
+CN(C 2)C(=O)
+CCOc1 cccc2
+CC(C)C c1ccc(cc1)C
+)c2 C#N
+)C(=O)C n
+) [C@@H]4
+nc1 O
+cc(O) c(O)
+cc n2)c3
+c4 nc(cs
+c3cn 4
+c3ccc(OC )cc3)
+c1ccccc1 )O
+[C@H]2 [C@@H]3C
+[C@H](C c5ccccc5
+OC(=O) CCC(=O)N
+O) C(=O)O
+C\C(=N/ NC(=S)N
+CO c5ccccc5
+CCc1 onc(
+CC(O) CO
+C3CC CO
+4C [C@@H]5C
+4)C (C)(C)C
+3CCN(C c4ccccc4)CC
+2)c3ccc( O)cc3
+1)c2 ccc(C
+)c4ccccc4 F
+)c4 ccc(F)cc
+)c3ccc( nc3
+) [C@H](C)C
+s 3)c4
+c(C(=O) C
+[NH 3
+[C@H]%1 3
+[C@@H]3 C)
+NC(=N)NCCC [C@H](NC(=O)
+NC =O)
+F)c 6
+Cl.Cl. Cl.
+C[C@@]1 (O)
+CN(CC =C
+CN(C c1occc1
+CC(C)C 1=C
+= O.OC(=O)C(F)(F)F
+5CCOCC 5)c6
+2CCC 2
+)c3 cc(ccc3
+)c1 4
+)C(= C)C
+) [C@@H](OC(=O)
+c6ccccc4 6
+c4ccc(cc4 )c5ccccc5
+c3 ccnc(
+c1 C(=O)
+[nH] c2c1
+S(=O)(=O) NC(=O)
+Oc3 cn
+OC(= CC(=O)c1
+N1 C(=N
+COc1cccc(c1 )N2
+CN(C1CCCCC1 )C(=O)
+CC(=O)N (CC(=O)N
+C(N 1CC
+C(=O)N)NC(=O) [C@@H]3CCCN3C(=O)
+.OC(=O)C(F)(F)F .OC(=O)C(F)(F)F.OC(=O)C(F)(F)F
+)c(c3 )C(F)(F)F
+c4ccn c5cc(Cl)ccc4
+c3c2 )C(=O)N
+c3c(F)cc cc3Cl
+c2ccc(cc2 )S(=O)(=O)N
+\C=C\C(=O) OCC(=O)N
+O) c(Cl
+Cc1 cccc(Cl)c1
+CS(=O)(=O)N 1CC
+CCN1 C(=N
+CCN )NC(=O)C
+C1 =CC(=O)
+C(C 1=O)
+C \C=C(\C)/
+4)N 5C
+4 )c2
+3C(=O) c4ccccc4C3=O)
+3)C #C
+)n cnc3
+)CC c12
+%2 4
+s c1C(=O)N
+cc nc(N)n
+c5ccccc5 )c6ccccc6
+c3 c(Cl
+c2n cnc(
+c(Cl)cccc4 Cl
+[C@@H]4CCCN 4
+Oc1ccc2 C[C@H]3N(CC4CC4)CC[C@@]56[C@@H](Oc1c2
+N =N\
+COc1cccc(c1 )C(=O)
+COc1ccc( NC(=O)c2
+C(=N 1)N
+=N OC(=O)N
+2)c3ccc4 OCOc4c3
+)c4 ccc(C)cc4
+)c(Cl )cc2
+)C(=O)N[C@@H]([C@@H](C) O)C(=O)N[C@@H](C
+(CC(=O) OC
+( OC1
+cc(o c4c1)c5cn6
+c4 c(N
+c3cccc(c3 )C(=O)O)
+c3 cc(CC
+\C=C(/C)\ CCC=C(C)C
+[n+]2 [O-]
+[C@H]4CC [C@@H](C
+Nc1n cnc(N
+NC(=O)CN (
+CCc4 nn(C
+CC[C@H](C) [C@H]1
+CCCCCCCC CCC(=O)N
+CCCC [C@H](NC(=O)C
+= CC1
+4CCCC 4)cc1
+4C =CC=CC
+3CCCC 3)cc1
+3CC 3)n
+)[N+](=O)[O-] )[N+](=O)[O-]
+)NC(=O) CN
+( c3ccc(F)cc3)c4
+nc( cc(
+n( c2C
+c6 cnc[nH]
+c3 C(=O)
+c2cn cnc2
+c2ccccc1 2)c3ccccc3
+c1cccc n1)c2
+c1ccc( O)cc1
+\C=C\C 1=C
+\C=C/2\ C(=O)N
+NC(=S)N \N=C\
+N(C)S(=O)(=O)C )c(cc1
+Cc1n cccc1
+CC(= CC
+C(=O)O) C
+=CC(= CC1=O)
+1CCCCC1 )N
+)N C3=O)
+)C3 CCCCC3
+) o
+s 2)
+nn2 )N
+n5 nccn5
+ccc( nc2
+c3ccc(F)c( c3
+c2ccccc2 N=C1
+c2c(OC )c1
+c2 nc(N)n
+c(OC )n
+[nH]c( \C=C\2/C(=O)N
+[C@]1 3C
+[C@H]2O[C@H]([C@H](O) [C@@H]2O)N
+[C@@H]1 [C@H](O)
+[C@@H](O) [C@H](N)C[C@H](N)
+P(=O)(O) [O-]
+OC2 =C(
+NC(=O) S
+N Cc2
+CN(C)C(=O) c1cc2
+CCCCC S
+CC [C@H](C)C
+C3CCN(CC 3)C(=O)
+7CCOCC 7
+5CC 6
+5 CCCN
+3CCN(CC3)c4 ccccn4
+)cc 3Cl
+)c4 ccc(Cl)cc4
+(C)C )\
+ccc3 [nH]
+c[nH] c4ccccc34
+c3nc4ccccc4 [nH]
+c1c( [nH]
+c(C) n3
+[S+](C) [O-]
+[C@H]3 [C@@H]4
+[C@H]2 N
+[C@@H]5CC[C@@]4(C)[C@]3(C)CC 2
+[C@@H]1 CCC2=O
+Cn1 c(N
+Cc1n nc(
+Cc1 cnc2
+COc1ccc2 c(O[C@@H]3C
+CN(CC O)
+CCC c4c3
+=C3 )C2
+= CCCCC
+/C/2=C \
+)c3n cccc3
+)c3ccccc3 F
+)c(C) cc2
+(C)C )C(=O)NC
+(=N N
+no [n+]([O-]
+ncn 3
+n2 )C3CC
+n1 ccn
+ccc2 c1
+c8ccccc8 )c9
+c5cccc( OC
+c2 csc(N
+c2 C(=O)
+c1ccc2c(c1 )nc(
+c1cc(nc(C) n1)c2ccc3
+c1cc( ccn1)c2
+[C@@H]( NC(=O)[C@H](C)NC(=O)[C@H](C
+Nc1n cc(cn
+Cc1nc( ccc1
+C(= C4
+)nc( nc2
+(C) O
+%4 0
+nn (C)c2
+nc1 4
+n2 c(S)nnc2
+n1 )c3ccccc3
+cc(Cl)c(Cl )cc3
+c8 ccccc8
+c5ccccc4 5)c6
+c4cccc(C )c4
+\N=C (\C
+\C=C/ C=C/
+[O-][N+](=O)c1cn2C[C@@H](COc2 n1
+[C@]3 (NC(=O)NC3=O)
+[C@]12C )O
+[C@H]3O) c2n1
+[C@H]1 OC(=O)C
+[C@H](O)[C@@H](NC(=O) c6ccccc6)c7ccccc7
+[C@@H]1 [C@@]23OO
+O[C@@H]4 O
+O)c(OC )c1
+N =CC(=C
+N 4CC
+C\C(=N/ NC(=O)c1
+COc1ccccc1 N2CCN(C
+CCN CCN
+CCC(C) (C)c2
+1 )c3ccccc3
+)c4 ccccc2
+)\N=C (/
+)[C@@H](O) [C@H]3O
+)C(=O)N CC(=O)N
+(c1ccccc1)c2 ccccc2
+(C)c3 ccccc2
+c6 cccnc6
+c4ccccc4 )NC(=O)[C@H](C
+c4ccccc4 )C(=O)N
+c3n cccn
+c1ccc(NC(=O)N 2C
+[C@H](O [C@@H]8
+S c5ccccc5
+N c1ccc(cc1)c2
+COc1ccc(C NC(=O)
+CC(C) N1C(=O)
+= CCCC
+5)c6ccc( cc6
+4CCN(CC 4)c5ccccc5
+3)c4 ccc(F)cc
+2)c3ccccc3 Cl
+)cc3 F)ccnc2cc1OCCCN
+(C)(C)C )
+n4 c3
+cn( nc2
+cc1 Cl
+c3ccc(cc3 )S(=O)(=O)N
+c2ccc(cc2)c3 nc4
+c2ccc( \C=C\
+c2c[nH]cn 2
+c2 ccc1
+c1oc(cc1 )c2
+c1ncn c2c1
+c1ccc2 c(
+c1ccc(O)cc1 )NC(=O)[C@H](C
+NC(=O)CSc2nn c(C
+CCc1cccc(CC )c1
+CC(N 1C(=O)
+C(=O) CO
+C \C(=C\
+=[N+]=[N-] )
+3)c4 ccc(Cl)cc4
+3 )cc1Cl
+)c( [nH]2)c4
+)c( NC(=O)N
+)S(=O)(=O) c3ccc(C)cc3
+)C(=O)N[C@@H](CCCCN )C(=O)N[C@@H](C
+)C(=O)N CCC
+no nc2
+cccnc2 3
+cc( c5
+c3cc( n[nH]
+c1ccc( cn1)c2
+c1ccc( OCC(=O)N
+c1cc( O)c2
+[C@H]1 CO
+[C@@H]2 OC(=O)
+Cn1 c(CN
+Cc1cc(C )c(C
+CCC1 CCCCN1
+CC(C)(C) c1ccc(cc1)S(=O)(=O)N
+CC(=O)N 3
+CC(=O) OCC(=O)
+C1 )c3
+C(=O)O)NC(=O) [C@@H](NC(=O)[C@H](CC
+4CCC [C@@H]4
+3CCC(CC3)N C
+)cc2 )CC1
+)c1 2
+)C (F)F
+) \C(=C/
+nn (C)
+cc(CN (C
+cc(C(=O)c2cn cc(NC(=O)C
+c4cs c(N)n
+c3c(C) nn(C)c3
+c3c( o
+c2n nc(N
+c2n ccnc2
+c2ccccc2 )C1
+[C@@H](C c1ccccc1)C(=O)N
+O[C@H](CO)[C@@H](O)[C@H](O) [C@H]4
+O) O)
+CNC(=O)c1c(oc2cc( N(C)S(=O)(=O)C)c(cc1
+CCO c1ccc(cc1)n2
+CCCC N
+CCC2(CC1 )CC
+CC1 CC2
+CC l
+4)c5 cccnc5
+3CCC4(CC3) OCCO
+3)C (C)(C)C
+)c4ccc( F)c(
+)c3ccc( NS(=O)(=O)C
+)\N=C /
+( OCCO
+no c(C
+c7 n
+c6n c7ccccc7
+c3n c(C)c
+c2nn 3
+c2ccc(Cl)cc2Cl )c1
+c2ccc(Cl)cc 2)C(=O)N
+[C@H]2CN(CC c3c([nH]c4ccccc34)[C@@](C2)(C(=O)OC)c5cc6c(cc5OC)N(C)[C@H]7
+[C@H](O) [C@@H](NC(=O)OC(C)(C)C
+[C@H](N(C 3)C(=O)
+[C@@H]2 C
+OCCOCCOCCOCC OCCOCCOCCOCC
+COc1ccc(OC)c( c1)c2
+COc1ccc(OC )c2
+CN1CCN(CC1)c2 ccc3
+CCCCCCCC C(=O)N
+CCCC1 =N
+CC(C) [C@H](NC(=O)[C@H](C
+C(=O) OCC(=O)
+3)c4ccccc4 n2
+3) N
+)c3 c(
+nc3ccccc2 3
+cccc 4
+ccc(Cl)cc 8
+c7ccccc6 7
+c6 [nH]
+c5 ccncc5
+c3ccccc3)c4 ccccc24
+[nH] 1)C(=O)N
+[C@](O)([C@H](OC(=O)C) [C@]8(CC)C=CCN9CC[C@]67[C@H]89
+[C@H]7 O)
+[C@H]1O[C@H]([C@H](O) [C@@H]1O)n2cnc3
+[C@H](N C
+O)C (C)C
+N )c2
+Cc2 cccnc2
+COc1ccc(C(=O) \C=C\
+CCCCCCCC n1
+C4 =C(C
+4Cc5ccccc5 C4
+4CCCC 4=O)
+4)c5 ccc(OC
+3CCC4(CC 3)C
+3)c4ccc(cc4 )C#N
+1=O) c23
+)n c(C
+)cc2 OC
+)c3ccccc3 )cc1
+(O) C(=O)C=C
+% 6
+nn (CC
+cc2 C(=O)N
+c5ccc(F)c( F)c5
+c4ccc5OCCO c5c4
+c3ccc(F)cc 3)cc1
+c2cccc( Br)c2
+c2c( s
+\N=C/ 2\
+[C@H]4 O[C@H](C
+[C@H]2 [C@H](N
+[C@H](O 1)
+[C@H](C OC
+[C@@H]2 3)
+S(=O)(=O)N (C
+S c2nnc(
+NC(=O)C )C(=O)O
+N 4CCOCC4
+I )cc(
+Cc1nn c2
+CS c1nnc(
+COC(=O) \C(=C\
+COC(=O) C
+C2 CCC(C
+3CCCC 3)c4
+)c1C#C c4ccc(N)nc4
+)C =N
+(C) (C)c3
+cccc n
+c4ccc( OCC
+c4cc(OC)c(OC)c(OC )c4
+c3ccccc3 )C2
+c3ccccc2 3)n
+c2cccs 2)
+c2ccc(F)cc 2)C
+c1 ccsc1
+c(CC C(=O)O)
+\C(=C\C )\
+[O-][N+](=O) c1ccc(cc1
+[C@H]1 CCC2
+[C@@H]1 C)
+S C=C(N
+OS(=O)(=O)O) [C@@H](O
+OP(=O) (OC
+OC(= CC2=O)
+O)C(=O)N 1
+O)C #N
+C[C@@H](O) [C@H]1
+CS c2nnc(
+CN \C(=N/
+CCCC CC(=O)
+CC(C) C(=O)N1
+CC c1cn
+C(=O)O) C(=O)
+=C(N 3
+2)c3 cccc4
+)C3 (O)
+)C#N )
+(CCCC 4
+(C)C )C(=O)O)
+nc2 C
+cs c1
+c3 n2C
+c2ncc( s
+c2cccc (C(=O)N
+c1cccc(c1)c2 ccc3
+[C@]2 (O)C(=O)
+[C@H](O) [C@@H]1O
+[C@H](CC(=O)N C
+O)N 2C
+Nc2ncn c3cc(
+NC(=O)N c2ccc(Oc3
+CC(C) CC(NC(=O)
+C2(CC CC2
+=CC(=O) O
+4 )c6
+2CCN(CC2)c3 n
+2C=C (F)C(=O)N
+)c2 )CC1
+)NC(=O)[C@H](CC (=O)N
+)NC(=O)[C@H] 3CCCN3C(=O)
+) NC(=O)[C@H](C)NC(=O)[C@H](CC
+(O C(=O)N
+oc(cc2 )C(=O)N
+nc3 C
+cccnc4 5
+c4 onc(
+c4 c2[nH]
+[C@](C) (CO)
+[C@@H](N)CCCN=C(N)N )C(=O)N
+CO[C@H]1C [C@H](C)CC
+CC1 CN(CCN1
+3CCCCC3 )
+)n c(NC
+)c4 cccnc4
+)N )N
+n2 c(C)
+cccc 8
+cc(O) cc2
+c6 ccccc16
+c4ccccc2 4)
+c3cccnc3 )C(=O)
+c3c2 )C
+c2ccc3 c(\C=C\
+c2ccc(cn 2)C(=O)N
+c2c(C) [nH]
+c1ccccc1 )N2
+c1cccc(c1 )C2
+[C@H](OC)[C@@H](OC(=O)N )\C(=C\
+[C@@]5(CC [C@@]34C
+O[C@H](CO)[C@@H](O)[C@H](O) [C@H]8
+OC(=O)C )c(OC(=O)C
+OC Oc1
+CN(C c1ccccc1
+CC(=O)N CC(=O)N
+CC 5
+5CCNCC 5
+5)n 6
+3C c4ccccc4C3
+2CC3CC(CC(C3)C1 )C2
+onc( c2)C(=O)N
+ncccc4 c3
+nc1 C(=O)N
+c5ccccc5 )cc4
+c3ccccc3 OC2=O)
+c3ccccc3 )ccc2
+c2nn [nH]
+c1nc( nc2ccccc1
+c1ccc(O c2ccccc2)cc1
+c(O) nc2
+[C@@H]4 N(C
+[C@@H]( [C@@H]1
+OC(CO)C (O)C(O)
+O c1ccc(cc1)N
+Nc1nccn2 c(nc(
+N(C(=O) C=C
+N(C c4ccccc4
+Cc1 c(C)
+COc1cccc(C (=O)N
+COc1cc(OC)cc(c1 )N(CC
+CC(C) Oc1ccc(
+C2 =CC
+3)c4n cccc4
+3 )c5ccccc5
+)n c4ccccc14
+)O C(=O)N
+)NC(=O) [C@@H](CC
+(c3ccccc3 )
+ccc4 ccccc3
+ccc3 ccccc23
+cc4 cc(O
+c3nccs 3
+c2ccc3 N(CC
+c2ccc(cc2 c1
+[C@H](C O
+[C@@H]1 OC(=O)
+P(=O)(OC [C@H]1O
+Oc1 cn
+OC [C@H]1C
+C[N+] (C)(CC
+C[C@]1 2CCC3C
+COc1ccc(C [C@H]2
+COc1cc(cc(OC)c1OC) [C@H]2[C@@H]3[C@H](COC3=O)
+CCOc1ccc( CCNC(=O)
+CCCN(CC 1CC1
+C(=O)NC (C)(C
+=C(N)N(Cc4ccccc4 )C(=O)NC3=O
+5C c6ccccc6
+4CCC (C)CC
+)c5 ccccc25
+)c(Cl )cc1C(=O)N
+)C(=O)N[C@H](CC CNC(=N)N
+c4cnn(C )c4
+c4ccccc3 4)N
+c3 ccnc(c3
+c2n c[nH]
+c2cc(F)c( F)cc2
+c2 cc(OC)c(OC)cc2
+[C@H]2 OC(=O)
+[C@H]2 CN
+[C@H]2 C1=O)
+Nc1 cccc2
+NS(=O)(=O) c1ccc(cc1)n2
+NC(=O)C 4CC4
+N(C(=O) C
+N n1
+Cc1ccccc1 )N
+COc1ccccc1 C(=O)
+COC(=O) c1ccc(N
+CC1=C (CC
+C3=O) N
+C3 =CC(=O)N
+C(=O) c13
+Br)cc 5
+6)C 5
+4CCC 5
+3CCOCC 3)c1
+3)c4 cc(
+2CCCC(C 2)C(=O)
+)cc1 NC(=O)
+)c1 2)
+)c(c2 )C(F)(F)F
+)c(F )c4
+)C2=C(N)N (Cc3ccccc3)C(=O)NC2=O
+)C c2ccccc2
+( SC
+n ccc2
+c6 ccc(C)cc
+c4cn c(N)nc4
+c3cccc n3)c4
+c3c(Cl)cc cc3Cl
+c3 cc(OC)c(OC
+c3 c12
+c2ccc(nc2 )N
+c1 ccc(Cl)c(
+\C=C\ CC
+[C@]12CC [C@H]([C@@H]1
+[C@H]4 [C@@H]5
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+= CCC2
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+C(=O) c3c2
+2CCC (C)CC
+.OC(=O) CC(O)
+)n3 C
+)cc( N
+)c4cnn(C )c4
+)C(C) (O)
+) [C@@H]1C
+(C)N(C)N(C 2=O)
+(=O)N 1C
+n2 )c4ccccc4
+cccc (C)
+c5ccccc5 )C(=O)
+c5cc( ccc4
+c4ccc(Br )cc3
+c3ccc(O)cc3 )NC(=O)
+c3ccc( SC
+c1n c(C
+c1cccc(c1 )[N+](=O)[O-]
+c1ccc2ccccc2 c1
+\C(=N\ O)\C
+[C@]3 5
+[C@]1 4
+[C@@]24OO 5
+[C@@H]5CCCN 5
+[C@@H](C c2ccccc2)
+Oc1ccc( \C=C\
+F) ncn
+Cc1cc2 c(cc1
+COC (C)(C)
+CN(C) CCCN1
+CC1 CN
+C=C 2
+3CCCC 3)C(=O)
+)c4cc5 OCOc5
+)c3 cccc4
+(C S
+( c1ccccc1)
+c6 cc5
+c5ccc(cc5 )C#N
+c4ccccc4 )NC(=O)C)C(C)C)C(C)C)C(=O)N
+c4cccc5 ccccc4
+c2n n1
+c2ccc(F)c(c2 )
+c2c(C1) s
+c(C) nn(
+[C@H]2C [C@@H]2
+[C@H](C) OC(=O)
+[C@@]5 (C)C
+[C@@H]2C c3ccccc3
+OP(=O) ([O-])O
+O c2cccc(
+O 2)cc1
+Clc1ccc( OCC(=O)N
+Cl.Cl. Cl.Cl.
+Cc1 cc(O)c2
+COC(=O) [C@H](C)
+CN 3CCN(C
+CCN)NC(=O)[C@H](CC CCN)NC(=O)[C@H](CC
+CCN(CC) CCCC(C)
+CC(C)c1 cc(C
+C2CC 3CCC(C
+C(=O)c3ccccc3 C(=O)c12
+= NC(=O)
+4CCCC4 =O
+3 C(=N
+2)c3ccc( Br)cc3
+)CC3 )c2c1
+)C(=O)N[C@@H](CC(C)C)C(=O)N [C@@H](C)C(=O)N
+)C(=O)N[C@@H](C c3ccccc3)
+)C(=O) CCC(=O)N
+( [C@@H](C
+( OC1=O)
+cc( ccn
+c3n ccn3C
+c3ccc4 c(c3
+c1oc( nc1
+c1cc( nc2ccccc1
+c(nc2 3)N
+\C=C\C 2=N
+[O-][N+](=O) c1cccc(c1)C(=O)N
+[C@]12CCC (C)(C)C
+[C@](C) (CC(=O)O)
+[C@H]5 O[C@H](C
+[C@H]4 O
+[C@H]2 [C@H]1
+[C@H]1CC[C@H]2[C@@H]3 CCC4
+[C@@H]1 C[C@H]1
+OC [C@@H]2
+N2C(=N c3ccccc3C2=O)
+N2 C1=O)
+COc1ccc(cc1)c2 ccc(cc2
+CC(=O)O) C(=O)O
+3CCC(CC 3)C(=O)
+2CCC [C@H]2C
+2)c3 ccc(C)cc3
+)c4 ccsc4
+)c3 cccnc3
+c4ccccc4 )s
+c3ccc(cc3 )C(C)(C)C
+c3 c(Cl)c
+c1ccc(N C(=S)N
+c1)c2 ccc3
+c1 cc(CC
+c1 OC
+[C@@H](CC C
+Oc2 nc(N
+O)c(O) c3
+NC(=N)N CCC[C@@H]1
+Cc1cc2 ccccc2
+C[N+] (C)(C)C
+C[N+] (C)
+CSCC[C@H](NC(=O) [C@@H](CC
+COc1cccc2C(=O)c3c(O)c4C[C@](O)(C [C@H](O[C@H]5C
+CNC [C@@H](C
+CN(CC (=O)O)
+CCN(CC1)C (
+CCCC N1C
+CC1 CCCN(C1)C(=O)
+CC(=O)O .
+)C(=O) c2cc1OC
+(c5ccccc5 )c6ccccc6
+nn n2
+nc(s c2c1
+c4n cc(
+c4cc( ccc2
+c1cn ccc1
+c1)c2 ccc(cc2
+\C(=C(/ C#N)\
+[nH]c( cc3
+[N+](C)(C) CC
+[C@]13 C)
+[C@@H]7 O
+[C@@H](O)C[C@@H](O) CC(=O)O)
+Oc3 cc(ccc23)S(=O)(=O)N
+NC(=O) c2ccc(cc2
+Cc1 ccn
+CS(=O)(=O)N c1ccc2
+COC(=O) CS
+CO CCN(C(=O)
+CN(C) c1ccc(cc1)C2
+CC(=O)N c1cc(
+C3(CC CC3
+3)c4cccc( F)c4
+)c1 N
+(CC=C )CC
+(CC CO
+(C)C) [C@@H](O)
+%2 2
+s ccc4
+n2cc( nc2
+ccc2 c3ccccc3
+cc(OC )c(
+c5ccc(cc5 )[N+](=O)[O-]
+c4ccc(Cl)cc 4Cl
+c4ccc(Cl)cc 4)cc1
+c3ccc(cc3 C2=O)
+c3ccc(Oc4ccccc4 )cc3
+c2 3)c1
+c1cn (CC(=O)N
+\N=C\ c2ccccc2
+Oc1 cc(F)ccc1
+OS(=O)(=O)[O-]) [C@@H](O
+O=S1 (=O)
+Cn1 ccc(
+C[C@H](N 1CC
+CS CCC(NC(=O)
+CN( [C@H](C)C
+CCc1 cccc(C)c1
+CCOC(=O) C(CC
+CC (CN1
+C2 =C(C(=O)N
+)cc1 C(F)(F)F
+)C #Cc2
+(F) [C@@H](O)C
+(C1 )CC(C
+n1 cc(C
+cn 5
+c7 cnc[nH]
+c5cc6 ccccc6
+c4cn cn
+c3ccc( F)c
+c2ccccc2 )S(=O)(=O)
+c2ccc(NC(=O) \C=C\
+c1ccccc1)c2 ccc(cc2
+[C@H](C)[C@H]1O) \C)\C
+[C@@H]4CC [C@H]3C
+[C@@H](C) C(=O)OC
+O=C1 C2
+NC(=O)c2 oc(cc2
+FC(F)(F) c1ccc(cc1)c2
+Cn1cn nc1
+C[C@H]1 CCC
+COC 2
+CCc1 nc(N)nc(N)c1
+CCC (CO)
+CC(C) c1nc(
+C(=O)NC c3ccccc3
+2)N 3CCOCC3
+2)C(=O) OC
+)c(O) n1
+)C(=O)N[C@@H](CCCCN )C(=O)O
+) NC(=O)[C@H](C)
+nnc( n1)c2
+nc(C )c(C)
+cccnc3 4
+cccc1 c23
+c7cnc[nH] 7
+c5ccc(cc5 )C(F)(F)F
+c3ccc4 c(C
+c2 ccc[nH]
+c(O) cc3
+[C@H]2C [C@H](C
+[C@H](N)C(=O)N 1CCC[C@H]1
+[C@@]3(C) [C@@H]4CC
+[C@@H]1 CCC
+O[C@H] 4C
+OCC (F)(F)F)
+O c2ccc(cc2)c3
+NC [C@H]1O
+C[C@H]1 CN(C
+COc1cccc2 c1
+COc1cc2 nccc(
+CN( c1ccccc1)S(=O)(=O)
+C 4CC4
+)CC 6
+) [C@H]2CC[C@H]3
+(N C4=O)
+(CCN (C)C
+(CCC (CC
+(CC O)CC
+no nc4
+cn[nH] c4
+ccc( O)
+c3s c4CCCCc4c3
+c3ccc(N )cc3
+c3ccc(F)cc3 F)
+c(C)c(C )c3
+[C@@H]5 C
+[C@@H](N)C [C@@H](N)
+[C@@H]( NC(=O)[C@H](C)NC(=O)[C@H](CC
+NC(=O) c1cnc(
+C\C\1=C /
+CO 5
+CCC (C)(C)C
+C2CC 3CC
+C#N )N
+=C( OC)C(=O)
+7CCCC 7
+2C (CC
+)ccc1 4
+)c1 cccc(
+)C#N )c1
+#N )cc1
+nc(o c3c2
+ccc(C [C@@H]1
+cc( n2)c3ccccc3
+c6 ccc(Cl)cc6
+c5ccccc5 )CC4
+c4ccc5 ncccc5
+c4cc( o
+c3ccc(O) c(c3
+c3ccc(Cl)cc 3)C
+c2ccc(cc2[N+](=O)[O-] )S(=O)(=O)
+c2ccc(cc2)c3ccccc3c4nn [nH]n4
+c1cnc[nH] 1)NC(=O)
+c1ccccc1 )C(=O)N[C@@H](C
+c1 )C2
+[nH]c( nc3c2
+[N+] (=C
+[C@H]6 CC
+[C@@H]2CC[C@H](CC 2)
+SC (C
+O[C@H](C) [C@@H](O)[C@H](O)
+O=C1 C=C(N
+O [C@@H]1CC
+NC(=O)c2cc( NC(=O)c3cc(
+N(C (=N
+Cc1o nc(CN
+Cc1 nc(O)
+C3CC 3)n
+=C 6C
+4) (C
+2)C3CC 3
+)c5 cccs5
+)c2cc1 C
+)c(C )c2c1
+)N C2=O)
+(CC#N)(C 3)n4
+ncccn 5
+c4cn n5
+c4ccc5OCCO c5c4
+c2cnc[nH] 2
+c2ccc3 c(
+c2ccc(C) nc2
+c2c1 )c4ccccc4
+c2c1 )C(F)(F)F
+[C@] (O)(CC
+[C@@]2(OC1)O[C@H]3C [C@H]4[C@@H]5CC
+[C@@H]3CCCN3C(=O) [C@H](CC
+NC(=O)c2cn n3
+Cl c1n
+COc1ccc2nc3cc(Cl)ccc3 c(N
+CN2CCC 1
+CN1CCN(CC (=O)N
+CN1 S(=O)(=O)
+CN(C)c1ccc( \C=C\
+CCN C(=N)N
+CCCCCCCC N
+=CC=C2C(=CC1=O) [C@H](CC
+=CC(=O) c3c(O)
+4CCOCC4 )cc3
+2)c3 nc(
+2)C(=O) OC
+1)c2ccc(cc2 F)c3ccccc3
+)c5 ccccc45
+)C(F)(F)F )C#N
+)C(C)(C)C )C(=O)N
+)C =C
+o ccc1
+ncn n2
+cnc( n2)N
+c5ccccc5 )c6ccccc6
+c4ccc(O) c5
+c4ccc( NC(=O)N
+c4cc( ccc1
+c4c(cn n4C
+c4 cccn
+c3nn n[nH]
+c3cccs 3)
+c3ccccc3 C(=O)
+c2ccc(Cl)cc 2)\
+c1ccc(cc1 )[C@H]2
+c1ccc( Br)cc1
+[C@@H](N C
+O)C (C)C)
+NC(=O)C2 =N
+NC(=O) c2cccc(c2)S(=O)(=O)N
+C[C@]12CC[C@H]3[C@@H](CCc4cc(O)ccc34) [C@@H]1CC
+CN(C) c1ccc(cc1)C(=O)N
+CCOC(=O)C1 CCCN(C1)C(=O)
+CCNC(=O) \C=C\
+CCN C1
+CC3CCCO 3
+CC(C)(C) NC(C)(C)C
+C=CC(=O) Oc2c1
+C2 3
+C OCO
+2CCN(CC2)c3 nc4
+2)C3=N S(=O)(=O)
+)C(=O)N[C@@H](CCCNC(=N)N )C(=O)N
+)C#N )N
+(F)(F)F)cc 4
+(C(=O)N O)S(=O)(=O)
+n3cncn 3
+c5 ccc(Cl)c(Cl)c5
+c2c1 OC
+c2c( o
+c(C)c (CN
+[O-][N+](=O) c1cccc2
+[C@] (CC
+[C@H]1 C[C@@H]1
+[C@H](O S(=O)(=O)[O-])
+O[C@H](CO)[C@@H](O) [C@H]3O
+OC \C=C\
+OC [C@@H](O
+Nc1ncnc2c1 c(nn2
+NC(=O) c2ccc(cc2)S(=O)(=O)N
+Clc1 ccc(Cl
+Cc1n[nH] c(C)c1
+Cc1n cc(C
+Cc1ccccc1 N
+Cc1ccc2 OCO
+Cc1cc( cc(C)
+CO CCCn1
+CN1C(=O) NC(=O)c2c1
+CN(C) c1cccc2c(cccc1
+CCCC 1
+CCC(CC )C(=O)N
+CCC C
+CC1(CC N1C(=O)
+C(=O)N[C@@H](CCCNC(=N)N )C(=O)N
+4CCOCC4 )c3
+)n4 ccnc4
+)n2 cc(C(=O)N)c(N
+)c8 ccccc8
+)c3c2 )cc1
+(O)C O
+(F) F)c3
+n3 c(CC
+n3 )c1
+cn c12
+cn 1)c2ccc3
+cccc 7
+ccc( \C=N\
+c9ccccc8 9
+c4cc( F)ccc3
+c3ccc( cn3
+c3ccc( OCCCC
+c3 cccn
+c2nc3 CCCC
+c(N)ncnc2 3)
+c(N )c3
+c(Cl )s
+c(CN (C)C
+c( cc6
+[C@H](O)C O)
+[C@@H](O) [C@H](
+Oc4ccccc4 )cc3
+Oc2ccc(cc2 C#N)S(=O)(=O)N
+N(CC c4c3
+COC1 CCC2(CC1)C
+CN c1nc2
+CC(=O)N c1cccc(c1)c2
+CC(=O) [O-]
+=CC =CC(=O)N
+=C(C)C =C(C)N
+2CCC (O)(CC
+)c3n2C (C)C)c5
+)C l
+(CC O
+s ccc5
+nc( n3)N
+n2 cc(CN
+ccccc 9
+c2n cccc1
+c1nn (C
+c1ccc2 C(=O)
+c1ccc(cc1 )C(=O)O)
+[C@]1(C) CCC[C@]2(C)[C@@H]1CC
+[C@H]4 [C@@H]3CC
+[C@H]2 O[C@H](C
+[C@H]1 [C@H](O)C
+[C@@H]3 CN(CC
+O) NC(=O)
+N(C (C)C
+Cl. COC(=O)
+Cl )[C@@H](O)
+Cc2 ccc(Cl)cc2
+Cc1 cnc(N
+C[C@H]1 CN(CC
+C[C@@]1 2
+CCC c2ccccc2
+CC(=O) C1=C(O)
+CC COc1ccc(cc1
+C3 4CC5CC(CC(C5)C3)C4
+C(=O)N[C@H]1 [C@H]2SCC
+C(=O)N[C@@H](CC CN=C(N)N)C(=O)N
+C(=C (CC
+=CC 3=O)
+3)C(=O) C
+2CCN(CC2)c3 ccccn3
+1)C(=O) C
+)c(C )c4
+)\ [O-]
+(CC1 C3
+(=N )
+nc( cn3
+cc(cn 4)c5
+cc(Br )c2
+c4ncn s4
+c3cccc( O)c3
+c3ccc(cc3 )S(=O)(=O)C
+c3cc(cc(c3 )C(F)(F)F)C(F)(F)F
+c3c[nH]c4ccccc34) NC(=O)[C@H](C
+c2ccc3 CCC
+c1ccc( I
+c1cc2 c(N
+c(C) nc(
+[C@@]45 C)
+[C@@H]2CC[C@H](CC 2)N
+[C@@H]1C [C@@H]2
+OC(CO)C (O)C(O)C
+O c2ccc(NC(=O)
+N C2=O
+CN=C(N)N )NC(=O)C
+CCc1 ccccc1
+CCN1 C(=O)N
+C 5
+=CC(=O)N (C
+3)c4ccc5ccccc5 n4
+3)C 4CC4
+3 )c1
+2C=C 1
+2C =CC
+1)C(=O)N [C@H](C
+)cc2 C1=O
+)cc1 [N+](=O)[O-]
+(O 2)
+(C)C) OC(=O)C
+(C)C(=O) [C@H](C
+%10 CC
+n c3
+csc( n5
+c4ccc(F)cc4 F
+c3cnccn 3
+c3cc(C)o n3
+c2occc2 )c3
+c(S )nnc1
+c(NC 4CC4)n
+c(Cl )cc1
+[C@H]3 O[C@H]([C@H](O)
+Oc1 nc(
+NC(=O)[C@H](C c1ccccc1)NC(=O)
+Cl c1ccc(cc1)n2
+Cc1oc(nc1 CCO
+Cc1 2
+COc1ccc(cc1)N (CC(=O)N
+COc1cc2 C(=O)N
+COP(=O)(S)O [C@H]%2
+COC(=O)N1 CCN(CC1)C(=O)
+CCOC(=O)c1 o
+CCC 3=O)
+CC (C(=O)
+4CCc5ccccc5 C4
+2CCC(CC2)N3CC (CC#N)(C3)n4
+)n3 cnc4
+)c4 ccc(Cl)c(Cl)c4
+)c( c6
+)C(=O) O)cc2
+o cn
+no nc3
+ncn c4s
+n 4)c5ccccc5
+c3ccccc3 C1=O
+c2ccccc2) /
+c2ccc(O)cc2 )C(=O)N
+c2ccc(F)c3 CCN(Cc23)C(=O)
+c(F)cc3C(=O) C(=CN(C4CC4)c3c2
+c( nc4
+\C(=C (\C)/
+\C 2=O
+[C@H](C)[C@@H](O)[C@]1(C)O) OC
+NC(=O)C2CCN(CC 2)C(=O)
+FC(F)(F) c1ccc(C
+Cc1onc( c2ccccc2)c1
+Cc1ncc([N+](=O)[O-] )n1
+CS c1cccc(
+COC(=O)N1c2ccnc(N [C@@H](C
+CO \N=C\1/
+CC(C)C (N
+C(=O) c3c2
+C(=CC(=O) Oc3c2
+5CCC[C@H]5 C(=O)N[C@@H](C
+4CCC[C@H]4 C(=O)N
+3CCN(CC3)C(=O) c4occc4
+2CC (CC2=O)
+.OC(=O)CC(O) (CC(=O)O)C(=O)O
+)NC(=O)[C@H](CC (C)C)NC(=O)
+)C3=O) CC2
+(C)c2 ccccc2
+(=O) OCC
+%1 1
+onc( n2)c3ccc(
+no c(CC
+ccccc4 6
+cc(OC)c(OC)c(OC )c4
+c7 C
+c7 8
+c5n cccc5
+c5cn (C
+c4ccccc4 OC
+c4ccccc4 )c2c1
+c3ccc( NC
+c3c( n2
+c2n c1
+c2csc( n2)c3
+c(F)cc cc6
+c( Br)cc
+\C=C/ C(=O)
+\C=C(\ C(=O)OC)/
+\C(=C\C=C/ 3\C
+[Na+].[Na+].[Na+].[Na+]. [Na+].[Na+].[Na+].[Na+].
+[C@H](C)CC [C@@H](
+[C@@]1 (O)C
+O[C@H] 3CC
+N3 CCN(C
+N c1cccc(
+Cc1ccc( Nc2n
+Cc1ccc( NC(=O)C
+CS(=O)(=O)N (CC(=O)N
+COC1=C (C)C(=O)
+COC(=O) [C@@]1(C
+CO [C@H]2
+CCCCC \C=C/C\C=C/C\C=C/C\C=C/
+CC2 )cc1
+CC1 =CC(=
+C(=O)C =CC(=O)
+3CCC c4ccccc34
+2CCOCC 2)c3ccccc3
+)n c(O)
+)cc( nc2c1
+)cc( Br)c2
+)c5 ccccc35
+)c5 6
+)C(F)(F)F) \
+)C 7
+ncc( s
+nc( [nH]c2
+ccs c5
+c6cccc n6
+c5cccs 5
+c5ccccc5 )C
+c3 c(Cl)ccc(
+c2ccc(Oc3 cc4cc(O
+\C(=C/ C)\C
+[nH] c3ccccc3c2
+[C@](C)(C [C@@H](C)C(=O)
+[C@@]1 (O)CC
+[C@@H]2C)O [C@@H]7
+SCC (=N
+P(=O)([O-]) [O-]
+OCC 5CC5
+Nc1n onc1
+NC(=O)c2cccs 2
+CSCC[C@H](NC(=O)[C@H](CC (C)C)NC(=O)[C@H](C
+CS C(=S)N
+COc1ccc(C 2
+CCOc1ccc( OCC
+CCN(C [C@H]1N
+CCC 1CC
+CC1=C (C)C(=O)
+CC(C) CN(
+CC(=O)N c1ccc(cc1)c2
+C(=O)O) cc1
+6 OO
+6 )C(=O)
+4 )cc1OC
+3)S(=O)(=O)N 4CCOCC4
+)c5 ccc(F)cc5
+)C 4CC4
+(= C(C#N
+sc( nc12)N
+nc(OC)sc6 n5
+ccc2 n
+cc(ccc5 [nH]4
+c5n cccc4
+c4ccc(cc4 )C(F)(F)F
+c3C=C c(
+c2noc( n2)c3
+c2ccccc2 )\C
+c2ccc(cc2 )C(=O)NCC
+c1ccc(NC(=O)C )cc1
+\C=C\2/ NC(=O)\C(=C\
+\C=C\ CN
+[C@H]2C =C
+[C@H](N) CCCN=C(N)N
+[C@@H]5CC[C@@]34C)[C@@H]1 2)C(=O)O
+[C@@H]4 [C@H](O)C
+Oc3ccc(cc3 C#N)S(=O)(=O)N
+OC c1ccccc1)C(=O)
+OC OC
+NC1=C(C#N )C(C
+NC(=O) c1cccc(
+N1 C(=O)N
+N N
+FC(F)(F) c1cccc(c1)C(=O)N
+Cc1ccc(C (=O)N
+CN1 CCC2(CC1)C
+CN c1nc(
+CCOC(=O)C1=C (C)N
+CCCN(CCC )CC
+CCC(CC 1)n2
+CC(N S(=O)(=O)
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+. CC(=O)O
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+5 )C#N
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+1)c2 ccc(Cl)cc2
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+( c3ccc(cc3)C
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+4)c5 ccc(Cl)cc5
+4) [C@@H](C)
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+2 (CC
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+) OC(=O)C
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+[s +]
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+[C@@]2 (O
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+2 =C(C
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+\C(=N/ S(=O)(=O)
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+2 CN
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+)\S 1
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+(N =C
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+\C=C (\C#N)/
+[C@H](O) C(=O)N
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+OCCCC (=O)O)
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+F )C3
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+CC COc1ccc2
+3CCN(CC3)C(=O) c4occc4
+3)c4ccc( O)cc4
+3) cc(OC)c1OC
+2 =C(C(=O)NC2=O)
+)c4ccccc4 )c1
+)c1 [N+](=O)[O-]
+n4 )C
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+[C@] (N)
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+[C@@H]3O[C@H](CO)[C@@H](O)[C@H](O) [C@H]3
+[C@@H]2C [C@@H]3C
+[C@@H]2C =C
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+O) C(=O)C
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+CCCC COC(=O)N1CCN(CC1)C(=O)
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+2C c3ccccc3C
+2 N=C
+)C(F)(F)F) c1
+(OC 3
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+c1cc2 OCOc2cc1
+[C@]5 (C
+[C@@H]3 [C@H](O)C
+O=C1 Oc2ccccc2
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+COc1 cc(C)
+COC(=O)C1=C (C)NC(=C(C1
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+CC [C@H](C
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+Br c1ccc(cc1)S(=O)(=O)N
+Br )s
+=CC(= CC
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+4Cc5ccccc5 C4
+4)c5 occc5
+3CCOCC 3)C(=O)N
+2CCN(Cc3ccccc3 )CC2
+)c5cc6 OCOc6cc25
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+)c(O) cc2
+)\C )\C)C
+(CCCCC 3)
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+CCCN 3CC
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+CC c1ccc(N
+C c2ccccc2)c3
+=CC(=O)N (C)C
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+)c4o ccc4
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+) P(=O)(OC
+(=C(O) C1=O)C(=O)
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+[C@]1(C) [C@@H](C[C@@](C)(C=C)[C@@H](O)[C@@H]3C)
+[C@]1 (O)CC
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+=C 5C
+= CCC3
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+2C=C(C)C(=O)N C2=O)
+2)C(=O)C O
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+(C) CC1
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+[N-] =[N+]
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+[C@@H]5 O
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+1 CC(=O)N
+/C/ 2=N
+)nc( nc1
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+)cc3 OC
+)c4cc5 ccccc5
+)c3 cccc(Cl)c3
+)c3 ccc(C)cc3
+)c2 ccncc2
+)C5 =O)
+)C(=O)OC c4ccccc4
+)C(= C3
+)C( OC(=O)
+) [C@H](CC
+(C)C) OC(=O)C
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+ccc [n+](C
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+c1ccccc1 )C(=O)C
+c(O) ccc(
+\C )\C)C
+[C@H]2 3)
+[C@H](O) [C@@H](N)C
+[C@@H]1 CN
+[C@@H](C) [C@H](O)
+S c2ccc(cc2
+S )NC(=O)[C@H](CC
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+Nc1 c(
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+FC(F)(F) c1cccc(c1)c2
+Cl CCN(CC
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+C[C@@H]1CC [C@@H](C
+COc1ccc(cc1 )C(=O)\C=C\
+COc1cc( NS(=O)(=O)
+CCN 4CCOCC4
+CC(=O) c1ccccc1
+C(=O) CC(C)(C)C
+9 =O)
+4CCOCC 4)C(=O)N
+)c5 ccc(Cl)cc5
+)c4 ccn
+)c3ccc(NS(=O)(=O)C )c(F)c3
+)C2=O) cc1
+)C =O)
+(C 2)C
+s c(C
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+n [nH]c(
+cs c(CC
+co cc6
+c4ccccc4 )C3
+c4ccc(N )cc4
+c4ccc( Oc5
+c3ccc(Cl )s
+c2c(C1) sc3
+c(Cl)cc cc3Cl
+\C=C \C=C\C
+[C@H]2 [C@@H](C
+[C@@H]4[C@@]5(C)CC C(=O)C(C)(C)
+Oc2cc( N
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+5CC6CC (CC
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+2) S
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+) cc(OC)c1
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+[C@H]3 C(=O)O[C@@H](C
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+[C@H](C [C@H](O)[C@H](C
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+CCN C1=O)
+CC1CN(CC (C)
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+C(=O)N)NC(=O)[C@H](CC C(=O)N
+=C(Br )C(=O)
+8 CCCCC
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+)c2ccc( O)cc2
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+) on1
+(O)C (C)(C)
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+c4ccc( I
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+c2cc(cc(c2 )C(F)(F)F)C(F)(F)F)
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+[C@@]3 5CC
+[C@@H]1 [C@@]24OO5
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+Oc1 cc(C
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+OCCO 3
+OC(C Oc1ccccc1
+OC(=O)C1=CN(C2CC2)c3 cc(N
+O) c(CN
+O c2cccc3
+O N1C(=O)
+NC(=O)CC S
+N=C(C =CC2=O)
+Cc1cn (C
+COc1ccc2 c(C
+CN1 C(=C(C(=O)
+CCn1 cc(CN
+CCc1 c2
+CCN(CC)C(=O) C1
+CC2(CCN(CC 2)C(=O)
+CC1=C 2
+CC1=C (C(=O)
+CC [C@@H]1C
+CC (CC(=O)N
+C(=O)N 2
+4CCC 3C
+)c5ccc( O)cc5
+)c4cccc(c4 )C(F)(F)F
+)c4cccc( O)c4
+)N )n
+ccn 2
+c3o c(C
+c2cc3 c(N
+c2c( cnc3
+c1s ccc1
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+[nH] nc2
+[nH] n4
+[nH] 2)N
+[C@@H]5 O)
+[C@@H]3CC [C@@H](CC
+[C@@H](O) C(=O)N
+[C@@H](NC(=O)[C@H](Cc4ccccc4 )NC(=O)[C@@H](NC(=O)[C@H](CC
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+O=C(N 1CCN(CC1)c2
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+COc1ccc( OCC
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+CC(=O)NC[C@H]1CN(C(=O)O 1)c2ccc(
+C(=O)N(C( c4ccc(Cl)cc4
+=CC(= NC(=O)N
+= S(=O)
+5 )c2c1
+4CCc5ccccc5 C4
+2)c3 ccc(C)cc3
+)S(=O)(=O)N 3CCCCC3
+)NC(=O)[C@H] (N
+(Cl )C2=O)
+(=C (C(=O)
+o c(C)c1
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+n3 )C(=O)N
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+c5 cccc(C
+c3cccs 3)cc1
+c3 )C(=O)N
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+c1 ccnc2cc(Cl)ccc1
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+[nH] nc(C
+[nH] 2)C(=O)N
+[C@H](N C1=O)C(=O)N
+S(=O)(=O) c3ccc(C)cc3
+OC[C@H]1O [C@H](O
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+Fc1ccc( F)c(c1
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+COC1 =CC=C2C(=CC1=O)[C@H](CC
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+CN(C) CC1
+CN(C c1
+CCN(C)CC 2
+CCN(C c1ccccc1)C(=O)
+CCCN C(=S)N
+CC3 =C
+CC2 (O)
+CC1(C)C c2c(C
+CC(=O) c1ccc(N
+9 C(=O)
+3CCN(CC3)c4 cccc5
+3CCCC 3)
+)c4 ccc(C
+)c( F)cc1
+)NC(=O)CNC(=O)[C@H](CC(=O)O)NC(=O)[C@@H](NC(=O)[C@H](Cc3ccccc3)NC(=O) [C@@H](NC(=O)[C@H](CC(=O)N
+)C(C)C )C(=O)N
+ccc5 ccccc45
+ccc(Cl)cc 2Cl
+cc(OC)c(OC)c(OC )c3
+c8 9
+c3ccccc3 )c2
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+c3cc(O)c(O) c(O)c3
+c2s c(N
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+c1 sc(
+[nH]c( cc4
+[P @](=O)
+[C@H](CC(C)C)N(C)C(=O) [C@H](CC(C)C)N(C)C(=O)
+[C@H]( [C@@H](C)CC
+[C@@]4 6CC
+[C@@H]2CCCC [C@@H]2
+[2H] C(
+OC2=O) cc1
+Cc1ccc(cc1 )N2C(=O)
+CN2CCC 1CC
+CN 2CCN(C
+CN 1
+CCN 3CCCCC3
+CCCCCCCC \C=C\
+CC(C) CC(C)C
+CC(C n1
+CC(=O)N[C@@H]1 [C@@H](O)C
+CC(=O) OC[C@H]1O[C@H](
+CC n3
+=C1 N(C
+6CCN(C)CC 6
+4C OC
+3CCOCC 3)c4ccccc4
+2CC (N
+)c2 nc(cs
+)c1 ccc2
+)CC OCC
+)C(F)(F)F) N
+)C(=O)N c6
+n4 ccnc4
+ccccc1 3)N
+cc( nc4
+c4cccc(c4 )C(=O)N
+c3ccccc3 O2
+c3 cc1
+c2nc( sc2
+c2nc( ccc2
+c2ccc(cc2 )C(C)(C)C
+c1n [nH]c2
+c1ccc(O)cc1 )NC(=O)[C@H](CC
+c(Cl)cc( cc1
+\C=C (\C
+[n+] 6
+[C@@H](O [C@@H]5
+[C@@H]( OCC
+OC(=O) O[C@]1
+NC(=O)[C@H]2 CCCN2C(=O)
+C\C(=C/ C=C/
+COc1ccc(C n2
+CCN 3CCOCC3
+CCC n2ccnc2
+CCC c1cc(O)
+CC (CC(=O)
+C3 )cc2
+C2CCC(CC 1
+C2=C(C 1
+C(=O)OC )
+C(=O)C=C( Oc3c2
+)cc1 4
+)cc 8
+)c4 cccc(CN
+)c3nc4ccccc4 s3
+)c2 cc(NC(=O)
+)c(C#N )c(N
+occc 6
+nc(C) cc3C
+cn[nH] c5
+cn c3cc(
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+c4cn ccc4
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+c2ccccc2 OC
+c2ccc(Br)cc 2)
+c2cc(OC)cc(OC )c2
+c2c( F)c(F)c(F)c(F)c2
+\C=C\C(=O)N O)
+[nH] 2)cc1
+[C@H]4 CN(C
+[C@H](C)O %10
+[C@H]%10 O)
+[C@@H](C OC
+[C@@H]( [C@H](C)
+SSC [C@H](NC1=O)
+S2(=O)=O) cc1
+Oc1cccc(c1 )C2
+O[C@H] 9
+O[C@@H]1 CO
+OC1 C
+O [C@@H]4C
+NC(=N )S
+COc1ccc(CN 2
+CCCCN 2
+CC(C)C NC(=O)
+CC(C) CCCC(C)
+C (NC(=O)
+4CC4)n 5
+4)N 5CCOCC5
+4 C3=O)
+)C1=O) C
+(O) C(C
+(C)C)NC(=O)[C@H](CC (=O)N)NC(=O)
+(C)C)NC(=O) [C@@H](NC(=O)
+(C) CCCC2
+o ccc4
+n3)C 4CC4
+n2 cc(cn
+n cc[nH]
+cccc n3)c4
+ccc(Cl )c3
+c6 cc7
+c3ccccc3C2=O) c1
+c3 coc(
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+c2 coc(
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+c1ccc(OC )cc1
+c%2 0
+[N+] 4
+[C@]5 (NC4=O)
+[C@]3 4CC
+[C@H](C)[C@H]1O)\C)\C )C2=O
+[C@H]%1 5
+[C@@H]1[C@H](O) c2ccc(Oc3cc4cc(Oc5ccc(cc5
+[C@@H](O)[C@@H](O [C@@H]%3
+NC(=O)N \N=C\
+NC(=O)C (NC(=O)
+F)cc 2
+Cc1o nc(NS(=O)(=O)
+COc1ccc2[nH] cc(CC
+COC(=O) [C@]12
+CCN =C(S)N
+C(=O) [O-])
+C(=CC(=O)O c2c1)C
+C#N )
+7CCCCC 7
+2CCN(CC2)c3 ns
+)c4cccc( F)c4
+)c2 ccccc1
+)c(C)n2 C(=O)
+)c( cc3
+(OCCC #N)
+( [C@H]2C
+nn1 )N
+cccc3 ccccc2
+c5ccccc5 N
+c4n cccn
+c4ccc(O)cc4 )NC(=O)[C@H](CC
+c3cn c4[nH]
+c3cccc(Oc4ccccc4 )c3
+c2cc1 Cl
+c1ccc(cc1)N2 C(=N
+\C=C \C=C/
+[N+](=O)[O-] )
+[C@]6 (C
+[C@H]( [C@@H]2
+[C@@H](OC(=O)C) [C@H](OC(=O)C)
+[C@@H](O 2)
+OCCN(CC O)
+OC3 =N
+OC(=O) c1cn
+OC(=O) CO
+N=C (O
+N(C=C(C(=O)O)C(=O)c4cc3 F)C
+N c2ccc(cc2
+F) (C
+Cc2 ccc(C
+Cc1n nc(SCC(=O)N
+C[C@H](CO)N1C[C@H](C) [C@@H](CN(C)C
+C[C@@H]1CN(C [C@H](C)O
+COC(=O)c1 ccc(CN
+CN1 CCN
+CCN(CC C
+C3=C (CCCC
+C(=O)N 4
+=CC(=NC %4
+5CCN(CC 5)C
+5 )C(=O)O)
+4CCOCC4 )cc3
+2CCC(CC2)n3 nccc3
+2CCC(CC2)N3CC(CC#N)(C3)n4 cc(cn4)c5
+2)c3 occc3
+1CCCCC1 )NC(=O)
+)n3 ccnc3
+) [C@H](NC(=O)
+(F)(F )C(F)(F)F)
+(C)C) NC(=O)N
+(C(=O)N C
+o c(CC
+nn n3
+nccn 4
+cnc2 c(NC
+ccc(cc1 )c2
+c3ccc(F)cc 3)C(=O)N
+c2ccccc2 )C(=O)O)
+c2ccccc1 2)C(=O)
+c2cccc(c2 )C3
+c1cccc (CC
+c(N)n c3
+c(F) ccc(
+[P@ @](=O)
+[C@]6(C) [C@H]5
+[C@]2 (CO
+[C@@H]5C [C@]5(NC4=O)
+[C@@H](C 1)N
+NC(=O)c2 oc(C
+NC(=O)[C@H](C)NC(=O) [C@@H](NC(=O)[C@H](C
+NC(=O)CS C1=N
+Cc1cc(C)n( n1)c2
+COc1 c(C)
+CC[C@H]1OC(=O)[C@H](C)[C@@H](O[C@H]2C[C@@](C)(OC )[C@@H](O
+CCOc1 cc(N
+CCOP(=O) (C
+CCOC(=O) CNC(=O)
+CCNCC 5
+CCC (C)N
+CC1 CCCCN1C(=O)
+C=C 3
+3)c4ccc(cc4 )C#N
+3)c4 ccc(C)cc4
+)cc2 OC
+)c4 C
+)NC(=O)[C@H](CO) NC(=O)[C@@H](C
+)CC2 )c1Cl
+)C(=O)OC )C
+#CC #
+nc(C) c1
+n3 nc(cc3
+co cc1
+cnnn 5
+cc(nc( n2)N
+c5 cccc(Cl)c5
+c4ccccc4 OC3=O
+c3ccc(cc3)N 4CCOCC4
+c3ccc(cc3 )c4ccc(cc4
+c2s cc(
+c2s c3ccccc3c2
+c2ccc(cc2)N 3CCOCC3
+c2c(C) c1
+c1cc( F)ccc1
+c(O)c(O) c(O)
+c(Cl)cc cc3
+\C=C\3/ C(=O)N
+[C@H]4 O[C@H]([C@H](O)
+[11C H3]
+Oc1ccc( CCN
+O=C(CS C1=N
+NC(=O)CN2 C(=O)N(C
+N4 CCN(C)CC
+N2C(=O)C=Cc3cc(ccc23)S(=O)(=O)N c4cco
+Cn1 cc(cc1
+Cc1cccc( NC(=O)
+COc1 ccnc(
+CCNCC 6
+CCCC Oc1ccc2
+CCC2 =C
+CC(=O)NC[C@H]1CN(C(=O)O 1)c2ccc(N
+C1=O) S(=O)(=O)N
+C#N )C
+7 )c(
+4CCC[C@H]4 C(=O)N[C@@H](C
+3)S(=O)(=O) c4ccccc4
+3 )c1C
+2CCCCC2 )N
+2CC c3ccccc3C2
+2C [C@H]3
+)c4 5
+)c(C) nn1C
+)CC 3)n
+) CC(=O)O
+(C)C)C (C)C)N
+n1C c3ccc(cc3)c4ccccc4
+cccc(C 2=O)
+ccc[n+] (CC
+c4ccc( NS(=O)(=O)
+c4 ccc(C)
+c3n nc(NC(=O)C
+c3cccnc3 )cc2
+c3ccc(F)c( F)c3
+c3c(C)onc3 C
+c2nc( SC
+c2ccccc2 Br
+c1cc(F) cc(F)c1
+c%10 ccccc9%10
+[nH]c(nc2c1 )c3ccc(
+[O-][N+](=O) c1oc(
+[N+] (C)(CC
+[C@]4 5C
+[C@@H](O [C@H]2
+[C@@H](N)CCCN C(=N)N
+[C@@H]( [C@@H](C
+Oc2ccc(cc2 )C(=O)N
+OCCN 5CCCC5
+O)c(O) c1
+NC(=O)CNC(=O) [C@@H](NC(=O)[C@H](C)NC(=O)[C@H](C
+NC(=O) CC3
+Cc1 c2ccccc2
+CO [C@H]1O
+CCCCN C(=S)N
+CCCCCCCC N1
+CCC (O
+CC(=O)N O
+CC c1ccc(cc1)C(=O)N
+CC c1ccc(NC(=O)
+C(=O)C=C( Oc1
+C c4ccccc4
+=CC(=[N+] (C)C)C
+5CC CO5
+3)c4ccc(OC )cc4
+3)c4 cccc(Cl)c4
+2CCN(CC2)C(=O) c3occc3
+2CC3 (C
+2)c3ccc4 OCOc4c3
+1 CCCC1
+)c4 cccc(Cl)c4
+)S(=O)(=O) c2ccc3
+) CCN1
+(O[C@H]1 [C@H](O)[C@H](O)C
+(CCCC )C
+n2nn c(C(=O)N
+cnn(C )c5
+cn n3
+c4ccccc4 )CC2
+c4ccc(cc4 )C(F)(F)F)cc3
+c4 )c5
+c3ccccc3 Br
+c3c2 )C(=O)C
+c2cn cc(
+c2ccccc2 )s1
+c2cccc(Cl)c2 Cl
+c2[nH] cnc2
+[C@]6 (C)CC
+[C@H](C)O 5)
+[C@@]7 (CC
+Oc1ccc( \C=C\2/
+OC )n
+O [C@H]1O
+F) CC
+Cn1 cc(C(=O)N
+C[C@]1 2
+C[C@@H](O) CC
+CN(CC (O)
+CN 2CCCC
+CCn1 nc(C
+CCS c1nnc(
+CCCCC (CC
+C [S+]
+4CCC 4
+3CCN(C c4ccccc4)CC3
+3=N NC(=O)C4=C3CCCC
+1)c2 nn
+)c4 ccncc4
+)c( nn1
+)C(=O)N[C@@H](CCCCN C(C)C)C(=O)N
+)C(=O) [C@H](CC
+)C(=O) C(=O)N
+) [C@@H]2C
+(C (=C(N)O
+nc2 4
+nc(N 3CCOCC3)c4
+nc( nn2
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+ccn c6
+cccc n6
+ccc8 c7
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+c7 )c8
+c5ccccc5 F
+c3ncc( cc2
+c3ccccc3 )c(n2
+c2cccc( O)c2
+c2cc3ccccc3 cc2
+c1nc2 c(
+c%3 1
+\C 2=O)
+[C@H](O) [C@H]1O
+[C@H](O) [C@@H]2
+[C@@H]1 [C@@H]2
+[C@@H](NC(=O)CNC(=O)[C@H](CC C(=O)O)
+O N(C
+F )C#N
+Cc1c( cccc1
+CS(=O)(=O) c1ccc2
+COc1ccc( NC(=O)CS
+COC(=O) CCC(=O)N
+CN(CC C
+CN(C c1cn
+CCOP(=O)(OCC )C(N
+CCCC (
+CCC [n+]1
+CC(=O) Oc1ccc2
+C(CC (=O)
+C(C n1
+C (CC(=O)N
+4CCCC4 )c3
+4C [C@H](C
+4) ccc1
+3)c4ccc( Br)cc4
+)cc 3CC
+)C1=O) c4ccccc4
+(CC (C)C)C(=O)N
+n2c( nc3ccccc2
+n c3ccc(cc3
+cnc1 N
+cccc n6
+c3ccc(F)cc3 )c2
+c2ccccc2 )c3ccc(
+c2ccc3OCOc3c2 )cc1
+c1ccc(nc1 )c2
+[nH]n cc3
+[nH] c(N
+[O-][N+](=O) c1ccc(cc1)S(=O)(=O)N
+[C@H](C(=O)N 1C
+[C@@H]4C [C@H]3
+[C@@H]1 2)
+Oc4 cccc(c4
+OCCCC [C@H](C)
+O 1)C(=O)
+Nc1n cc(nc1
+NC(=O)C3 CC3
+Cl c1ccc(cc1)C2=N
+Cc1cc( NC(=O)N
+CN1 CCN(CC1)C2
+CN1 C(=S)N
+CN )NC(=O)
+CC[C@H]1OC(=O)[C@H](C)[C@@H](O[C@H]2C[C@@](C)(OC)[C@@H](O)[C@H](C)O2)[C@H](C)[C@@H](O[C@@H]3O[C@H](C)C[C@@H]([C@H]3 O)N(C)C)[C@](C)(O)C[C@@H](C)
+CCN1 C(=O)N(CC
+CC(C)(C)OC(=O)N 1CCC(CC
+CC \C=C/
+C(=O)NCC =C
+C(=O)N2 c3ccc(cc3)c4
+5 /
+4CC S(=O)(=O)
+4CC 4)cc1
+4 )C2=O
+1C =N
+)c3 cccs
+)c(O) cc3
+)CC 3)
+)C(=O)N[C@@H](C c4ccc(O)cc4
+)C(=O)N CC(=O)O
+)C(=O)N 3
+(OCCC#N) (OC
+c4 ccnc(N
+c3ccc(N )nc3
+c2ccc(C [C@H](NC(=O)
+c1cccc(c1 )C2=N
+c(N)n c(nc2
+[nH] 1)N
+[C@H]3C (C)(C)CCC
+[C@H](OC(=O)c4ccccc4)[C@]5(O)C[C@H](OC(=O) [C@H](O)[C@@H](NC(=O)c6ccccc6)c7ccccc7
+[C@@H](C =C
+O)C(=O)N[C@@H](CC (C)C)C(=O)N
+Nc1ncnc2c1ncn2 C3
+NC(C)(C)C =C(C
+NC(=O)c2 nc(s
+N (c3ccccc3
+COc1cc(OC )nc(
+CNCCO 3
+CCCC CO
+CCC (=N)N
+CC2 CCN(C
+C2=O) C
+C1=N S(=O)(=O)
+C#N)c2 onc(n2)c3
+3)n4 cn
+3 )c6
+1)c2 cccnc2
+)CC 6
+)C(=O) C1
+(F)(F)F.F[P-](F)(F) (F)(F)F.F[P-](F)(F)
+nccnc2 C3CN(C
+n6 cn
+n5 ccnc5
+n2cncn 2)c3ccc(F)cc3
+n1C c3ccccc3
+ccnc4 cc(Cl)ccc3
+cc7OCOc7 cc2
+c4c([nH] c5ccccc45)
+c3n c4cc(ccc4
+c3cccs 3)c4
+c3c2 )c4ccccc4
+c2ccccc2 N1
+c2ccc3 oc(
+c2ccc(F)c(Cl )c2
+c2c1 )C(=O)O
+c1ccc(OC2 CCN(C
+[NH +]
+[C@H]([C@H](O) [C@@H]2O)n3cnc4
+[C@@H]5CC [C@H](CC
+[C@@H](NC(=O)OC )C(C)C
+S )NC(=O)[C@H](C
+Oc1ccc(C (=O)
+O[C@H](CO)[C@@H](O)[C@H](O) [C@H]4O)
+OCC(=O) OCC(=O)N
+NS(=O)(=O) c1ccc2
+NC(=O)[C@H] 4
+Cc1nc(C )c(s
+C[C@@H](O) CN
+COc1ccc2nccc( [C@@H](O)CN
+COC(=O)C1=C (C)N(C
+COC [C@H](C)
+CN [C@H](C
+CC[C@H](C)[C@H]( NC(=O)[C@H](C)NC(=O)[C@H](CC
+CCO c1ccc(cc1)C2
+CCCCCC n1
+Br )n
+=N c2cc1
+=CC(=NC2=O) N
+5CC6CC(CC 4C
+3CCN(CC3)c4 ccc(F)cc4
+2CCN(CC2)c3 cccc4
+2CCC(Cc3ccccc3 )CC2
+2CCC c3ccccc23
+)ncn 1
+)n1 2
+)c5ccc(cc5 )C#N
+)c1 n
+)N =C1
+o c2c1
+nn 3)N
+nc(C) s
+nc( cc4
+c[nH] c(
+c3ccc(cc3 C24
+c2nn c(CCCC
+c2ccccc2 )C1=O
+c2ccc3 ncnc(N
+c2ccc(F)cc 2)c3ccc(F)cc
+c2ccc( c(c2
+c2ccc( CCCC
+c1ccccc1)NC(=O)C NC(=O)CNC(=O)[C@@H](N)C
+c1 cc(Cl)c2
+c(Cl)n c23
+c( F)c4
+\C #N
+[C@]2 (N
+[C@H]1CC[C@@]2(C)[C@@H](CC [C@]3(C)[C@@H]2CC
+[C@H](O) [C@H]3
+[C@H](O) [C@H](C)C
+[C@H](C) \C=C\
+[C@@H]5CC [C@@]34C
+[C@@H]2CC[C@H]1C [C@H](C
+[C@@H](O)[C@@H](O) [C@@H]1O
+[C@@H](O [C@H]3
+Oc5ccccc5 )cc4
+O[C@H](CO)[C@@H](O)[C@H](O) [C@H]5
+OCCCC [C@@H](C)
+Nc1nc(O) c2ncn
+NC(=O)c2 cccs2
+NC(=O)N 2C
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+F)cc 5
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+CN(C)C(=O)c1cc2 cnc(N
+CN(C) c1ccc(cc1
+CN(C) c1ccc(CN
+CCN 1
+CCCCCN(CC CCC
+CC(C)(C) CC(=O)
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+CC 3CC
+C=C 2C
+C=C (C)C
+3)c4ccc( nc4
+2CC (CC
+2 =C1
+)c(c3 )[N+](=O)[O-]
+)c( cn
+)C(=O)O c2c1
+(C(=O) C2
+%3 9
+nnnn 5C
+n1)C2 CCCN(C2)C(=O)
+cc4 C(=O)NCC
+cc(ccc1 2)S(=O)(=O)N
+c7ccc8 ccccc8
+c5ccccc5 n4
+c5ccc( O)c(O)
+c4 oc(cc4
+c3n c(C
+c3ccccc3 )C2
+c3cccc4 cccc(C2=O)
+c3c2 )C(=O)N
+c3c(C 2)
+c2oc(cc2 )C(=O)N
+c2o nc(C
+c2ccccc2 C(=O)O)
+c2ccccc2 )CC1
+c2cccc3ns nc2
+c2cc(cc(c2 )C(F)(F)F
+c2c(C #N
+c1ccc( s1)c2
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+c(cc( nc1
+c%2 9
+[nH] cnc4
+[nH] c(C(=O)
+[C@H]3C 2
+[C@@H]6CC [C@@]5(C)
+[C@@H](O)C (C)(C)
+[C@@H](CC CN=C(N)N
+SC (C)(C)C
+S4 (=O)=O)
+OCC2 =CC(=O)N
+OC(=O) c1ccc(cc1)c2
+NC(=O)c2s ccc2
+NC(=O)c2 occc2
+N 3CCOCC3
+FC(F)(F)c1 cc(C
+Cc2 ccncc2
+Cc1ccc( c(C)c1)S(=O)(=O)N
+COc1ccc( CCC(=O)N
+COC (C)(C)C
+CN3CCC2CC 3
+CCN1 C(=O)N(C
+CCN C2
+CCCCC (NC(=O)
+CCCC c2c1
+CCC n3ccnc3
+CC2 N(C
+CC1 =C(C#N
+CC(C) CCCC
+CC(=O)N[C@@H](CC C(=O)N
+C(=O) CCCC
+C( c1ccccc1)c2
+=C( I
+2)c3cccc( F)c3
+1) (
+)nc1 O
+)S(=O)(=O) O)
+)C (O)C
+(O) CC2
+(=O)O) C(=O)N
+sc( cc3
+nc3 NC
+c5cccc6 ccccc56
+c5ccc(OC (F)(F)F)cc
+c4ccc(F)cc4 )cc3
+c3cn c4ccccc4c3
+c3ccc(cn 3)C(=O)N
+c2ccccc2 )nc3
+c1ccc( CC(=O)N
+c(C 1=O)
+\N=C (N
+\C=C\C(=O)O) cc
+\C=C \C=C(/C)
+[nH] 2)C
+[n -]
+[C@@]3(C)[C@@H](CC [C@]4(C)
+[C@@H]1CC[C@H](CC 1)NC(=O)
+O=C1C=C (O
+O=C(N CCc1
+O [N+](=O)[O-]
+NC(=N)c1 cccc(
+N(CC(=O)N C
+Cc1ccccc1 C
+COc4ccccc4 O3
+COc1cccc(c1 )S(=O)(=O)N
+COC(=O)c1 c(
+COC(=O) [C@@]1(C)
+CN1N(C(=O)C(=C1 C
+CN( [C@H]1
+CCN(C 1CCS(=O)(=O)C1)C(=O)
+CCCCC1 =NN(C
+CC(C)N1 CC
+CC [C@H]3
+C2=O) cc1OC
+5CC 5)c6
+5 C4
+5 )CC2
+3CCCC 3)c1
+2)N (CC
+)n n4
+)cc2 3)c1
+)c3ccc4 OCCOc4c3
+)c ncc2
+) cc(O)
+nc( cn2
+n2 c(N
+ccc3 c2)C(=O)N
+c5ccccc5 )C(=O)N
+c5 cc(OC)c(OC)c(OC)c5
+c4cn cnc4
+c4c(F)cccc4 F)
+c4 ccsc4
+c3ccc4 ncccc4c3
+c3 nc(N)n
+c2n c3cc(
+c2n [nH]c(
+c2cnc[nH] 2)C(=O)N
+c2ccc(cc2 )C(=O)O
+c2cc( I
+c(C) nn3
+c( cs
+[O-][N+](=O) c1ccc(cc1)N
+[C@]2(O)C(=O) C(=C1
+[C@@H](C(=O) O3)
+Oc4 cccc(
+Nc1ccccc1 NC(=O)
+NC(=O)c2 oc(
+N C3
+COc1ccc( \C=C/2\
+COC(=O) [C@@H](C
+CCOC(=O)C1 (C
+CCOC(=O) [C@H](C
+CCN(CC 1)
+CCCC1 =CC(=O)
+CCC(=O)N (
+CC(C)(C) OC(=O)C
+CC(C) NC(=O)C1
+C4 CCC(C
+C3=C (CCC3
+=C( NC(=O)
+5CC c6
+4)C (C)C
+3C c4c(
+)C(=O)N[C@H](CC CCN
+(CCN 3C
+(CCN (C)CC
+nc(O) c1
+cccc2 c34
+ccc(C [C@H](NC(=O)[C@H]
+c5 c(Cl)cccc5
+c4ccccc4)n 3
+c4ccc(OC )c(OC)c4
+c3cnc[nH] 3)
+c2nc( cc(
+c2cccc(c2 )C(F)(F)F)cc1
+c2 cccn2
+c1o c(CS
+c(C)c (CC
+\N=C/ 1
+[NH 4
+[C@]1(C) O
+[C@@H]([C@H](C [C@](C)(CC(=O)O)
+[C@@H](C c3ccccc3
+O=C(N [C@@H](C
+O=C(N CCCN
+O=C(CC c1ccccc1)N
+O) c2ccccc2
+CNCCO 4
+CN1C=N c2cc(nc(N
+CN1C(=O)O c2cc(ccc12)S(=O)(=O)N
+CN(C(=O) C3
+CCCCC 6
+CCCC NC(=O)C
+CC1 N(C
+CC(C)C[C@@H]1 NC(=O)[C@H](C
+CC c1cc(N
+C(=O) C(=O)N
+5CCOCC5 )c4
+5)C (C)(C)C
+)c8 ccccc8
+)c3ccc4 OCOc4c3
+)c2 3)cc1
+)c(Br )c3
+)C3 4CC5CC(CC(C5)C3)C4
+)C1 C
+(N =C1
+n3 c(S
+cc(OC )c2
+c7 ccc(Cl)cc7
+c6ccccc6 )C
+c4ccccn4 )n[nH]
+c4cccc(c4 )[N+](=O)[O-]
+c4ccc(cc4 s
+c3 cc[n+]
+c2nc( ncc2
+c2cccc3ccccc2 3
+c2c[nH]cn 2)NC(=O)
+c2c3CCCC c3n
+c1c[nH] c(
+\N=C \C=C\
+\C(=N\C#N )\N
+\ S
+[NH4 +].
+[N+] (C)
+[C@](C) (O)CC
+[C@H](O) CC(=O)
+[C@H](C (=O)O)
+[C@@]45C [C@@]35CC
+[C@@H]5CC[C@@]34C)[C@@H]2C1 )C(=O)O
+[C@@H]5CC[C@@]34C) [C@@H]2C1
+[C@@H](N )C(=O)N
+S )C(=O)O
+Oc2 cc(C)
+Oc1 3
+OCOc3 c2
+OC )O
+NC(=O)N C1=O
+Cc1nn (C)cc1
+Cc1nc( O)c2
+C[C@H](CC C(=O)N
+COc1cc(NC (C)CCCN
+COC (
+CNC(=N)N)NC(=O)[C@H](CC CNC(=N)N)NC(=O)
+CN(CC(=O)N c1ccccc1
+CN(C) CCCn1
+CCn1 cc(C
+CCN 2CCOCC2
+CCC (C)C
+CC1 CCCN1
+CC(C)(C) c1ccc(CN
+C=C(C(=O)N C
+C2 CCC1
+C2 )N
+C P(=O)(O)
+5 CCCCCC
+5 )cc4
+5 )cc3
+3CCN(CC (=O)N
+3CCC(Cc4ccccc4 )CC3
+3 =CC(=O)N
+2CCN(CC2)C(=O) C
+)c6 ccccc16
+)c3cccc( F)c3
+)c3ccc(cc3 )c4ccccc4
+)C(=O)N[C@@H](CC(C)C )C(=O)N[C@@H](C(C)C
+)C(=O)N[C@@H](CC(C)C )C(=O)N[C@@H](C
+(CCN(CC 2)C(=O)
+n4C )cn3C
+cc n1C
+c4ccccc4 )ccc3
+c4cc(Cl)cc(Cl )c4
+c3ccc( NC(=O)c4
+c2cccc(c2 )C#N
+c2ccc(c(F)c2 )c3
+c1cc(cc(c1 )C(=O)N[C@@H](Cc2ccccc2)
+[nH] n1)C(=O)N
+[C@H]3 [C@H](C
+[C@H]2CN(CCc3c([nH]c4ccccc34)[C@@](C2)(C(=O)OC)c5cc6c(cc5OC)N(C)[C@H]7 [C@](O)([C@H](OC(=O)C)[C@]8(CC)C=CCN9CC[C@]67[C@H]89)C(=O)OC
+[C@H]2 C(=O)N[C@@H](C
+[C@H]1 C(=O)O
+[C@@H]( NC(=O)[C@H](C)NC(=O)
+Nc1nc( SC
+N=C (NC(=O)
+N( \N=C\
+N c1cccc(c1)c2
+N CCO
+F)c(Cl )c3
+Cc1onc( c1)C(=O)N
+C[C@@H]1 O[C@@H](
+COc1cccc (CC(=O)N
+CO 3
+CN1N(C(=O)C(=C1 C)N
+CN1 CCCC(C
+CN(C)C )C(=O)
+CCOC(=O) Cc1csc(
+CCCS(=O)(=O)N c1ccc(
+CC(=O)N c1
+C4CCCC 4
+C4 =C(
+C3CCCC 3)n
+C1 CCN(C
+C(=O)C=C 1
+C )C(=O)O
+=[N+]=[N-] )N
+=CC(= CC3=O)
+6)c7 ccccc7
+3CCN(C c4ccc5OCOc5c4
+3CCC(CC 3)c4ccccc4
+3C=C (C)C(=O)NC
+2=O) N
+)n2 nnnc2
+)c( F)c4
+)[N+](=O)[O-] )[N+](=O)[O-]
+(c3ccccc3)c4ccccc4 )cc1
+(OC(=O)C )C1
+(F)(F )C3
+c3ccccc3 )c2c1
+c3ccc( OCC(=O)O)
+c2nc( nc(n2)N
+c1ccccc1 )/
+c1ccc(Cl)cc 1)C(=O)
+c(O) c(C
+[C@H](O [C@@H]9
+[C@H](C(C)C)N(C)C(=O) [C@H](CC(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)
+[C@@H]3C [C@H](CN
+SC(=N C2=O)
+Oc1cccc( c1)C(=O)N
+O[C@H]( [C@@H](O)
+OCC OCCN
+NC(=O)[C@H]1 CCCN1C(=O)
+NC(=O)CN 2C=N
+N(C)C(=O) CC
+FC(F)(F)c1 cccc(C
+Cl C1=C(N
+Cc1ccc( F)c(
+C[C@H](N S(=O)(=O)
+CS(=O)(=O) c1ccc(cc1)C2
+COc1ccc2c(c1)c (CC(=O)N
+COC(=O)c1 c2
+CN(C) \C=N\
+CCO \N=C\
+CCN1C(=O) [C@H]2[C@H](N
+CC1Cc2ccccc2 N1C(=O)
+C1 Cc2ccccc2
+C(=O)N )NC(=O)CNC(=O)[C@H](CC
+Br )CC
+5 )cc3
+3=O) N
+3)c4ccc5 OCOc5c4
+)c4 cccc5
+)c3cc( cc(c3
+)c3 cccc(Cl)c3
+)c2cc( [nH]c2C1=O)
+)c1 C(=O)
+)S 1
+)C c2cc1OC
+n3cncn 3
+n 9
+cc7OCOc7cc2 6
+cc(OC )ccc5
+c6ccccc6 c5
+c3n cc(C
+c3ccc4 OCC
+c3ccc( \C=C\
+c3cc( [nH]
+c3c(C) cccc3C
+c2o nc(C)c2
+c2nc3ccccc3 s
+c2ccc3c(\C=C\ c4ccccn4)n[nH]
+c2ccc3 nc(oc3c2
+c2cc1 OC)C(=O)
+c2c(cn n2
+c1n [nH]c(
+c1ccccc1 Nc2nc(N
+c1cccc2 ccccc12
+c1ccc(Cl )c(c1
+\C(=N\ NC(=S)N
+\C(=C(/ C)\
+[C@H]6 O[C@H](C
+[C@H]2O[C@H]([C@H](O) [C@@H]2O)n3cnc4
+[C@@]5(C [C@H]5C=C
+[C@@]23 C)
+P(=O)(O)O) P(=O)(O)O)
+Oc4 ccc(Cl)cc4
+OCC(O)CNC (C)(C)C
+OC(F)(F) F)c4
+O)C 5
+NC(=O)c1 c2
+N3 C(=O)N
+N(C (C(=O)N
+C[C@@H]1 CN2
+COc1 cc(C)ccc1
+CCCCCCC (C)(C)
+CCC(C) (C(=O)O
+CC(=O)N c1cccc(c1
+CC(=O)N CCCC[C@H](NC(=O)[C@H](C
+4)C(=O) C
+2CCCC 2)C
+2CCC(CC2)N 3C
+2CCC(CC 2)c3ccccc3
+1 )\
+)n n5
+)c(C(=O) OCC
+)NC(=O)C (C)(C)
+)CC C(=O)N
+)C(=O)N[C@@H](CO)C(=O)N [C@@H]([C@@H](C)
+)C S
+) [C@H](NC(=O)C
+occc 1
+ncn(C (C)C)c3
+nc2 O
+nc(N \N=C\
+nc(C) cc(
+c4cccc(c4 Cl)C(F)(F)F
+c3c1 2)c4
+c2ccccc2 )C(=O)NC
+c2ccc3 OCC(=O)N
+c2 cc(Cl
+c1nn n2C
+c1ccc( O)c(c1
+c(C)c(s c1
+[nH]n cc5
+[nH] ccc8c7
+[S+]([O-] )CC(=O)N
+[C@H]4 CO
+[C@@H]3 O)N
+[C@@H](OC(=O) CC
+[C@@H](O)[C@@H](O [C@@H]%1
+[C@@H](CC (=O)O)NC(=O)
+Oc3cc(O) ccc3
+N( [O]
+C[C@H](CO)N1C[C@H](C) [C@@H](CN(C)S(=O)(=O)
+C[C@@]1 (CO)
+COC(=O)N [C@H](C(=O)N
+COC(=O) C[C@H]1
+CN(C)C c1ccccc1
+CN 2CCN(CC
+CCc1 cc(C
+CCN(CC(=O)O) CCN(CC(=O)O)
+CCN(CC CCCC
+CC(C) (C)c2
+CC OCC(=O)N
+C(=O)O) C(C)(C)
+C(=O)N[C@@H](CCC(=O)O) C(=O)N[C@@H](C
+C#N )c1
+3C 2
+3)c4 ccccn4
+2C(=O) c3ccccc3C2=O)
+2)S(=O)(=O) N
+1CCC (=
+)n3 cn
+)n c2n1
+)C(=O)OC )C(=O)OC
+)C(=O)N[C@@H](CCCCN )C(=O)N[C@@H](CCC(=O)N
+)C (CC(=O)N
+(N =C2
+(N (CC
+( \C=C\
+s 1)C
+cccn 5
+c5ccc( N)n
+c4cc(Br )ccc4
+c3c2 C
+c2nc( S
+c2nc( O)c3
+c2cccc(c2 )C(=O)
+c2ccc3 [nH]n
+c2ccc(cc2 )C(=O)NC
+c2ccc(Cl)cc 2C(=O)
+c2 cc(Cl)c(O)
+c1ccc(cc1)c2 cc3
+c1cc( o
+\C(=N\O)\ NC
+\C(=C/ 2\
+[C@H]5C(C)(C) [C@H](CC
+[C@H]3CC[C@@H](C 2)N
+[C@@H]9 CCCN
+[C@@H]1C [C@H](CN1C(=O)
+[C@@H]1C [C@@H](O)
+S(=O)(=O) Cc2
+P1 (=O)
+OP(=O) (O
+NC(=O)[C@H](CO)NC(=O) [C@@H](NC(=O)[C@H](CC
+NC(=O)C=C )c3
+NC(=O) [C@@H](
+N [C@@H](
+Fc1ccc( CC2=N
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+CC[C@H]1OC(=O)[C@H](C)C(=O)[C@H](C)[C@@H](O[C@@H]2O[C@H](C)C[C@@H]([C@H]2O)N(C)C) [C@@](C)(C[C@@H](C)C(=O)
+CCCN 3CCOCC3
+CCC c3ccccc3
+CC(C) N1C
+C#N )C(=O)
+C =CC(=NC
+=[N+] (/
+=C6C [C@H](CC
+6CC 7CC
+6 (CC
+2) NC(=O)[C@H](CC
+1CCN(CC1)c2 ccc(cc2
+1)c2 ccncc2
+)ccc3 F
+)cc2C(F)(F)F) CC1
+)c(N )c2
+)c(C) nn2
+oc( S
+o cc(
+nn 3CCCC
+n4 nn
+cc(Cl)ccc1 4
+c5ccc(Br)cc 5
+c5 )C(F)(F)F
+c4ccc(Cl)cc4 )cc3
+c3cnn( c3
+c3ccccc3)n c(N
+c3ccc( S
+c2o c(C)cc2
+c2ncc( cc2Cl
+c1cn( nn1)c2
+c1cccs 1)C(=O)
+c%2 3
+[n+]2 ccc(cc2
+[C@@]4(C)[C@@H](CC[C@]5(C) [C@@H]4CC
+O[C@@H](O [C@@H]2
+OC1 =N
+O=C(N CCCN1
+Nc1 c(s
+NS(=O)(=O) c1cc(
+NC(=O)CC #N
+N3C(=O) C=C
+Fc1 cc(Cl)ccc1
+F)c( cc1
+CSCC [C@H](NC(=O)C
+CN(CC CNC(=O)
+CCc1 c(
+CCOC(=O)C1 CCCN(C
+CCN1 C=C(C(=O)
+CCCN1 CCC
+CCCCCC (O)
+CCCC1 =C(C
+CC(C)c1ccc2 c(CC
+CC(=O)N S(=O)(=O)
+C=C(N =C2
+C3CCN(CC 3)C
+C1=O) C(=O)OC
+Br c1cn
+5) C
+4CCC [C@H]4C
+2 N=C(CC2
+)ccc2 c1
+)c5 ccncc5
+)NC(=O)C )C(=O)N
+)C(=O)OC c3ccccc3
+)C(=O)N[C@@H](C O)C(=O)O
+(=O)N)NC(=O)[C@H](CC C(=O)N
+n5 c(cc6
+n2cn cn2
+cc1 C
+cc( I
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+c4c3 )CC2
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+c2cc1 O)
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+c2 cc(OC)c(OC)c(OC)c2
+c1cccc( Br)c1
+c1ccc( F)c(F)c1
+c( OC(=O)
+\C )C
+[C@H](O) [C@H]1
+[C@H](CC C
+[C@@H](O [C@@H]6
+[C@@H](C O
+S(=O)(=O) \C=C\
+Nc1n ccc(
+NC(=O)C2 (C)
+N=C (c3ccccc3)c4ccccc4
+Clc1ccc(cc1 Cl)C(=O)N
+Cc1n csc1
+CO B(O)
+CCNC(=O)N c1nc2
+CCN(CC)CC NC(=O)
+CCCNC(=O) \C=C\
+CCC (N)
+CC1 Cc2
+CC(C)C[C@H](NC(=O) [C@@H]1CCCN1C(=O)C
+=CC(=O) CCC4
+=C4 )C3
+=C(N3 [C@H](SC2
+5CCOCC5 )cc4
+3CCC(CC 3)C(=O)NC
+3CCC(C)CC 3
+1CCCC 1)C(=O)
+)C4 CCN(CC
+)C(=O) c12
+) ccc6
+(C)C )cc3
+c4ccccc4)cc3 C1=O
+c4cc(F)cc( F)c4
+c4 cocc4
+c3cn c(nc3
+c3cn (Cc4ccccc4
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+c3 cc(Cl)ccc2
+c3 [nH]2
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+c(O) cc1
+c(N)ncn c12
+[C@H](OC(=O)C)[C@@H](OC(=O)C) [C@@H]1OC(=O)C
+[C@@]3(C) O
+[C@@H]1 [C@H]([C@H](O)[C@@H]2O)n3cnc4
+Oc5c(O) ccc(C[C@@H]1
+OC1 (CN
+NC(=O)[C@H](CO) NC(=O)[C@H](CC
+Clc1ccc(N C(=S)N
+Cc1cco c1C(=O)N
+COc1cccc2 C(=O)
+COC(=O)c1 c(C)
+CCN1 \C(=C\
+CCCN(CC 1)C(=O)
+CCCC [C@H]1N
+CC1= CC(C)(C)N
+CC(C)C[C@H](NC(=O) OCc1ccccc1
+CC(C)(C) NC(=O)C1
+CC(C) Oc1cc(
+CC(C) OC(=O)C1=C
+CC c1ccc(cc1)N
+CC COc1ccc(
+C1 CC(CC
+C(=O)O) /
+C(=O)O CCC
+C )n
+=O)N )n
+4)c5 cccs5
+3CCCCC3 )cc2
+)n2 3
+)cc2 4
+)c(F) c1
+cc(C) nc(N
+c4nc5ccccc5 s4
+c4ccc(N )cc4
+c3c[nH] c4
+c2ccc(cc2)S(=O)(=O)N 3CCOCC3
+c2c(F)cc cc2Cl
+c1onc( n1)c2
+c1ccc(Nc2n ccc(
+c(Cl )c2
+\C(=N\ OC)\
+[Si](C)(C)C (C)(C)C
+[C@H]( OCC
+[C@@]5 (O)
+[C@@H](O)C[C@@H](C (C)C)C(=O)N
+[C@@H](O [C@@H]4
+S(=O)(=O)N c3onc(C)c3
+O[C@H](CO)[C@@H](O)[C@H](O) [C@H]9
+Nc1n cc2
+F c1cnc(nc1
+Cc1ccccc1 N2CCN(CC
+CN(CC )CC
+CCn1 ccc2
+CC(C =C3)C2
+6 )C(=O)O
+3CCCCC3 )c1
+3CCC 1
+2CCCC 2)cc1
+2)C3 CCN(CC
+1 OC
+)c7 ccc(F)cc7
+)c( cn2
+)C(=O)N [C@@H](C)C(=O)N[C@@H](CC
+(c5ccccc5 )c6ccccc6
+(O CCN
+%3 4
+nc(C )c(s
+ccccc6 c5
+cc5OCOc5 cc4
+cc2 N=CN(C)C(=O)
+c3nc( nn1
+c3cc4ccccc4 cc3
+c2ccccc2 )CC
+c2ccc(C )c(c2)S(=O)(=O)N
+c1 c(C
+[nH]3 )c5
+[C@]7 8
+[C@H]1CC [C@]2(C)
+[C@H]1 OC
+[C@@H](O [C@@H]1
+[C@@H](C [C@H](N)
+OCC O
+O) ccc1O
+O c2n
+NC(=O)OC (C)(C)C
+F)C (=N)N
+F) nc3
+CS c2nn
+CN1C(=O)C(=Cc2 cnc(N
+CCc1 nc(
+CCCC 3
+CC(C)(C)OC(=O)N[C@@H](C c1ccccc1)
+CC(=O)N[C@@H](C O)C(=O)N[C@@H](CC
+C2 CO
+C1= NC(=O)N(C
+4CCN(CC4 )S(=O)(=O)
+3CCC (=CC
+3C =CC=CC
+3)C(=O) c4ccccc4
+2CC3CC (C1)CC(C
+)nc( SC
+)cc3C 2
+)c4 c[nH]
+)c(Cl )c5
+)C(=O)N[C@@H](CCCN=C(N)N )C(=O)N[C@@H](CCCCN
+)C(=O)N[C@@H](CCCCN )C(=O)N[C@@H]([C@@H](C)O)C(=O)N
+)C(=O)N(C 2=O)
+)C(=O)N 3CC
+)C(=O) C1=O
+(F)(F)C l
+sc( nc2
+n c1
+cn 1C
+cc(Br )c(N
+c9 %10
+c4nc5ccccc5 [nH]4
+c4ccccc34 )c1
+c4ccc(Cl)cc3 4
+c3s cc(
+c3n c5
+c3cccc4ccccc3 4)
+c3c(O) cccc3
+c2n cc[nH]
+c2cnn(C )c2
+c2ccc3 ncccc3
+c2ccc(NC(=O) c3ccccc3
+c2c(O) ccc3
+c2 cccnc1
+c1ccccc1)NC(=O)[C@H](C c2ccccc2)NC(=O)[C@H](CC
+c1ccccc1 Cl
+\ CC
+[nH]c(nc2c1 )c3ccc(cc3
+[C@H]3 [C@@H]1
+[C@H]1CC[C@@]2(C) [C@@H]3CC
+[C@@H]4 O
+[C@@H]3 [C@H](CC
+[C@@H](C N(C)C(=O)
+Oc1n c2ccccc2
+Oc1c( ccc2
+OC(=O)C NC(=O)
+NC(=O)CSC2=N c3ccccc3
+NC(=O) CCN2C(=O)
+N=C(N )NC(=O)
+N(CC c3c2[nH]
+N CCC(=O)N
+C[C@H]1 CO
+C[C@H](Nc1n cnc(N
+C[C@@H](C N1
+COc1ccc(cc1)C2=N N(C(C
+COc1cc(cc(OC)c1O)[C@H]2[C@@H]3[C@H](COC3=O) [C@H](N
+CC[C@H](C)[C@H](NC(=O) [C@@H](NC(=O)[C@H](CC
+CCN 2CCOCC2
+CCCC1 =CC(=O)Oc2
+CCCC CCCNC(=O)
+CCC(= C2
+CC [C@H]1C
+C(=O)N(C) C(=O)N(C
+C(= Cc1
+6CC7CC (CC(C
+6CC7CC(CC(C 7)C
+3)c4ccccc4 )c5ccccc5
+2C=C(F)C(=O)N C2=O
+2)c3 c[nH]
+2 \C
+)c4 cccc5ccccc45
+)c(Cl )c4
+)c(C) cc3
+)NC(=O)CNC(=O)[C@H](C c3ccccc3)NC(=O)
+)N=C2 S1
+)N6 CCCC
+)N 4CCCC4
+)C(=O)N[C@@H](C c6ccccc6
+)C l
+) [C@H](O
+(CC (O)
+(C )c4
+n4 5
+n3 nc(
+cccs 6
+cccn 5
+ccc( cc5
+c3n cc(Cl)c(N
+c3ccccc3)n 2C
+c3ccc(O) cc23
+c3cc( OCC
+c3c( F)cc(
+c3C=Cc( [nH]3
+c2sc( cc2
+c2o c(N
+c2cn cn
+c2ccccc2 C(=O)O
+c2cc(ccc2 OC
+c2c(C (=O)N
+\C=C/ CCCC(=O)O
+[O-][N+](=O) c1oc(cc1)C(=O)N
+[N-]=[N+] =N
+[N+](=O)[O-] )\
+[N @@+]
+[C@]12CCC(C)(C)C [C@H]1C3=CC
+[C@H]4 CCCN
+[C@H](O)CC [C@]4(C)[C@H]3CC[C@]12C
+[C@H](CC(C)C)C(=O)N [C@@H]1[C@H](O)c2ccc(Oc3cc4cc(Oc5ccc(cc5
+[C@H](CC C(=O)N
+O[C@H](C)C[C@@H]( [C@H]4
+O)cc1 O
+O) c2ccc3
+O c1ccc(cc1)C
+COc1 c(O)cc2
+CN(C)C 1=O)
+CCn1c2ccccc2 c3cc(
+CCOC(=O)c1 sc(N
+CCN1 CCN(CC1)C
+CCN c1nc(
+CC2CCCO 2
+CC2 (C
+CC(C)Oc1ccc(cc1 C#N)c2onc(n2)c3
+CC(C)C[C@H](NC(=O) [C@@H](NC(=O)
+CC(C) Nc1
+CC(C) NC(=O)c1
+CC \C=C\
+C) S(=O)(=O)C
+C(=O)c3ccccc3C(=O) c2c(N
+C [C@@H]2
+= CCCCC1
+4CC SCC
+2)\C #N
+1)N2CC c3c2nc(nc3c4cnc(N)nc4
+)c3 nc(cs
+)c( nc3
+)NC(=O)C NC(=O)
+)C 6CC6
+ncc1 C(=O)N
+n3 ccc4
+n2 nc(C)cc2
+cncn 4
+c5 c(F)cccc5
+c3c( cccc3
+c2nnc( SC
+c2ccc(OC )cc2)cc1
+c2ccc(O)cc2 )NC(=O)[C@H](CC
+c2cc( n[nH]
+c2 3)C(=O)N
+c1)c2 ccccc2
+c(cn n3
+c(cccc1 2)C(=O)N
+c(S CC(=O)
+c(F)cc 2
+c%3 0
+[C@](O)(C O)
+[C@H]4 C=C
+[C@H]2CCCN 3CCC
+[C@H](N)C(=O)N1CC [C@H](F)C
+[C@@]6 (CC
+[C@@]2(CC 1)C
+OCCO c5c4
+O=C(N c1cccc(c1)c2
+O=C(N 1CCOCC1)c2
+O=C(C 1CC1
+O NC(=O)C
+NC(=O) n1cc(
+Cc1ccc(C) n1
+COc1 ccc(Cl)cc
+CO [C@]12CC
+CCNC(=O) OC
+CCNC 3
+CCCC CCOc1
+CCC1 (CC)C
+CCC #N
+CC1=CC=C N2C(=O)
+CC1 )C
+CC(C) Oc1ccc2
+C#N )\N
+5CCCCC 5)
+4CCCC4 )cc3
+4)n cnc23
+3CCCO 3
+3CCCCC3 )c2
+2c3ccccc3 S
+2CCC (C2)
+2)C (C)C
+)c5 nc6ccccc6
+)c5 cc(Cl)ccc5
+)c4cccc( F)c4
+)c3 ccccc1
+)c2n oc(
+)c2n cccc2
+)[C@@H](C) O)C
+(F) (CC
+(CN(C 2CC2)C(=O)
+(=C )C(=O)
+s c3ccccc3c2
+noc( n2)C3
+c4ncccc3 4
+c3ccccc3)n n2
+c3ccc(F)c(Cl)c3 )c2cc1
+c3 c(CN
+c2ccc( NC(=N)N
+c1cc(O) ccc1
+c(Cl)cc( cc2Cl
+\C=C \C=C\C=C\
+[C@]3(C)[C@@H]2 C(=O)C=C4
+[C@H](N C6=O)
+[C@@]3 4CC
+[C@@H]3C [C@@H]3C
+[C@@H]2CC [C@@H](CCN
+[C@@H]1 CCN(C1)c2
+[C@@H](O) [C@@H](O)C
+[A s
+Oc1 c(CN
+O[Si](C)(C)C(C)(C)C )
+OC(=O)C )C(=O)
+OC c1ccc(C
+O=S(=O) (C
+O)N (C)C
+NC(=O)c2 [nH]
+N(C)C 1=O)
+Clc1ccc( \C=C\C(=O)N
+C[C@H]( NC(=O)N
+COc1ccc(OC )c(N
+COc1cc(C c2cnc(N)nc2N
+CNC(=O)c1c(oc2cc(N(C)S(=O)(=O)C)c(cc1 2)c3ccc4
+CNC(=O) c1cccc(Nc2ncnc(n2)N
+CCCCC (
+CCC(CC 1)C(=O)N
+CC c4ccccc4
+C(=O)c5ccccc5 C(=O)
+C(=O)N(C 3=O)
+Br c1ccc(N
+=[N+] ([O-]
+5CC6CC(CC(C6)C 4)C5
+4CCCCC 4)N
+4) cc(OC)c1OC
+2=N S(=O)(=O)
+2)c3ccccc3 )c1
+)cc2 F
+)c3 ccc(C(=O)N
+)C2CCN(CC 2)C(=O)
+)C(=O)NCC C(=O)O
+)C =C3
+(C)C)NC(=O)[C@H](C c1c[nH]cn1)
+%3 2
+n3 cccn3
+n (CCC
+cncn c3
+cc(c4 )C(F)(F)F)C(F)(F)F
+c6ccccc6 c5
+c4ccccc4 CC
+c4ccc5 OCC(=O)N
+c3cs c(N)n
+c1ccc( NC
+\C=C/ CCCC(=O)O)
+[nH]ccc8c7 )c4
+[n+]2 ccc3
+[C@H]2CN(CCc3c([nH]c4ccccc34)[C@@](C2)(C(=O)OC)c5cc6c(cc5OC)N(C)[C@H]7[C@](O)([C@H](OC(=O)C)[C@]8(CC)C=CCN9CC[C@]67[C@H]89)C(=O)OC )C1
+[C@H](O) [C@H](C)
+[C@H](CCCCN)NC(=O) [C@@H]4CCCN4C(=O)
+[C@H]( [C@H](C)O
+[C@@]56C [C@@]46CC
+[C@@H](C) OC(=O)[C@H](C
+[C@@H](C OC(=O)
+OP(=O)(O)O) [C@@H](O)
+O=C(N 1CCC
+O [C@@]2(C)
+Cc1n cc2
+C[C@H](N (C
+C[C@H](CO)N1C[C@H](C) [C@@H](CN(C)C(=O)N
+C[C@@H]1O[C@@H](O [C@H]2
+COc1ccc(OC )c(CN
+CO[C@H]1 O[C@H](C
+COC(=O)C1=C(C[C@@H]2CC[C@H]1 N2
+CC[C@H](C)[C@H](NC(=O)[C@H](C c1ccccc1)NC(=O)[C@H](CC
+CC[C@H](C)[C@H](NC(=O)[C@H](C c1ccc(O)cc1)NC(=O)
+CCN1CCCC 1C
+CCN(CC )C1
+CCC(=O)N (C1
+C2= NC(=O)
+C1=C (C)
+C(=O)N =C(N)N
+5CCOCC5 )cc4
+3)c4 cccc5
+2CC 3CCC
+)c3 cn(nn
+)c2 cccnc2
+)C(=O)N[C@@H](CO)C(=O)N[C@@H]([C@@H](C) OP(=O)(O)
+)C(=O)N[C@@H](CC CN
+) P(=O)(O)
+(=O) [O-]
+n2 )C(=O)NC
+cn ccc1
+ccccc1 2)
+cc(CN (CC#C
+c9 c[nH]
+c4n cccn4
+c4ccccn4 )cc3
+c4ccccc4 OC3=O)
+c4c3 )cc1
+c4 cc(OC)c(OC
+c3n ccn3
+c3cn( nc3
+c3ccccc3 O)
+c3ccccc3 )C(=O)N
+c3ccc( OCCCN
+c3cc(C)o n3
+c2s c3ccccc3
+c2cn n3C
+c2ccccc2 C3=C1
+c2ccc(cc2)[N+](=O)[O-] )cc1
+c2ccc( cn1
+c2c(OC )c1OC
+c2c( [nH]c3
+c2N )c3
+c2 ccc(Cl)c(
+\C(=N\ OC
+[S+]([O-] )
+[C@]4(C)C3CC [C@]12C
+[C@H]2C [C@@H]1
+[C@H]( OP(=O)(O)OC
+SCC 3
+O[C@@H]( [C@@H](O)
+O) c5
+O) O
+NC(=O)c2c(C) onc2
+N(C(=O) O
+ClC (Cl)
+C[C@@H](O) [C@H](C
+COc1cccc( \C=N\
+COc1ccc(cc1 )N(C
+COc1cc( NC(=O)N
+CN CC(C
+CCCCN(CCCC )CC
+CCCCCCCC\C=C/ CCCCCCCC(=O)
+CCCCCCCC CC(=O)
+C2=C (C)N(C)N(C2=O)
+=NCCN 4
+=C( NC(=O)N
+4 )C(=O)OC
+3CCN(CC 3)C4
+3CCCCC3 )c4ccccc4
+3 )C(F)(F)F
+2)C3 CCCCC3
+)c4cncn c14
+)c4 cc(Cl)ccc4
+)c1 nc2
+)c1 Br
+(C)C)C (C)C
+nc( NC(=O)N
+n2 cccn2
+ccc2 4
+ccc %10
+c5ccc( NC(=O)
+c3c(Cl)c ncc3
+c3 C
+c2ccccc2)c3 ccc4
+c2ccc3 cc(O
+c1ccc(Nc2n cc3
+c1ccc(Cl)cc 1)C(=O)N
+c(Cl )nc(N
+[C@H]5 CCCN
+[C@@](C)(C S1(=O)=O)
+[C@@H]5CC[C@@]4(C)[C@]3(C)CC 2)C(=C)C
+[C@@H]( [C@H]2O)N(C)C)
+OC1 =C(N
+O=C(N 1CCCCC1
+Nc1n c2ccccc2
+NS(=O)(=O) c1ccc(C
+NCCCC [C@H](N
+NC(=O)C =C
+NC(=O) c3occc3
+NC(=N) c1ccc(cc1)c2
+N=C (c2ccccc2)c3ccccc3
+N C1=C
+I )
+Cc1ccccc1 CN
+COc1cccc2C(=O)c3c(O)c4C[C@](O)(C [C@H](OC
+CO CCC1
+CC[C@@]1(C [C@H]2CN(CCc3c([nH]c4ccccc34)[C@@](C2)(C(=O)OC)c5cc6c(cc5OC)N(C)[C@H]7[C@](O)([C@H](OC(=O)C)[C@]8(CC)C=CCN9CC[C@]67[C@H]89)C(=O)OC)C1
+CCOc1ccccc1 N2CCN(CC
+CCN1 C(=O)C
+CCCCN1 C(=O)N
+CC2 =C1
+CC(C) [C@H](NC(=O)N
+CC(=O) c1cc(
+CC 5
+C2CC3CC (CC1C3
+C1=N c2ccccc2
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+4 c5ccccc5
+2CCc3c(C 2
+2 [C@@H](C
+1)c3 cc(NC(=O)
+)c4 )cc1
+)C(F)(F)F) CC1
+)C(=O)N 3CCC[C@H]3
+(F)(F)F) C(=O)
+(C(=O)NO)S(=O)(=O) C
+(C c4cc5
+oc( nc1
+nc3 cc(ccc3
+ccccc4 n3
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+c4cc(cc(c4 )C(F)(F)F)C(F)(F)F
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+c2n ccn2
+c2ccccc2 S1(=O)=O)
+c2ccc(cc2)N 3
+[nH] 1)C(=O)
+[O-] .C
+[C@](C2) (c3ccc(cc3)C
+[C@H]2 [C@@]1(C)CC
+[C@H](O)[C@@H](O) [C@H]1O
+[C@@]34 C
+[C@@H]3 4
+OC)C(=O) C(=O)N
+OC c2onc(c2)C(=O)N
+O) n1
+Nc1n ccc(n1)c2
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+N3 CCN(C)CC
+N(C)C 1=O
+F)cc (Cl
+Cc1cc(O)cc(C)c1 C[C@H](N
+COc1cccc(c1 )N2CCN(CC
+COc1ccc(c(OC)c1 )c2
+CO CCC(=O)N
+CN1C(=O) CC
+CN( [C@@H](C)C
+CCNC(=O)[C@H]1 O[C@H]([C@H](O)[C@@H]1O)n2cnc3
+CCC 1C
+C4 =C2
+6CCC[C@H]6 C(=O)N
+6CCCC6 )c4n3
+6 )c4n3
+5C(=O) c6ccccc6
+4CCCC (C
+4)C(=O)N (C
+2CCCCC2 )NC(=O)
+2)c3ccc(cc3 )C#N
+2)c3 n[nH]
+)c5ccccc5 Cl
+)c2n nc(s
+)c(C =O)
+)[C@@H](C)CC )[C@@H](C)CC
+)S )cc1
+)C2=N OC(C
+(F)(F )C(F)(F)
+(C)C)C(=O) OC
+%2 3
+cc4OCOc4 cc3
+c5 6)
+c4ccccc4)n cnc23
+c4ccc(cn4 )C(F)(F)F)
+c3ccc(Cl)cc 13
+c3c[nH]cn 3
+c3c[nH]c4ccccc34 )NC(=O)[C@H](CC
+c3c[nH] nc3
+c3c4CCCC c4n
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+c2n cc(cc1
+c2ccccc2 )ccc1
+c2c[nH]cn2) NC(=O)[C@H](C
+c2 cc(Cl)ccc1
+c1ccc( OCCCC
+[C@H]5O[C@H](C [C@@H]5
+[C@H](C 1)
+[C@H]( [C@H](C)
+[C@@H]4 O[C@H](C)C[C@@H]([C@H]4
+[C@@H]3[C@@H]2CC[C@@]4(C) [C@H]3CC
+[C@@H]3O[C@H](CO)[C@@H](O)[C@H](O) [C@H]3O)
+[C@@H](C c3ccccc3)NC(=O)
+[C@@H] %20
+Oc1ccc(cc1 )
+O[C@H](CO)[C@@H](O)[C@H](O) [C@H]2
+OC3 CCN(C
+NC(=O)C2 CCCCC2
+Cl c1ccc(cc1)N2
+Cc1ncc([N+](=O)[O-] )n1CC
+Cc1nc2 cc(ccc2
+Cc1ccc( NC(=S)N
+C[C@@H]1CC [C@@]2(OC1)O[C@H]3C[C@H]4[C@@H]5CC
+COc1cccc(c1)c2 cc(ccc2
+COc1cccc( \C=C\C(=O)
+COc1ccc(cc1 F)c2
+CCc4nn(C )c(
+CCOC(=O)c1 c(N
+CCOC(=O)N 1CC
+CCO \C(=C/1\C
+CCCCCC C(=O)
+CC(C)(C)O c2nc(nc(
+CC(C)(C) c1ccc2
+CC(C) [C@@H](NC(=O)
+CC 4CCCO
+C4CCC 4
+C1=N N(C(=O)
+C(=O)O [C@@H](
+C( c1ccccc1)n2
+C )CC1
+=O) C
+)nc1 N
+)n5 nccn5
+)cc1 )\
+)c4n oc(
+)c4ccc(cc4 )c5ccccc5
+)c(O) c4
+(=C(N 3C2=O)
+cn 1)
+c5 s
+c5 n3
+c5 C(=O)
+c3ccccc1 3)c4ccccc4
+c3cccc(c3 )C(=O)O
+c3ccc(NC(=O)C )cc3
+c3ccc(NC(=O) c4ccccc4
+c2n c(C)cc(C)
+c2cn (C)c3ccccc2
+c2ccccc2 )C(=O)N[C@@H](C
+c2ccc(cc2 )N(C
+c2cc( c(Cl)c
+c1ccccc1)c2 ccc(
+c(OC )cc2
+c%2 7
+[O-][N+](=O) c1ccc(CN
+[C@H]( [C@H]1O)
+[C@@H]5CC [C@H]4C
+[C@@H](O [C@H]5
+[C@@H](C)O) [C@@H](C)O)C
+S =C1
+P(=O) ([O-])O
+Oc3 ccccc23
+Oc2 3
+O[C@H] 8
+OS(=O)(=O) [O-]
+OC(=O)C)[C@@H](OC(=O)C) [C@H](OC(=O)C)
+O 2)c4
+NC(=O)C [C@H](NC(=O)
+Cc1ccc( F)cc
+C[C@H](CC C(=O)O)
+COc1ccc(cc1 Cl
+CNC(=O) \C=C\
+CN1C(=O) C(=C(O)
+CCOc1ccccc1 C(=O)N
+CCOC(=O)c1c(C)[nH] c(C)c1
+CCCCCCCC CCCCCCCCCCCC
+CC1CC(C)CN(C 1)C(=O)
+CC1(C)CC[C@@]2(CC[C@]3(C)C(=CC [C@@H]4[C@@]5(C)C
+CC1(C)C 2CCC1
+CC(=O)N[C@@H]1[C@@H](O)C [C@@]
+C2=C (C(=O)
+C )n1
+=CC(=O)N C3=O
+=C )cc1
+6 CCC(CC
+2CCC(CC2)c3 nc(cn3
+1 )c4ccccc4
+)c2 ncn(
+)\ c2ccccc2
+)CC 2)C
+)C(F)(F)F) C(=O)O
+)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N [C@@H](C)C(=O)N[C@@H](CCCCN
+)C [C@H]1
+(C1 CC1
+(C) S
+nc( SCC
+csc( n2
+cccc1 c2
+ccc(F)cc 6
+cc(Cl)c( n1)C(=O)N
+c7ccc( O)c(
+c7 ccc(Cl)cc7
+c4ccccc4C (O)(
+c4ccccc4 )C3
+c4 c(CC
+c3ncc( s
+c3ccc4c(\C=C\c5ccccn5)n[nH] c4c3
+c2ccc( CC3
+c2cc(ccc2 Cl)S(=O)(=O)N
+c2c1 )c3ccc(cc3
+c1ccc(OC (F)(F)F)cc
+\N=C(/ NC#N
+\C(= C1
+[C@H]1C O
+[C@H](C 3)
+[C@@]1(C S(=O)(=O)N
+[C@@] %10
+[C@@H]2CC [C@@H](C
+[C@@H](CC CNC(=N)N)NC(=O)
+Oc2 cccnc2
+Oc1ccc(Cl)cc1 C(=O)N
+O[C@H](C (=O)O)
+OCC #
+OC([C@H](O) [C@@H]1O)n2cnc3
+OC )c3
+O)CC 1
+N(C=C(C(=O)O)C(=O)c3cc2 F)c4
+N 4C
+FC(F)(F) c1ccc(NC(=O)N
+Cc1onc(C) c1C
+Cc1[nH] cnc1
+Cc1 ncn
+COc1cccc( \C=C\
+COc1cc( NS(=O)(=O)C)ccc1
+COC(=O)C1=C (C)N
+COC(=O) CC1
+CO \C(=C/1\
+CN2 C(=O)N(C
+CCC2 (C)C
+CCC1 =CC(=O)
+CC(=CCC\C(=C\CC \C(=C\C
+C3 (CCCC
+C2 CCOCC
+C(C )c2
+C(C (=C(O)C1=O)C(=O)
+C(=O)O) NC(=O)[C@H](CO)NC(=O)
+C(=C (O
+5)C3 =O
+5 =CC
+4CCCCC 4)C
+2CCN(CC2)c3 nc4ccccc4
+2=C(CCC2=O)N(C1=O) c3cccc(c3)C(F)(F)F
+)c4ccccc4 Cl
+)c3ccccc3 C
+)C(=O)NC(CCC(=O)O) C(=O)O)
+)C(=O)N 1CC
+) [C@H]3CC
+(Cl )N=C(N
+( [C@H](C
+#CC #C
+oc( cc2c1
+nnc( SC
+nc(C )c4
+nc( ncc3
+n(C )c5ccccc45
+csc( n1)c2
+ccn3 c(cn
+c4n [nH]
+c4ccc(cc4)c5ccccc5 c6
+c4c3 )c5ccccc5
+c4 cc(CN
+c3cc(O) ccc2
+c3c2 )N
+c2co cc2
+c2ccc(cc2)c3 nc4ccccc4
+c2ccc(Cl)cc2 c1
+c2c(Cl)cc cc2Cl
+c2c(C) nn(C)c2
+c1ccc(cc1)C(=O) \C=C\
+c1ccc(cc1 )C(=O)O
+c1ccc( cn1
+c1c[nH]cn 1
+c1 ccn2
+\C=C/ C(=O)N
+[nH] ccc2
+[C@H]2O[C@H]([C@H](O)[C@@H]2O)N 3C=CC(=O)NC3=O)
+[C@H]2 [C@@H](CC
+[C@@H]3C[C@@H](C \C=C(/C)\
+[C@@H]3 C(=O)N
+ONC(=O) CCCC
+OCC OCCO
+OC2 (C)
+OC(=O)C [C@@H](O)
+OC 7
+OC (F)F)c2
+O=C1NC(=O)\C(=C\ c2cnn3
+NS(=O)(=O) c1cccc(
+F )C1
+Cc1occc1 C(=O)N
+Cc1cn n(
+COc1ccc( S
+COCC COc1cc(C
+CN1C(=N)N [C@@](C)(CS1(=O)=O)
+CCc1 cc(CC
+CCS(=O)(=O) c1ccc2
+CCN c1nc2
+CCCCCCC C
+CCC 5
+CC(C)C1 =CC
+CC(C S
+C(=O) c2ccccc2
+C(=C) C
+6 /
+3)C 4CCCCC4
+2C=C (C)C(=O)NC
+)cc 4Cl
+)C(=O)N[C@@H](CCC(=O)O) C(=O)N[C@@H](CCCCN
+)C N(C
+(O) C(F)(F)F
+n2c(C 1
+n c7
+ccc(Cl)cc 3Cl
+c6ccc(F)cc 6
+c4ccccc4 )\
+c4cccc(c4 )C#N
+c3n2 C
+c3 cc(Cl)
+c2 cc(C)c3
+c1c[nH]c2ccccc12) (NC(=O)
+c(nc( nc3
+c(C)cc( cc2
+[C@]2(C) [C@H](CC
+OC5 =O
+OC(=O) c1cccc(c1)c2
+O=C1 \C(=C\
+N2 C(=S)N
+Fc1cc( Br)ccc1
+FC(F)(F) c1cc(ccc1
+F) n1
+Cl )C(=O)NC
+Cc1ccc( c(C)c1
+COc1ccc(cc1OC )c2cc(
+COc1cc(N )c(Cl)cc1C(=O)N
+CN(C)C (C
+CCN 5CCOCC5
+CCCCCCCC c1ccc(
+CCCCC \C=C/C\C=C/
+CCC [C@H](NC(=O)[C@H](CC
+CC(C) N(C(=O)
+C3=O) C5(C)C)C
+C2=N c3ccccc3
+=NCCN 5
+7 OO
+3)n c1
+1CCC[C@H]1 C(=O)N[C@@H](C
+)c4 nc(
+)C(=O)O) C(=O)
+)C(=O)N CCCC
+(C) C(=O)O)
+(=C )C(=O)O
+ccccc2 5
+c5ccccc5 C4=O)
+c4cc(C) o
+c4c(C)onc4 C
+c2cccc( NC(=O)N
+c2ccc3c(c2 )n
+c1o c2ccccc2
+c1ccc(Cl )c(c1)C(=O)N
+c1ccc( OC(=O)
+c1 cc(Cl)cc(Cl)c1
+c(ccc1 c2
+c(C) n3
+[nH] c4ccccc4
+[N+](=O)[O-] )c2
+[N+] (CC)
+[C@H]2 [C@H]1C
+[C@H]([C@H](C)O 1)C3
+Oc4 cc(O)cc(O)
+OCO c8
+OC(=O)C) OC(=O)C
+N=C( SCC(=O)N
+FC(F)(F)c1 cccc(CN
+Cl. CN1CC
+Cc1ccc(cc1 C)S(=O)(=O)N
+Cc1cc( NC(=O)
+C[C@]1 (CC
+CN1 c2ccccc2
+CN1 CCOCC
+CCOC(=O) c1ccc2
+CC(=O)O[C@@]12CO[C@@H]1C [C@H](O)
+5C [C@@H]6
+5C 4
+5 )cc2
+4CCCC 4)c5
+4) ccc2
+3CCC[C@H]3C #N
+3)c4 ccc(C
+2CCCC2 =O
+2CCC3(CC2)N (CN
+)s cc1
+)cc4 5
+)c3 cc(Cl)ccc3
+)c( n1)C(=O)N
+) [C@H](OC(=O)
+(C)C)NC(=O)[C@H](CC CCN)NC(=O)[C@H](CC
+(=O)O)NC(=O)[C@H](CC (=O)N)NC(=O)[C@H](CC
+ncnc3[nH] ccc2
+n2 ccc(
+csc( n3)c4
+cs c1n
+ccc(Cl )c(N
+cc(ccc4 [nH]
+c4ccccc34 )C(=O)
+c3cccc( n3
+c3ccc(cc2 3)C
+c2o c(CN
+c2ccc(OC(F)(F)F)cc 2
+c2 n1)c3
+c1ccc(cc1 )C(=O)NC
+c1ccc( OCC(=O)O)
+\N=C\ c2oc(cc2
+[nH]1 )C3(O)
+[O-][S+] (C
+[C@H]5 CCC
+[C@H](O)[C@H](O)C O)C(=O)O)
+[C@@H](C c2ccccc2)C(=O)N
+OC[C@H]1O[C@H]([C@H](O) [C@@H]1O)n2cnc3c(N
+OC (C)(CC
+NC(=O)C1 CCN(CC1)C(=O)
+Fc1ccc(cc1)N 2CCN(CC
+Cl )n1
+Cc1oc(C )c(c1)C(=O)N
+Cc1ccccc1 NC(=O)N
+Cc1cc( Br)ccc1
+COc1ccc(OC )c(C
+COCCCOc1cc(C [C@@H](C[C@H](N)
+COC(=O) [C@]1(C)CCC
+CN c1cc(
+CCOC(=O) CS
+CCNC(=O) c1cc(
+CC(C)(N )C(=O)N[C@H](C
+C=C N(CC
+C4 5
+C1 CCC(C
+C(=O)C(=C (Oc2c1
+Br )C#N
+=C(C(=O) OC
+=C( SC
+5CCOCC 5)
+2CCC(CC2)N3CC(CC#N)(C3)n4cc(cn4)c5 ncnc6[nH]ccc5
+2)c3 ccccn3
+)n cn2
+)cc3 )cc2
+)c3n ccs
+)c1C#C c5ccc(N)n
+)S(=O)(=O)N c5
+)C2 CCN(C
+)C2 )c1
+)C(=O)C (C)
+(C)c2 ccc(
+(C)C )c2ccccc2
+%3 6
+nc(O) c(O)
+nc(C S
+cc(Cl)c( cc2
+c5ccc6ccccc6 n5
+c5ccc( Br)cc4
+c4nccs 4
+c3cccc4 cn
+c3ccc(F)cc3 )cc2
+c3cc(Cl)cc(Cl )c3
+c3c2 )S(=O)(=O)
+c2n ccn2C
+c2n c(C)c
+c2cccc( OCc3ccccc3
+c2 C1
+[C@H]6 C(C)(C)
+[C@H]2O [C@@H]3
+[C@@]2 (C)C3
+[C@@H]1NC(=O) [C@@H](C
+O[C@H](CO)[C@H](O)[C@H](O) [C@H]1O)
+OP(=O)([O-]) [O-]
+OCC 6
+NC(=O) c1ccc(cc1)c2
+N [C@H](CN
+C[C@]12CC [C@H](O)
+C[C@@H]1CN2 [C@H]([C@H](C)O1)C3
+COC(=O)c1 cccc2
+CCOC(=O) \C(=C/
+CCO c1ccc(cc1)N2C(=O)
+CCN1 CCOCC
+CCN(CC CO
+CCCCC NC(=O)N
+CC(C)(C) S
+CC(C) (C(=O)N
+C3 CCCN(C
+C2 CC3
+C(=O) CCC
+=C(N C
+4CC5CC(CC(C5)C3 )C
+3)c4ccc(cc4 )C(F)(F)F
+2C(=O) C(=N
+2) cc(OC)c1OC
+2 =CC(=O)
+)N2CC (CC2=O)
+)C3 =C(N
+)C(=O)N[C@@H](C c5ccc(O)cc5
+) OC1=O
+(c2ccccc2)c3ccccc3 )CC1
+(O)C (C)
+( \N=C\
+( Oc1
+nc(N)n c3
+n3 nc(C
+ccc(Cl)c (C(=O)
+c4cccc(c4 )c5
+c4ccc(Oc5ccccc5 )cc4
+c3ccccc3 )C4
+c3cccc(N )c3
+c2nn c(CC
+c2cn c3[nH]
+c2ccnc3 [nH]c(cc2
+c2ccccc2 C(F)(F)F)
+c2cc(C) nc3
+c2 c(Cl)ccc(C
+c1n ccc
+c1cnc2nc(N)nc(N )c2n1
+c%2 5
+[n+] (CC(=O)
+[C@]8 (C)
+[C@]2 (O)CC
+[C@H](O [C@H]3
+[C@H](O [C@@H]%10
+[C@H](CC (C)C)N
+[C@@]36 O
+[C@@H]3 OC(=O)
+[C@@H]2 [C@H](
+[C@@H](O) CO
+[C@@H](C)C(=O)N[C@@H](C (C)C
+[As ]
+Oc1 ccc(C(=O)N
+O[C@H](C OC(=O)C)
+OC2 (CC
+O)c( c3
+O) c%12
+O 5)
+NC(=O)C2 CN(C
+FC(F)(F)c1cccc(c1 )N2CCN(CC
+FC( F)C
+F)c(F)c(F)c( F)c3
+Cc2 ccc(C)cc2
+COc1ccccc1 OCC(=O)N
+COc1ccc2 c3
+COc1ccc( CC(=O)O)cc1
+CN(C) CCN1C(=O)
+CCCCOC(=O)N S(=O)(=O)
+CCCCCCCC CN
+C(F)(F)F )C(F)(F)F
+C(=O)N CCCC
+C(=C (S
+=C (CC
+4CC C(=O)N
+4)c5ccc( F)c(
+)nc3 cc(C(=O)N
+)n (CC
+)c5 cccc6
+)c4ccccc4 )c5ccccc5
+)c4 ccc(C)cc4
+)c3c2 C
+)c2c1 O
+)c2 C1
+)\ c3ccccc3
+)S(=O)(=O) c2ccccc2
+)C(C)C )cc1
+)C(=O)N[C@@H](CCCNC(=N)N )C(=O)N[C@@H](C(C)C
+(C)C(=O)N C4=O)
+(= N1)N
+( [C@@H]2
+sc( nc2c1
+o nc(N
+n3c2 C1=O
+n3 cccc3
+cccn3 nc(N
+ccccc2 4)c1
+c3cc2 F
+c2cc(O)c(O) c(O)c2
+c2c( F)cc(
+c1ccccc1 OC
+c1ccc( O)c(
+c(c(C) nn2
+c(Cl)cc cc2Cl
+c(C#N )c2
+c( c5
+[C@] %10
+[C@@](C) (O
+[2H]C( [2H])([2H]
+S(=O)(=O)c2c1 )C3=C(O)
+Oc1ccc( F)cc1
+O[C@H](CO) [C@H](O)[C@H](O
+NC(=O)c2nc( cnc2N
+Fc1ccc(C (=O)N
+Cn1ncc( NC(=O)c2nc(s
+C[N+]1 (CC
+C[C@@H](O) [C@H](NC(=O)[C@H](C
+COc1ccc(cc1)c2 no
+COc1ccc(cc1)c2 nn(cc2
+COc1ccc( \C=C(\
+COC(=O)N 1CC
+CNC(=O) c1ccc2
+CN C1=O)
+CC[N+] (CC)
+CC[C@]1 (O)C
+CCN(CC)CC n1
+CCCC Oc1ccccc1
+CCC(C) N1
+CC1 CCN(C
+CC(C)c1cccc(C(C)C)c1 NC(=O)N
+CC(C)C[C@H](NC(=O)[C@@H]1CCCN1C(=O)C )C(=O)N[C@@H](C
+CC(C) [C@H](NS(=O)(=O)
+C2CCCCC2 )c3
+C2 CN(C
+C1 2CC3CC(CC(C3)C1)C2
+C )c3ccccc3
+=C( \C=C\
+4)c5ccc(cc5 )C#N
+3CCN(CC 3)c4ccc(OC
+3 CN
+2CCC3(CC N(C
+2C (N
+2) O
+)c3ccc( cc1
+)c2 C1=O)N
+)C(=O)N[C@@H](CCCNC(=N)N )C(=O)N[C@@H](CCCCN)C(=O)N
+(C)C)NC(=O) [C@@H](N)C
+%1 2C
+ncn c3c2
+cocc 2
+cnccn 2
+ccccc %12
+c5ccc(F)cc 4
+c5cc( ccc45
+c3o nc(C
+c3cn c(nc23)N
+c2ccn n2
+c2ccccc2 C(F)(F)F
+c2ccc3ccccc3c2 )C(=O)N
+[nH] c3ccccc3
+[C@]23 CO
+[C@H]3CN CC(=O)N3
+[C@H]3CC [C@@]4(C)[C@@H](CC
+[C@H](C)C [C@@H]2
+[C@@H]8 CC
+[C@@H]6 O
+[C@@H](C) N(C
+S c2c1
+S )c1
+Clc1ccccc1 CNC(=O)
+Cc1ccc( O)c(
+Cc1 c(C(=O)
+COc1ccc(cc1)c2 cn3
+COc1ccc(cc1 C(=O)N
+CO CC(C
+CNC(=O) c1cc2
+CN(CC n1
+CN [C@H](CC(C)C)C(=O)N[C@@H]1[C@H](O)c2ccc(Oc3cc4cc(Oc5ccc(cc5
+CCOCC OCCNS(=O)(=O)
+CCCN1 CCN(CC1)c2
+CCCN1 CCN(CC
+CC2 CCCCC2
+CC1=N N(CC(=O)N
+CC( Oc1ccc2
+C2 CN(C(=O)C2
+C(=O) C2
+=C (O)
+9CCC [C@H]9
+4CCN(CC4)c5 ccccn5
+4CCC5(CC 4)
+3)c4ccccc4 )c1
+2CCC [C@H](C
+1 )C(=O)NC
+1 )C#N
+)c6 c5
+)c4ccc(cc4 )[N+](=O)[O-]
+)C(=O)N[C@@H](CCC(=O)N )C(=O)O
+)C (Cl)(Cl)Cl
+(C)C) ccc2
+o c5ccccc5
+n2c( SC
+n cccn2
+ccnc(N C
+ccc(Cl)c (CN(C2CC2)C(=O)
+cc( OC(=O)C
+c5ccccc4 5)C
+c5 cnc(
+c4ccc(F)cc 4)C
+c4ccc( CC
+c3oc4ccccc4 n3
+c3nc4ccccc4 n3C
+c3ccccc3 Br
+c3ccc(F)cc 3C
+c3cc(OC )cc(OC)c3
+c2nn n[nH]
+c2ccc(cn2 c1
+c2cc(ccc1 2)C(=O)N
+c1nc(cs 1)C(=O)N
+c1ccc(OC )c(OC)c1
+c1cc( I
+c(no c5
+c(nnc3s 2)c4
+[C@]4 (CC
+[C@H]4 CN
+[C@H]2 [C@@H]1
+[C@H](N(C 2)C(=O)
+[C@@] (CO)
+[C@@H]1CN(C [C@H]1
+[C@@H](C)N [C@@H](C)C
+S(=O)(=O)N CC
+S(=O)(=O)N 2CC
+S(=O)(=O)C 2
+OCC (CO
+OC c3ccccc3)
+OC (CC(=O)
+O)N C3=O)
+NC(=O)C O)
+Cn1 nn
+Cl. CCN
+Cl )n
+Cc1 coc(n1)c2
+C[C@]12CC [C@H]3
+COc1ccc(C 2=N
+CNC(=O) c1
+CN1CCN(CC 1)
+CN1 CC(
+CN(C) CCN(C
+CCOC(=O)N1 CCC(CC1)NC(=O)
+CCNCC 1
+CCCCC1=NN(C (=O)N1C
+CC(=O)N[C@@H](C (C)C)C(=O)N
+C2 (CCCC
+C(=O)N c5
+=CC 2=N
+=C4C [C@H](CC[C@]4(C)[C@H]3CC[C@]12C
+4 )[N+](=O)[O-]
+2CCC (=
+1)c2ccc(cc2 )C(=O)N
+)c2cc1 Cl
+)C(=O)N[C@@H](Cc4ccccc4 )C(=O)N
+(C)c3 c2
+(=O) C
+%3 5
+s c(N)n
+o nc(C)
+ccc(cc 9
+c5ccc(cc5 )C(=O)O
+c4ccc(cc4 )c5ccc(cc5
+c4 C
+c3ccccc3 )C2=O
+c3ccc4 [nH]nc(
+c3c( s
+c2nccc3 cc(ccc23)S(=O)(=O)N
+c1n cc(cc1
+c1ccccc1)NC(=O) OC(C)(C)C
+c1ccc(cc1 )C(C)(C)C
+c(nn n2
+c(Cl)cc cc2Cl
+\C(=C\C=C/[C@H](OC)[C@@H](OC(=O)N)\C(=C\ [C@H](C)[C@H]1O)\C)\C)C2=O
+[nH]c( SC
+[nH] n3
+[nH] ccc6
+[O-][N+](=O) c1cc2
+[C@]34C [C@](C)(CC
+[C@]3 (C)C(=O)
+[C@H](O [C@@H]4
+[C@H](F)C NC(=O)c1cnc(
+OC(=O) C(=O)N
+NC(=O) N
+N c1ccc(cc1)C(=O)N
+FC(F)(F)c1ccc(O c2ccc3nc(
+Clc1ccc(c(Cl)c1 )c2
+Cl c1ncccc1
+Cc1 csc(N
+C[C@H]1 CN(CCN1
+C[C@@H]1 CN(
+CS(=O)(=O) c1ccc(cc1)n2
+COc1ccccc1 OCCN
+COc1ccc2 CC
+COc1cc2C(=O)N 3CCC[C@H]3C
+CCCC NC(=O)C1
+CCCC CCNC
+CC1 (CO
+CC(C)n1 ncc2
+CC(C) CN(C(=O)
+CC(=O) Oc1
+C= CCN1
+C(C )c3
+C [Si](C)(C)
+Br c1ccc(CN
+=[N+](/ [O-]
+=CC(=O)C=C [C@]4(C)[C@@]3
+=C2 N(C
+6CCN(CC 6
+4)n c1
+4)n 3
+4 N
+4 /NC(=O)N
+4/NC(=O)N C4=O)
+3CCOCC3 )cc2
+2CCCN(CC 2)C(=O)
+2CC OC(C
+1C SC
+)c4ccccc4 C1=O
+)C3 =NCCN3
+(N =O)
+(C)C)N(C)C)N (C)C)N(C)C)N
+nc(C) cc1
+cs 3
+cccc( F)c3
+cc(cnc3 4)C#N
+c5ccccc5 )c4
+c5cc6OCOc6 cc5CC
+c4n nc(
+c4ccccc4)c3 n1
+c3c( cccc2
+c2cccnc2 )cc1
+c2ccccc1 2)NC(=O)
+c2cccc(c2 )c3ccc(cc3
+c2ccc(cc2[N+](=O)[O-])S(=O)(=O) NC(=O)c3ccc(cc3
+c2ccc(C )c(c2
+c1c[nH]c2ccccc1 2
+c(C) cc(N
+c( ccc(C
+[n+]3 ccccc3
+[n+]2 C
+[C@]1 2CCCC
+[C@H]2C 1
+[C@H]( OP(=O)(O)O)
+[C@@H]5C [C@](C)(CC
+[C@@H]1[C@@]23OO 4
+[C@@H](O)C O
+O[C@H](C)C[C@@H]( [C@H]5
+OC(F)(F) F)c2
+OC(= S)N
+OC =C(C(=O)
+O=C( OC1
+NS(=O)(=O) c1ccc(NC(=O)
+NC(=O)[C@H](CC (=O)N)NC(=O)
+Cc2 coc(
+Cc1ccc(C)c(c1 )S(=O)(=O)N
+Cc1 co
+C[C@@H](CO)N1C[C@@H](C)[C@H](CN (C)C
+COc1ccc(cc1)S(=O)(=O)N (CC(C)C)C
+CN(C) CCN1C(=O)c2cccc3
+CC[C@@]1(O)C(=O)OCC2=C1C=C3N(C c4cc5
+CCC2 =N
+CC3 CCCCC3
+CC(C)C[C@H](NC(=O)[C@H](C O)
+C2CC2 )n3
+C1 CN(CCN
+C) s1
+C(=O)N[C@@H](CC(=O)O) C(=O)N[C@@H](C
+C )C(=O)O)
+5CCN(C)CC 5)n
+5CCN(C c6ccccc6
+4)n n2
+4) ccc3
+3C c4ccncc4
+3 C
+2CCN(CC2)C3 CCCCC3
+2C(=O) c3ccccc3
+2)c3ccccc3 F
+2)c3 ccc(C
+2)N 3CCCCC3
+2)C (N
+1)c2ccc( cn
+1 )C2=N
+)n2 )C(F)(F)F
+)c5ccccc5 )cc1
+)c3ccc(OC )c(OC)c3
+)c( c1)c2
+)c nn3
+)C(F)(F)F) nc(c1
+(C)C)N(C)C(=O) [C@@H](NC(=O)[C@H](CC
+ncc( [nH]
+cnc(N C
+ccccc3 c2
+cc6 OCOc6cc25
+cc(NC(=O)C 4CC4)n
+c3cnc4[nH] ccc4
+c3ccc(OC)cc3 )cc1
+c3 ccnn
+c2o c3ccccc3
+c2ccn (
+c2cccc( n2)c3
+c2ccc(Cl)cc2)c3 ccc(Cl)cc3
+c2ccc( CCN
+c2cc(C) on2
+c1ccccc1)C(=O)N 2CCC[C@H]2
+c1ccc(cc1)N (C
+c1ccc(cc1 )C(=O)O
+c1 cccc(Cl)c1
+c(N)n cnc34
+\C=N\ n2c(S)nnc2
+\C=C\ c4ccccc4
+[C@H](O)C(=C [C@@H](C(=O)O3)
+SC(=N C1=O)N
+S C(=O)N(C
+OC(=O)CC (NC(=O)
+NC(=O)c2 ccc(C)cc2
+N2C(=O) c3ccccc3N=C2
+N2 CCN
+N [C@@H]1C
+Clc1cccc( N2CCN(CC
+Cc1onc(C) c1C(=O)N
+Cc1ccc( NC(=O)CS
+CCOc1cccc( c1
+CCCC (O)
+CC(C)CN(C c1cc(Cl)c2
+C(C 2
+C(=N C1
+=C4 [C@@H]5
+5CCCCC 5)n
+4)n (
+4 .OC(=O)C(F)(F)F
+4 )C3=O)
+4 (C)C
+3CCC(CC3)c4 c[nH]
+3)c4 nn
+3)C(=O) OC
+2CCC(CC2)c3nc(cn3 CCN
+)c4 nn
+)c(c5)[N+](=O)[O-])c(O c7
+)C(F)(F)F) ccc1
+)C(C)(C) O)
+)C(=O)N[C@@H](CC(C)C)C(=O)N [C@@H]([C@@H](C)CC
+)C(=C( [C@@H](O)
+)C (F)
+(O)C(O) C1
+ncn n4
+nc3ccccc3 nc2
+cn ccc4
+cc(NC(=O) c3cnn4cccnc3
+cc(C) cc(C)c3
+c4csc( n4)
+c2oc( cn
+c2c(C1 )cccc2
+c1ccc(cc1 )\N=N\
+c1[nH]cnc1 C(=O)N
+c( nc1
+\C=C/3\ C(=O)N
+[N+] 5
+[Cl-]. C[n+]1
+[C@H]6O[C@H](C [C@@H]6
+[C@@]7 (C
+S \C(=N\
+Oc3ccc( cc2
+Oc3ccc( NC(=O)C
+Oc3 c(C
+Oc1ccc(cc1 )[C@@H]2
+OCC Oc3c2
+OC(=O)\C=C\ C(=O)O.
+O=C(N 1CCOCC
+O [C@H]1CC
+NS(=O)(=O)C) ccc4
+N)C(=O)N \N=C\
+Cl. CCN(CC)
+COc1ccc(cc1)C 2C
+COc1 nc(N
+COC(=O) c1cn
+CO [C@@H](C
+CN(CC CN
+CCN(C)CC 2
+CCCC [n+]1
+CCC2= CCCCC2
+CC1(C) OC
+CC c4ccccc4
+C(=O)c2c1 O
+C(=O)O)NC(=O)[C@H](CC (C)C)NC(=O)[C@H](C
+C(=O)NC(=N c%4
+=C2C 1
+= NC(=O)\C(=C\
+4C(=O)c5ccccc5 C4=O)
+3CCN(CC 3)S(=O)(=O)C
+3CCCC(C 3)C(=O)N
+3CCC(CC3) Oc4
+3 )c(Cl)c1
+)c4 cs
+)c4 c(F)cccc4
+)c3 cccc4ccccc34
+)c1C#Cc5ccc(N)n c5
+)c1 )N
+)c(c1 )C#N
+)C2 CCCCC2
+)C(=O)N[C@@H](CO)C(=O)N[C@@H](CC CNC(=N)N
+)C(=O) CC(C)(C)C
+(O)C (OC
+(CC #N
+(C)C [C@H]2
+(C)C 2=O
+n3 )cc2
+cnc2 cc1
+cn c(C
+cc(O) c4
+cc(C OC(=O)
+cc( ccc7
+c6c[nH] cn6
+c6 n5
+c4cncn 4C
+c4cc(Cl)cc(Cl )c4
+c4cc( N
+c3cn c4
+c3 nc(s
+c3 c(Cl)cc(Cl
+c2ccc( CC(=O)N
+c2 cc(OC)c(OC
+c2 c(N)n
+c1n o
+c1cc( cc2
+c1 c2CCCCc2nc3
+c(C)cc(C)nc2 n1C
+c( F)c2
+c( F)c(F)
+[C@H]2 OCC
+[C@H](OC(=O)C )C(=O)
+[C@H](O [C@@H]7
+[C@@]2(CC 1)
+[C@@H]6CC [C@@]45C)
+[C@@H]4O[C@H](CO)[C@@H](O) [C@H]4O
+[C@@H]2C [C@@H](C
+[C@@H](O)[C@H](O) C
+[C@@H](CCCCN )C(=O)N
+[C@@H](C)C(=O)N[C@@H](CCCN=C(N)N )C(=O)N[C@@H](CCCCN
+Oc4ccc5 ccccc5
+O[C@H] %10
+OP(=O) ([O-]
+O=C(CS c1nnc(
+O \N=C(/N
+Nc1n nc(s
+NC(=O)CNC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@H](C c2c[nH]c3ccccc23)
+Clc1 cc(Cl)c2
+ClCCN(CC Cl)
+Cc1n co
+C[C@H](CO)N1C[C@H](C)[C@H](CN (C)C
+COc1ccc(cc1 )C(=O)O
+COc1ccc( NC(=O)CN
+COc1cc2 C
+COc1cc(OC )c(C(=O)
+CN1CCC =C(C
+CCc1n c2ccc(cn2c1
+CCCN 2C(=O)N
+CCCCCC C(=O)O
+CCC c3
+CC(C) OCCC
+CC(=O)N[C@@H](CCCCN )C(=O)N
+C3 =C2
+4CCOCC 4)C(=O)
+4CCOC 4
+4)c5 ccccn5
+1 C
+)[C@H](O) CN
+)C(=O) c2c1
+(CC(=O)O) C
+(C)C )C(=O)C
+n(C 6CCCC6)c4n3
+cn[nH] c4
+ccccc4 6
+cc3 cc(
+cc n1)C(=O)N
+c8 cnc[nH]
+c4cnc[nH] 4
+c4ccccc34 )CC2
+c4ccc(cc4 )S(=O)(=O)C
+c4c3 )c2
+c3n cccn3
+c3ccc(cc3 C2
+c3cc(C)ccc3 C
+c2cn n(CC
+c2ccccc2 NC(=O)
+c2cccc3 cccc(C
+c2ccc(OC (F)F)cc
+c2cc3 OCOc3
+c2cc( NC(=O)C
+c1ccc( \C=C\2/
+c1cc(ccc1 O)
+c(O) cccc1
+[N+](=N \
+[C@]6 (O)
+[C@]2 5
+[C@H]7 O[C@H](C
+[C@H]2 C
+[C@H]1 OC(=O)C
+[C@H](C) [C@H]4
+[C@H](C OC(=O)
+[C@H]%1 6
+[C@@]6 7C
+[C@@H]5 COC(=O)
+[C@@H](CC CN
+[C@@H](CC C(=O)N)NC(=O)
+[C@@H](CC (C)C)
+O)C(=O)N C
+NC(=O)CC C(=O)O)
+NC(=O)C2 (CC
+N=C(C =CC1=O)
+N1 CCN(CC1)c2
+COc1ccc(cc1 )C(
+COC(=O) CCC(=O)
+COC(=O) C(C)(C)
+CN1C(=O) C(=N
+CN1C \C(=C/
+CN1 [C@@H](C
+CCOC(=O) [C@]1(C)CCC[C@]2(C)[C@@H]1CC
+CCN1 CCCC1
+CCCN1C(=O)N(CCC)c2 nc(
+CCCCCCCC\C=C/ CCCCCCCC(=O)N
+CCC2(CC CN(C
+CC1C c2cc(ccc2N1C(=O)
+CC1(C) [C@H]2CC
+C(=O)N 3
+C(=O) C)
+Br c1ccc(NC(=O)
+=CC(=O)N C2=O
+8CC 8
+3)C(=O) OC
+2CCC(CC2)C (O)
+1)c2ccccc2 )c3ccccc3
+1)C 2CC
+)C(=O)N 2CC
+(OC 3=O)
+(C)C)C (C)(C)
+(C)C )cc2
+nnn[nH] 3
+ccccc3 6
+c4ccc(Cl)cc4 )c3
+c3cccc4cccnc3 4
+c3cc(OC)c(OC)c(OC )c2
+c2nccs 2
+c2ccccc2)C c3ccccc3
+c2ccc3 CCCC
+c2ccc(Cl )nc2
+c2cc(F)cc( c2
+c1 2)c3ccccc3
+[C@H](C NC(=O)
+[C@@H]3C c4ccccc4
+[C@@H](CC l
+Oc3ccc( NC(=O)
+Oc1 cccc(C
+O\N=C\ c1
+O[C@@H]3 O
+OCCOc3 cc2
+OCC(=O)N CC
+OC 6=O)
+Nc1nc( cc(
+NC(=O)c2ccc3 c(c2
+NC(=O)N C(=C2
+N(C)C(=O) C=C
+Fc1ccc( F)c(
+FC(F)(F) c1ccc(cc1)C(=O)N
+FC(F)(F) C(=O)
+Cn1c(N c2c(Cl)ccc(C
+Clc1cccc( NC(=O)N
+Cc1ccc( O
+CSc1n cccc1
+COc1ccc( \C=N\NC(=O)
+CN=C(N)N )C(=O)N
+CN1CCN(CC1)c2 cc3
+CCS C1=N
+CCOc1cc( cc(OCC
+CCN(C (=O)N
+CC(C) Nc1nc2
+C(=O)O [C@H]2
+5 )c2
+3) s
+2CCN(CC2)c3 ncnc4
+)n cc12
+)c3ccccc3 O
+(Cl )C1=O)
+(C 2CC2)C(=O)
+(=O)O) NC1=O)
+sc( nc3
+oc( nn1)c2
+nc(cs9 )C(=O)N
+cccc(C )c2
+c8cnc[nH] 8
+c6cccc n6
+c3cc([nH] n3)C
+c3C 2
+c3 c2cnn3
+c2sc( nc2
+c2n c3ccc(Cl)cc3
+c2cccs 2)c3
+c2cccc3ccccc2 3)cc1
+c2ccc3 CCCc3c2
+c2cc(Br )cc(Br)c2
+c2cc( F)c(N
+c1cccc n1
+c1 2)S(=O)(=O)
+c(c(C) nn1
+c [n+]1
+\C=C(/C)\CCC=C (C)C
+\C=C \C=C(/C)\
+\C(=C/1\C(C(=C(C) N=C1
+[O-][N+](=O) c1
+[C@H](CN [C@H](CN
+[C@@]1 4
+[C@@H]3 OP(=O)(O)OC
+[C@@H]2 [C@@H](O)
+[C@@H](CC CC
+P(=O)([O-] )C
+P(=O) (C
+OP(=O)(O) C(F)(F)
+OC =C
+NC(=O)c2ccc(C )c(N
+NC [C@@H]1O
+Cl )cc2
+Cc1ccc(cc1 )N
+Cc1cc(Nc2cc(ccn2 )C(F)(F)F)nc(c1
+COc3ccccc3 O2
+COc1cc2c(N c3ccc(Cl)c(Cl)c3
+COc1cc( \C=N\NC(=O)
+CNC(=O) OC
+CN(C) [C@@H]1
+CN( [C@H](CN
+CN( [C@@H]1CC
+CCOc1 nc(
+CCN(CC(=O)NCCN(CC(=O)N CCN(CC(=O)NCCN(CC(=O)N
+CC1CCCC(C) N1
+CC(C) Nc1nc(
+CC(=O) OC
+CC(=O) O)C
+C=C N(C
+C3=C )\CCC[C@]12C
+C1=O) c4ccccc4
+C(=O)N S(=O)(=O)
+C(=O) C1
+C )C(F)(F)F
+3N [C@@H]4CC[C@H]3C
+3CCOCC3 )cc2
+3CCCCC3 )C(=O)N
+3) OC
+3) NC(=O)C
+)nc( nc3
+)cccc2 3
+)cc3 n2
+)cc2 )C
+)c(OC )cc2
+)N 1
+)C(F)(F)F )c5
+) [C@H](
+) [C@@]4(C)CC
+(NC(=O) OC(C)(C)C
+(CCN (C)C)C
+(C(=O) O)c2
+(C (C
+%2 3C
+nc( SCC(=O)
+ccc5 ccccc45
+c5ccc6ccccc6 c5
+c4cnccn 4
+c4ccccc4 )C(=O)NC
+c4ccc(OC )cc
+c4ccc(F)cc 4)cc1
+c4ccc( cc1
+c4ccc( O
+c4 ccc(Cl)s
+c3cc(ccc3 N
+c3cc( sc3
+c3c[nH]c4ccccc34 )NC(=O)
+c2cccc3 c2
+c2cccc(Cl)c2 )cc1
+c2ccc(cc2)c3 ccc4
+c2ccc(cc2)S(=O)(=O)N 3CCCC3
+c2ccc(cc2)C3 CCCCC3
+c2ccc(cc2 )C#C
+c2ccc(Br )cc1
+c1cn cc(
+c(C) n[nH]
+\C=C(/ NC(=O)c2ccccc2
+[O-][N+](=O) c1ccc(O
+[C@H]7 C
+[C@H]4O[C@H](C [C@@H]4
+[C@H](CC CC
+[C@@H](C NC(=O)
+O[C@@H]1 [C@@H](O)[C@@H](C
+OC(=O) Cc1ccc2
+NC(=O)[C@H] (N)
+Clc1ccc( CCNC(=O)
+C[C@@H]1CC[C@@H](C n2c(nc3
+C[C@@H](CO)N1C[C@H](C) [C@@H](CN(C)C
+COc1ccccc1 CC(=O)N
+COc1cc2 CCN(C)
+COC(=O)[C@H](C c1ccccc1)NC(=O)
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+CO N(C)C(=O)
+CO 1
+CN=C(N)N )NC(=O)[C@@H](C
+CN(C c1ccccc1)c2
+CC[C@@H]1 NC(=O)[C@H](
+CCOc1 cc(C
+CCNC(=O) OC(C)(C)C
+CC(C)C[C@H]( NC(=O)[C@H](C)NC(=O)[C@H](CC
+CC(C)(C) c1ccccc1
+C3=N CCN3
+C1 CCC(CC1)N
+C(F)(F)F )c1
+C(C (N
+C#N )C(=O)N
+=CC 2=O
+=C(C)C 1=O)
+3CCOC[C@@H]3C )c4ccc(nc4
+2CCN(CC2)c3ncccn 3
+2)C3 CCCC
+1=O) c2ccccc2
+)cccc2 n1
+)cc n2
+)c6 cn
+(CCC1 )c2ccc(cc2
+(C)C) NC(=O)[C@H](CO)NC(=O)
+s1)c3 o
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+n3 )C(=O)
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+cnn(C )c4
+ccs c1C(=O)N
+ccc2n 9
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+ccc( O)c2
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+c4c3 C2=O)
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+[S+]([O-] )CC
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+[C@]1(C) O)
+[C@H]7 O[C@@H]8
+[C@H]3 O[C@H]4
+[C@H]1CC[C@H]2[C@@H]3 [C@@H](C
+[C@H](O)[C@@H](CO) O[C@H]1
+[C@@H]4[C@@]5(C)CC [C@H](OC(=O)C)C(C)(C)
+[C@@H]3 4)C
+[C@@H]2 CN
+[C@@H](N)C c2ccc(O)cc2
+[C@@H](CC (C)C)N(C)C(=O)
+Nc1ncc(cc1 OC(F)F)c2
+NC(C O)
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+Clc1cc(Cl) cc(c1
+Cl. CN(CC
+CS CCC(N
+COc1cccc(c1 )N
+COc1ccc(cc1)c2 ccc(
+COCCN1 C
+CCc1 2
+CCOC(=O) c1ccc(NC(=O)N
+CCCC c5c4
+CC(C)n1 nc(
+CC(C) O1
+C4=C 2C(=O)
+C3CC =CCC3
+C2CC 2)c3
+C(=O)N)NC(=O)[C@H](CC C(=O)N)NC(=O)[C@@H]3CCCN3C(=O)
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+3N[C@@H]4CC[C@H]3C 4)C#N
+3CCN(CC3)c4 ncccn
+3)c4 cccs
+2CC S
+2=O) C(=O)
+2)n3 cnc4
+)ccc2 [nH]1
+)C(=O)N[C@@H](CO)C(=O)N[C@@H]([C@@H](C)OP(=O)(O) O)C(=O)N
+(CC c2ccccc2)
+(=O)N)NC(=O)[C@H](CC CCN)NC(=O)[C@H](CC
+nnc( [nH]
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+cccc 2
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+c5 cc(F)cc(
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+c2cccc(c2)c3 cccc(c3
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+c1 Cl
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+[C@@H](N C2=O)
+[C@@H](N 2C
+[C@@H]( \C=C\
+SC (C)C(=O)N
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+O) c4ccccc4
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+N2C(=O)C=Cc3cc(ccc23)S(=O)(=O)Nc4cco n4)c5
+N [C@@H]1CC[C@H](CC1)N
+N CC2
+FC(F)(F) c1cccc(N
+Clc1ccc(cc1)C (N
+Cc1cccc( OC
+COc1ccc2 c(N
+COC(=O) CC[C@@H](C)
+CO c4ccccc4
+CN(C c1ccccc1)C
+CCCCCCCCCCCCCCCC CC
+CCCCC [C@H](NC(=O)
+CC(C)n1 cnc2c(NC
+CC(C(=O)N Cc1ccc(nc1
+CC(=O) O)cc1
+CC \C(=C(\
+C)\C 1=O
+C(=C \
+C(= CS
+=C6C[C@H](CC [C@]6(C)[C@H]5CC[C@]4(C)[C@H]3
+5CCC (O)CC
+5CC 8CC8
+3CCN(CC 3)c4ccc(
+3CCN(CC 3)
+2CCCCC2 )
+2)c3 ccc
+)n3CC OCc1
+)c2 3)C5
+)C(OC(=O)C )C(OC(=O)C
+)C(=O)N[C@@H](CCC(=O)O)C(=O)N [C@@H]([C@@H](C)CC
+)C(=O)N[C@@H](CC 3CCCCC3
+)C (C)N
+cn 1)N
+cc(NC(=O) [C@@H]3C
+cc %10
+c5ccccc5 C
+c5ccccc5 )C(=O)O
+c5ccc( F)c(Cl
+c5cc6OCOc6cc5CC [n+]4
+c4cc(F)c( F)cc4F
+c2n s
+c2cccs 2)\
+c1ncnc2 c(cccc12)C(=O)N
+c1cccc n1)N
+c1ccc2c(c1 )nc(N
+c1ccc( OS(=O)(=O)
+c(ncn c23)N
+c(C) c1C
+c%2 8
+\C=C\C #
+[nH] c(CC
+[C@H](C)O 4)[C@@H](C)
+[C@@H](C 1)
+[C@@H]( [C@@H](CC
+Oc1cc(F)ccc1 Nc2ncnc3cc(
+Oc1 cc(N
+OC(=O)C )c2
+O=C(N c1ccccc1)c2
+N(CC O
+N( [C@H]3C
+Cl.Cl. N
+Cc1cccc(C (=O)N
+COc1cccc(c1)c2cc(ccc2 OC)C(=O)N
+COc1cc2 CCN3C
+COc1 c(CN
+CC[C@H](C)[C@@H]1NC(=O) [C@@H]2CSSC[C@@H]3
+CCOC(=O) CC
+CCCc1c(O)c( ccc1
+CCCN 2CCN(CC
+C2CCC c3ccccc23
+C2C c3ccc(O)cc3
+C1 (C)
+C(C (C
+C(=O)N[C@@H](CC(=O)O) C(=O)N
+C NC(=O)[C@H]1
+Br c1ccc(cc1)C2
+=CC(=O)C(= CO
+7 CCN(CC
+6 /NC(=O)N
+6/NC(=O)N C6=O)
+4CC OC4
+3CCN(CC3)c4 ncccn4
+3)[N+](=O)[O-] )cc1
+2CCC3(CC CN(C
+2)C(=O)N O
+2)C(=O)N 3CC
+)n(C)c2 C1=O
+)ccc1 OC
+)cc2 OC1=O
+)\ C(=O)N1
+)[C@@H](C) O)C(=O)N[C@@H](C
+)C(F)(F)F )c3ccc(NS(=O)(=O)C)c(F)c3
+nc(NCCOCCOCCOCC#C )nc(n3)N
+n2 cc(CC
+n c4ccccc34
+ccccc2 cc1C(=O)N
+cccc n3)
+cc(Cl)ccc2 c1
+c7ccccc7)c8 ccccc8
+c4c[nH]cn 4)
+c3cccc(c3 )N
+c3 nc2
+c3 c(CC
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+c2cc(O)c(O) cc2
+c1oc( nn1
+c1cccc( O
+c(O) c1
+c(N CCC
+c(Cl )c(O)
+\C=C/ CCCCN
+[nH]2 )c1
+[N+] 6
+[C@]4(C)[C@H]3CC[C@]12C )O
+[C@H]3 4)
+[C@@]6 (C
+[C@@]5(C) [C@@H](CC
+[C@@]3(C)[C@@H](CC[C@]4(C) [C@@H]3CC
+[C@@H](O)[C@H](O) [C@H]1O
+[C@@H](CC (C)C)NC(=O)
+P 2
+Oc3ccc(N c4ccccn4)cc3
+Oc3c(C)c(O) c4c(O)
+Oc2 cc(OC
+OC(C =C
+O=C(N c1ccc(cc1)c2
+Nc1ncnc2c1 c(cn2
+NC(=O)c2nc(cnc2N c3cncnc3
+NC(=O)N C3=O
+NC(=O)C S(=O)(=O)
+NC(=N)N )\
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+Fc1ccc( c(F)c1
+F c1ccc(cc1)N2
+Cl. CC(N
+Cc1ccc2 c(
+COc1ccccc1N2CCN(CC CCNC(=O)
+COc1ccc(cc1)C2 =CC(=O)
+CO[C@@H]1CC [C@@]2(CC1)C
+CNC(=N)N)NC(=O)[C@H](C SSC[C@H](NC(=O)
+CN3 C(=O)N
+CN1C(=O)N(C)c2 nc(N
+CCN(CC(=O)N c1c(F)cccc1
+CCCN(CCC )C1
+CCC3 =C
+CC1(C) OC(=O)N
+CC(C)CN(C [C@@H](O)[C@H](Cc1ccccc1)NC(=O)
+CC c1ccc(cc1)C2
+C(=O)O) N
+C(= CC(=
+Br )c5
+6)c7 ccccc7
+4CCN(CC 4)C(=O)N
+4C(=O)c5ccccc5 C4=O
+3CCC (O)(CC
+3)c4 ccc(C)cc4
+3)C (C)(C)
+2CCN(CC2)c3 nc(NCCOCCOCCOCC#C)nc(n3)N
+2CCN(CC2)c3 nc(
+2CCN(CC 2)
+2 )c5ccccc5
+)s c2C1
+)c(CC =C(C)C
+)c(C )c5
+)c(Br )c2
+)c( n1)c3
+)[C@@H](C)O) [C@@H](C)O)
+)N6 CCN(CC
+)C3=N c4c(cnn4C
+)C(C)C )C(=O)O
+)C(=O)NC 4CC4
+(F)(F)F)cc 3
+%10 CCC
+s c4ccccc4c3
+n6 cncn
+n2cncn2)c3ccc(F)cc3 F
+n2cn cc2
+n1)c2 cccc(
+cnc1 C(=O)N
+c5 cncn
+c4ccccc4 S
+c4ccccc34 )C(=O)N
+c4ccc(F)c( F)c4
+c3ccccc3 )O
+c3ccccc3 )NC(=O)C
+c3ccccc23 )cc1
+c3ccc(cc3)N (C)C
+c3c1 [nH]
+c3c(OC )cc(OC
+c2cn cn2C
+c2ccc( OCCC
+c2c[nH]c3ccccc23)NC(=O)[C@H](CCCNC(=N)N)NC(=O) [C@@H](Cc4ccccc4
+c2c[nH]c3ccccc2 3)N
+c1ccc(N C(=N)N
+c1ccc(F)cc 1)C(=O)N
+c(Cl) ccc(Cl
+c(C(=O) OCC
+c( cnc3
+\C=C\C(=O)N S(=O)(=O)
+\C(=N/O)\ c2ccc(C)nc2
+[C@]4 (
+[C@H]1 CCC
+[C@H](C c4ccccc4
+[C@@H]2 N(C
+[C@@H](O)C O)
+[C@@H](O) C2
+[C@@H](O 3)
+[C@@H](C[C@@H](C 5)C3
+[C@@H](C)C(=O) \C=C\C(=C\
+P(=O)(OCC )OCC
+OC)C(=O)C(=O)N 3CCCC[C@H]3
+O) C(=O)OC
+O c1ccc(cc1)S(=O)(=O)N
+NC(=O)c2 c(F)cccc2
+NC(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](CC (=O)O)NC(=O)[C@H](CO)NC(=O)
+NC(=O)[C@H](CC(=O)N )NC(=O)[C@H](C
+NC(=O)C1 CCN(CC1)c2
+NC(=O) c3ccc(F)c(
+N=C (C)c2
+N )c1
+FC(F)(F) c1ccc(CN
+C[C@H](CO)N1C[C@@H](C)[C@H](CN (C)C
+CS(=O)(=O) c1ccccc1
+CO c3ccc4
+CN1CC [C@]23[C@H]4
+CCCCCOC(=O)N1CCN(CC1)C(=O) [C@H](CCC(=O)O)NC(=O)c2cc(
+CCCCCCCC CC(=O)N
+CCCC c4
+CC2 (CCN(C
+CC(N c1cc(nc(C)n1)c2ccc3
+CC(C)N1 CCN(CC1)C(=O)
+CC(=O)N[C@@H](CCCNC(=N)N )C(=O)N[C@H]1
+CC(=CCc1 c(O)
+C3=C(COC3=O) N
+C3=C (C)N(C)N(C
+C2=O) ccc1
+C2=O) C(=O)NC
+C(=O) c7
+7)c8 ccc(Cl)cc8
+5CCCC 5)n
+3 S(=O)(=O)
+2)C (=N)N
+1 N=C(CC1
+)cc 2)c3
+)c4 ncn
+)c(c4 )C(F)(F)F
+)C3 =C(N)N(Cc4ccccc4)C(=O)NC3=O
+)C(=O)N 2CCC[C@H]2
+)C(=O) C(=O)
+)(C (F)(F)F)C(F)(F)F
+) \C(=N/
+(O) CC3
+(CCN3CC 6CC6
+(CC 2CCCO
+(C)C)NC(=O)[C@@H](NC(=O)[C@H](CC C(=O)O)NC(=O)[C@H](CC
+nnn[nH] 5
+ncc(\C=C\ 6/NC(=O)NC6=O)
+n5cn c(C
+n3 cccn3
+n2 4
+ccccc1 3)
+cccc n2)c3
+cc( c(
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+c2cccc [n+]2CC
+c2cc1 OC)c3
+c1cc(C) o
+c(O) ccc3
+c(Br )c3
+\C=C\ c1ccccc1
+\C(=C(\C#N)/ C(=O)N
+[C@H]3C(=O)O[C@@H](C [C@]23C
+[C@@]1 (
+[C@@H]8 C
+[C@@H]2 C3
+[C@@H]1CC[C@@H](CCN 2CCC(CC2)c3
+[C@@H](C) O[C@@H](O
+Oc1 [nH]
+O[C@@H]4O [C@@]5(C)CC
+OCCCC CN
+OC(=O)CCC \C=C/
+O c2cc1
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+NC(=O)c2 ccc(Cl)cc
+NC(=O)c1 ccc(CN
+NC(=O)C2 CN(C(=O)C2
+NC(=O)C (
+F) N
+Cc1cccc( N2CCN(CC
+Cc1ccc( CC(=O)N
+Cc1 sc(
+COc1cc2C(=O)N3CCC[C@H]3C =Nc2cc1
+COP(=O)(S)O [C@H]
+COC(=O) CCC
+CN1C [C@@H](C
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+CCOc1cc(cc(OCC )c1OCC
+CCO CCCNC(=O)
+CCN2C(=O) c3ccccc3C2=O)
+CCN1 CC(C)(C)Oc2nc(nc(
+CCN( [C@H](C
+CCC c1nnc(
+CC3 =C(C)C=C(C)N
+CC(C)C [C@H](O)[C@H](O)
+CC(C)C (=C)CC
+CC(C) (C)C)C
+CC(=O) n1
+CC (F)(F)
+C1= S
+=C4C [C@H](CC
+7 )N
+4CCC(CC 4)c5
+4CCC (O)CC
+4)c5 ccccc25
+4)C #C
+3C =CC(=N
+3) sc2c1
+2CCN(CC2)c3ccc(F)cc 3
+2C [C@H]3C
+2C )cc1
+)cc3 c2
+)cc2 c1
+)c3ccccc3 )N
+)c3cc4 ccccc4
+)NC(=O)[C@H](CC CCN
+(Cl)C =NN(C
+cc(C)c(C) cc4
+cc(C )c(OC
+c4o c5ccccc5c4
+c4cccc (CN
+c4c5 CCCC
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+c2sc1 NC(=O)
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+c2cc( NC
+c2c(C1) sc(NC(=O)
+c2 cc(Cl)cc(Cl)c2
+c1n nc(N
+c%11 ccccc%1
+\ [N+](=C
+[C@H]2CC[C@H](N 2C1=O)
+[C@H](C) [C@H](O)[C@@H](C)
+[C@@]4(C)[C@@H](CC[C@]5(C)[C@@H]4CC =C6
+[C@@H]2 [C@@H]3
+[C@@H](C [C@H](O)
+S CCO
+Oc3c(C)c(O)c4c(O) c(NC(=O)
+OCC2 N(CC
+OC3 (CCN(CC
+OC(= N1)N
+OC 3
+O)cc2 )cc1
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+Nc1 ccc(C
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+N(CCN (C)C)C(=O)
+N(C (=S)N
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+I)cc( I
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+CS c1ccc2
+COc1cccc(OC)c1 C(=O)N
+COc1ccc( Nc2nc(N
+COc1cc2 nc(
+COc1 cnc2
+CO 6
+CN(CCO 2)C(=O)
+CN(CC CC
+CN(C)c1ccc( \C=C\2/
+CN( c1ccccc1)c2
+CCn1 cc2
+CCCCc1n c(Cl)c(C
+CCCCC C
+CC(C)n1 cc(C(=O)c2cncc(NC(=O)C
+CC(C) c1ccc(cc1)c2
+CC(=O)OCC1 =C(N2
+C4 (C)C)
+C1 CCC2
+C(=O) C(=C1
+C(=N )S
+6C c7ccccc7
+5 )cc4
+2CCCC 2)C3
+2)c3cccc( F)c3
+/C/2=C /
+)n4 cc(nn
+)c5ccc( Br)cc5
+)c5 ccc(C)cc5
+)c4ccccc4 C2=O
+)c1 2)c4
+)c( NC(=O)C=C
+)\C 1=O)
+)[N+](=O)[O-] )c1
+)CC2 )c4
+)C(=O)O) cc1
+(N =N
+(C1 )n2
+%1 4C
+%1 3C
+o n
+nn nc4
+nc3ccccc3 s2
+nc(N)n c(O)
+n3cnc4 [nH]ccc4
+n1 )[C@@H]2
+n 8
+cc[n+] (CC(=O)
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+c4cc(O) c(O)
+c2ccccc2 )c(n1
+c2ccccc1 2)C3
+c2cccc(Cl)c2 )c3
+c2cccc( OCC
+c2ccc(cc2 )C(=N)N
+c(Cl)cncc2 Cl
+c(Cl)cc(Cl )cc4
+c(C(=O)N )c(N
+\C=C\C )N(C)C(=O)
+[C@H](OC(=O)C) [C@@H]1
+[C@@H]5C[C@](C)(CC [C@]5(C)CC[C@@]34C
+[C@@H](O [C@H]4C
+[C@@H](CO) O[C@H](
+[C@@H](C)O)C(=O)N[C@@H](CCCCN )C(=O)N[C@@H](C
+[C@@H](C)C(=O)N[C@@H](CC (C)C
+[B-] (F)(F)F
+Oc1 ncn
+O[C@@H]1 [C@]2
+OC )C2
+O) c2n1
+N 6
+Cc1nn( c2
+C[C@@H]1CC[C@@]2(CC[C@]3(C)C(=CC [C@@H]4[C@@]5(C)C
+CS(=O)(=O) c1ccc(
+COc1cc(O)c2 C(=O)
+COC(=O) [C@]1(C
+CO c3ccccc3
+CN(C) c1nc(
+CC[C@H](C)[C@H](NC(=O)[C@H](CC C(=O)O)NC(=O)[C@H](CC
+CCCCCCCCCCCCC C
+CC3 (CC
+CC1 CC(=O)
+CC(C)(C) [C@H](NC(=O)
+CC(C) c1ccc(cc1)C2
+CC(C) Oc1ccc(N
+C4 (CCCCC
+C3CC4CC(CC2C 4)C3
+C(=O)N(C )c2
+C(=O)N Cc2
+C(=O)N (CCC
+=CC(= C(C#N
+=CC 4
+8)c9 nc(cs9)C(=O)N
+5 COC
+3 )cc2
+2)\ C
+1 Cc2
+)n( n2
+)n c3cc(ccc1
+)cc2 O1
+)c3cccc( O)c3
+)c( n4
+)[N+](=O)[O-] )
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+=CC =C(C)C
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+)C(=O)OC c6ccccc6
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+) [C@@H](NC(=O)
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+=CC 3=N
+=C(C(=O)N C
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+C(=C(N =C1
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+1)C #C
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+)[N+](=O)[O-] )\
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+(C)C 4
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+c(C)cc(C) cc2
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+[N+](=O)[O-] )N
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+F) C(=O)C
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+)C3 CCN(C
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+) NC(=O)[C@H](C)NC(=O)
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+[N @+]
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+[C@@]7 (C)
+[C@@H]2 [C@@H](CC
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+F )c7
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+CCN 4CCOCC4
+C=C (CN
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+C(=O)N 4CC
+C(=N )
+=C(C(=O)N C3=O)
+4C[C@@H]5C [C@@H](C[C@@H](C5)C3
+3CCC (O)CC
+3 )CC2
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+2) O
+/C/ 3=C
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+)[C@H](O) [C@H](C)O5)c4c(O)c3C(=O)c1
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+)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCC(=O)O) C(=O)N[C@@H](C
+)C(=O)N (C)C
+(O) CCN(CC
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+(C)c1 nc2
+s1 )C2
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+o +]
+n5 C
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+c4ccc(cc4 )n5
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+c3occc3 )C
+c3ccccc3 )OC
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+c2nnc( NC(=O)
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+c1 c(Cl)cccc1
+c( ccnc3
+\C(=C/ C)\
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+[C@H](O) [C@@]3(C)[C@@H]4CC
+[C@H](C(=O)N [C@H](C(=O)N
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+[C@@H](C =C(O
+[ o+]
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+O[C@H](C OC(=O)
+NC(=O) c2ccc(N
+NC(=O) c1cnc(N
+N CCCCN
+FC(F)(F) C(=O)N
+F)N3C [C@H](C
+F c1cn
+Cc1cc( Br
+Cc1 coc(
+C[C@@H](N 1CC
+CSSC [C@H](NC(=O)[C@H](CC
+COc1cccc(c1)c2 ccc3
+COc1ccc2 nc(N
+COC(=O) [C@@]1(C)CC
+CNC(=N)N)NC(=O)[C@H](C O)NC3=O)
+CN1CCN(CC1)C(=O) c2cc3
+CN1 CCO
+CN C2
+CCOC(=O) c1ccc(NC(=O)
+CCO c2ccc3
+CCCCC (CC)C
+CCCC CCNCC
+CCC2(CC N(C
+CC1(C) Oc2cc(
+CC(C) c1ccc(CN
+CC(C) N(C)
+C5CCCC 5)n
+C(=O) c2c(O)
+C(= NC(=O)
+C \C=C/
+4CCC(CC 3)
+3)c4 ccc(Cl)c(Cl)c4
+3)C (C)C
+3 4
+2C =CC=CC
+2)c3ccc(cc3 )[N+](=O)[O-]
+2 )C(F)(F)F
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+)c(CC )c1
+)[C@H](O) [C@H]1O
+)C3 )cc1
+)C(=O)O .OC(=O)C(F)(F)F
+(c3ccccc3 Cl
+(N 2
+(=C(N2C1=O)C(=O)[O-] )C
+( [C@@H]3
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+n2 nc(nc2
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+cccc(c2 )C(=O)N
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+c4 cc[nH]
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+c2cccnc2 )c3
+c2ccccc2 Br
+c2ccc(cc2 C1
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+c2cc(ccc1 2)c3
+c2c( n1
+c1ccc(Cl)cc 1)C
+\C=C\C (C)(C)
+\C=C/ CCCC(=O)
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+\C #N
+[C@H](N =[N+]=[N-]
+[C@H](C)[C@@H](O[C@@H]3O[C@H](C)C[C@@H]([C@H]3 O)N(C)C)
+[C@@]4(C)[C@@H](CC[C@]5(C)[C@@H]4CC=C6 [C@@H]7
+[C@@H]4 C=C
+[C@@H]1Cc2ccccc2 CN1C(=O)
+SS CCN
+P(=O) (
+OCC (F)(F)F
+O=C (OCC
+O) C(F)(F)F
+O c8
+NC1 (CCC1)c2ccc(cc2
+NC(=O)C2 (CCC2
+NC(=O) c4occc4
+N=C N(CC(=O)N
+N2C(=O) C
+N \N=C\
+Fc1cc(F) cc(c1
+F)C #N
+Cc1onc( c2ccccc2Cl
+C[C@@]1 (CC
+COc1ccc2 C[C@@H]3
+COc1ccc(CN (C
+CN(C(=O) C
+CCN(CC 5
+CCCC NC(=O)c1
+CCC1 (C)
+CCC [C@H](NC(=O)[C@H](C
+CC(C)(C)c1cc(cc(c1 O)C(C)(C)C
+CC [C@H](
+C(CN 1CCCCC1
+C(=O)NC [C@@H](F)C
+=C(C#N )C#N
+=C =S
+6 7
+5CC5 )cc1
+5 C(=O)N
+4Cc5ccccc5 C
+4CCCC4 )c1
+3N=C (CC3
+3CCN(CC3)c4 cccc(Cl)c4
+3)c4ccc(Cl)cc 4Cl
+3)c4 cc(ccc4
+2C (SC1
+2)c3ccc(cc3 )C(F)(F)F
+2)c3ccc( Br)cc3
+1CCC(CC 1)n2
+)s 4
+)ccc1 OC(=O)
+)c4ncccc4 F)n(
+)c4ccc(OC )c(OC)c4
+)c(OC )cc1
+)C(=O)N \N=C\
+(O S(=O)(=O)
+(NC(=O)C (CC
+(C)C)NC(=O)[C@H](CC CNC(=N)N)NC(=O)
+(C)C )C(=O)N[C@@H](CC(=O)N
+(C )c3ccccc3
+nn cn2
+ccc(C )c3
+cc(C NC(=O)
+c8 csc1n8)c9nc(cs9)C(=O)N
+c4c2[nH] c5ccccc45
+c3cccs 3)C
+c3ccc(cc3 )C(=O)NC
+c3ccc [nH]
+c3c( F)
+c2ncn c3c2
+c2cncn 2CCCC
+c2ccc(Cl)c(c2 )C(=O)N
+c2cc( c(O)c(c2
+c1ccc(cc1)c2 cn3
+c(N)ncnc1 N
+\C(=C/ N
+[I-]. C[N+](C)(C)
+[C@H]3 CN(CC
+[C@H](C)O4)[C@@H](C) \C=C\C=C\
+[C@@H](O) C3
+[C@@H](C) CO
+SSC[C@H](NC(=O) [C@@H](NC(=O)CNC(=O)
+O)N (C)C(=O)
+N[C@@H](C c1ccccc1)C(=O)N
+NC(=O)[C@H](CC CN=C(N)N)NC(=O)[C@H](CC
+NC(=O)[C@H] 2C
+Fc1ccc( NC(=O)
+FC(F)(F) c1ccc(nc1
+Cc1nn(C)c(C)c1 NS(=O)(=O)
+COC (=N
+CO c2ccc(
+CNc1n ccc(n1)c2
+CN(CC #C
+CN(C)C1 =N
+CC[C@H](C) [C@H](N)C(=O)N
+CC(C)C[C@H](NC(=O)[C@@H]1CCCN1C(=O)C)C(=O)N[C@@H](C c2cncn2CCCC
+CC(=O)O[C@H]1C(=O)[C@]2(C)[C@@H](O)C[C@H]3OC[C@@]3(OC(=O)C)[C@H]2 [C@H](OC(=O)c4ccccc4)[C@]5(O)C[C@H](OC(=O)
+C2=C (CN(CC
+C(C)(C)C )C(=O)
+C(=O)N 2CCCC
+Br c1ccc(cc1)C(=O)
+6CCC 6
+5 )CC
+4CCCCC 4)C(=O)N
+4CC4) CCC5
+3C 4C
+2)c3nn n4cnc5[nH]ccc5
+2)c3ccc(Cl)cc 3Cl
+1)c2ccc( s
+1 )C(F)(F)F
+)n3 cncn3
+)c4 ccccc24)cc1
+)c3ccc(cc3 )C#N
+)c3 oc(nn
+)c(F)cc 4C(=O)
+)c( OC(=O)
+)C(C 3)C2
+)C(=O)N[C@@H](CCCN=C(N)N )C(=O)N
+(c3ccccc3 )c2
+(N =C(N)N
+s cnc4
+ncc( cc4
+n5 C
+c6c7 CO[C@@H]([C@H](NC(=O)
+c5ccccc5 )C(=O)N
+c4cccs 4)
+c4 nc(N
+c3nn [nH]n
+c3ccc(O) cc2
+c3c(Cl)ccc( c3
+c2ccccc2 )s
+c2ccc(cc2 Cl
+c2ccc(Oc3 ccc(Cl)cc3
+c2ccc(F)cc2 F)c3
+c1ccc( Br)cc
+c1cc(C) nc2
+c(N)n c5
+c(C(=O) OC
+[n+]1 ccccc1
+[C@](C) (F)
+[C@H]6 O
+[C@H]2[C@@H]3CC c4cc(O)ccc4
+[C@H](C)[C@@H](O [C@@H]2O
+[C@@H]6CCCN 6
+[C@@H]5C [C@H]4
+[C@@H]3C #N
+[C@@H](N)C c1ccc(O)cc1
+SC c3ccccc3
+Oc3 c(NC(=O)N
+O[C@@H]2C O)
+OC(=O) c1ccc(cc1)S(=O)(=O)N
+OC (CC(=O)N
+O2 )nccc3
+O) C3
+NC(=O)C )C(=O)N
+Cl c1cc(ccc1
+Cc1 c(cnn1
+CS c1ccc(cc1)C2
+CCc1 cc(OC
+CCOC(=O)c1c(C) o
+CCOC(=O)c1 c(C)c(sc1
+CCNC(=O) [C@@H](NC(=O)[C@H](CC
+CCCCC #
+CCC1=C (C)
+CC1 CCCC
+CC (OC
+C=CC(C)(C) Oc4
+C=C (CC
+C2=N CCS
+C(F)(F) F)cc2
+C(=O)O[C@H]1C [C@@]2(O)
+=C 3C(=O)
+6 =CC(=O)
+6 )c1
+5)N6CCOCC 6
+4)C =C
+3CCCCC3 )C
+3C (C
+2)c3 ncnc4
+)c2ccccc2 )c3ccccc3
+)c(C#N )c2
+)C(C)C) [C@@H](C)CC
+)C(C)(C) CCC3
+)C(=O)N[C@@H](CC 2CCCCC2
+(C)C) NC(=O)[C@H](C)NC(=O)[C@H](CC
+(=O)N )NC(=O)[C@H](C
+(= C1C
+nn n(CC
+nn c[nH]
+ccc1 5
+cc(NC(=O)[C@@H]3C NC(=O)C3)nn2
+cc(Br )ccc5
+c8 cccc(
+c6ccc7 OCOc7
+c6 cccc(C
+c5cccc(OC )c5
+c5ccc(F)cc 5)n
+c4csc(n4)[C@@H]5COC(=O) c6c7CO[C@@H]([C@H](NC(=O)
+c4cccc( OC(F)(F)F)c4
+c3oc4ccccc4 c3
+c3c(C 2
+c3c( F)c(F)c(F)c(F)c3
+c2s 1)N
+c2nc( cc(n2
+c2n [nH]c3
+c2ccc3CCC c3c2
+c2ccc(cc2 )C3=N
+c2ccc(cc2 )C(=O)C
+c2cc(ccc1 2)S(=O)(=O)
+c2cc( cc3
+c1cccc( OC)c1
+[Se] [Se]
+[S+](C) [O-]
+[C@H]4 C(=O)N
+[C@H](C) O[C@H](C
+[C@@]4 5CC
+[C@@H]6 NC(=O)[C@H](NC(=O)[C@@H]4NC(=O)[C@H](CC(=O)N)NC1=O)
+[C@@H]2 CCCCN2C(=O)
+Oc3cc(ccc3 S1(=O)=O)
+OC(=O)C1 CN(C
+OC(=O) c1nc(
+OC(=O) Cc1
+N[C@@H](CC(=O)N 1CC
+NS(=O)(=O) c1ccc(cc1)c2
+NC(=O)NCC (F)(F)F)c4
+NC(=O)CS c2oc(C
+NC(=O)CS c1nnc(
+NC(=O)CC C(=O)
+Cc1ccc( cn1)c2
+C[C@@H](N 1
+C[C@@H](CO)N1C[C@@H](C) [C@@H](CN(C)C
+COC(=O)c1 [nH]
+CN(C c1o
+CCc1 c([nH]
+CCc1 [nH]
+CC[C@H](C)[C@@H]1NC(=O)[C@@H]2CSSC[C@@H]3 NC(=O)[C@@H](NC(=O)[C@H](CC
+CCOC(=O)N1 CCN(CC
+CCN(CC) CC(=O)N
+CCCN C1
+CCC(CC 1)C
+CC(CC 1=O)
+CC(C)(C) O
+C3 C(C
+C2CCN(CC 2)C(=O)N
+C2CC2)n3 ncc(\C=C\
+C(=N )C=C
+C(=C(C#N )C(=O)N
+C(= [N+]
+= S)
+= CCC1
+5CC8CC8 )c67
+5) [C@H](O[C@H]
+4CCCC 4)C(=O)
+/C/ 3
+)c4 c3n2
+)c2cn1 )c4ccccc4
+)c( [nH]2
+)c( F)cc2
+)[C@@H](O) [C@@H](O)[C@@H]1O
+)N 5CCOC[C@@H]5C
+)C(=O) COC(=O)
+)C c5ccccc5
+(F)(F )C2
+(C2CC S(=O)(=O)C2
+(=C(C 1=O)
+sc( NC(=O)N
+n4 )C(F)(F)F
+n2 n1
+n2 3)C(=O)
+cn c2n1
+cco n5
+cccc(c7 %11
+cccc(c7%11 )n6
+c6nc7ccccc7 s
+c5ccc(O)c(O) c5
+c4ccc(cn4)C(F)(F)F) ccc3n2
+c3ccccc3 OC2=O
+c3c[nH] c4ccc(cc3
+c2nc3 N(C)C(=O)N(C)C(=O)
+c2ccc3OCOc3c2 )c4
+c2ccc(s 2)c3
+c2c( O1
+c1n c[nH]
+c(C)nn c2
+c%11 cccc(c7%11)n6
+\C(=C\CC \C(=C\CC
+[O-][N+](=O) c1oc(cc1
+[C@](O)( [C@H](C)C[C@@H]2
+[C@](C c2ccccc2)
+[C@H]3 4
+[C@H](O 3)
+[C@@]6 (C)C
+[C@@H]3CCCC [C@H]3
+[C@@H](O) [C@H](C)
+S c2nn
+S )C
+Oc4 ccc(C)cc4
+Oc1cc( cc2
+OCC Oc1
+O=C(N c1ccc(cc1)S(=O)(=O)N
+O)\ N=N\
+NC(=O)[C@H](CCC(=O)N )NC(=O)[C@H](CC
+NC(=O)[C@H](C)NC(=O)[C@H](C)NC(=O)[C@H](CC C(=O)N)NC(=O)CNC(=O)[C@H](CC
+N( [C@H]4C
+Clc1ccc( \C=C\2/
+Cc1ccc( cn1)C(=O)N
+C[C@H]1COCCN1 c2nc(N
+COc1cccc(c1 )C2=N
+COc1ccc2 C[C@H]3
+COc1ccc(OC)c(c1 )C2
+COc1cc(C =O)
+CN(c2ccccc2 )C3
+CN(C) c1ccc(cc1)C
+CN(C) CCCC
+CCO \C(=C/1\C(C(=C(C)N=C1C
+CCO \C(=C/1\C(C(=C(C)N=C1
+CCN(CC O)
+CCCCCCCCCCCCCCCC CC(=O)N
+CCCCCCCCCCCCCC C
+CCCCC c1ccc(cc1)C(=O)N
+CCCC c1nc(
+CCC(=O)N 1C
+CC1(CC (=NO
+CC1(C) [C@@H](O)CC
+CC(C) Oc1ccc(C
+CC(C) N(C)C
+CC [C@H](O)
+C3CCN(CC 3)S(=O)(=O)
+C2C (C(=O)
+7CCC [C@@H]7
+3CC c4c(C
+3C 4CC
+3)S(=O)(=O) N
+1) OC(=O)
+)c6 ccc(Cl)cc6
+)c5ccccc5 n2
+)c4 ccccc34
+)c3cccc( F)c3
+)c(C)c1 C
+)\ c2ccccn2
+)N(CC Cl)CCCl
+)N(C)C )C(=O)OC
+)N(C)C)C(=O)OC c%11cccc(c7%11)n6
+)C(=O)N[C@@H](CCC(=O)O) C(=O)O)
+)C(= C(C#N
+(F)(F)F)cc 1
+(C)C3 =O
+(C)C)N )N
+(= N1
+n 4)c5ccccc5
+ccccc1 2)N
+ccc3 cccc4
+cc(nc(c4cncc(Cl)c4 )c23)C5
+cc(OC)c(OC)c(OC )c6
+cc(OC )c3
+cc(OC (F)(F)F)
+cc(C#N )c(N
+cc( nn2)c3
+c4ccccc4 )C3=O
+c3s 2
+c3n ccnc3
+c3cccc(c3 )C#C
+c3cc(cc(c3 )C(F)(F)F
+c3c( ccc4
+c2oc(nn2 )c3ccccc3
+c2ccc3c(\C=C\c4ccccn4)n[nH] c3c2
+c2ccc3 ncc(
+c2ccc(Cl)cc1 2
+c2c(C)nn( c2C
+c1c[nH]cn 1)N
+c(OC )cccc2
+c(Cl)c1 )c2
+c( ccn2
+\C(=N/ C(=O)
+\C(=C\C=C 3C
+[n+]3 [O-]
+[C@]13 C
+[C@H]2 [C@H](O)C
+[C@H](C)[C@@H](O) CC1=O)
+[C@H](C =C(O
+[C@@]6 (O)
+[C@@H](C O)C
+S =C1N
+Oc3ccccc3 )cc2
+O 3)C
+FC(F)(F) S(=O)(=O)
+Clc1ccc( NC(=O)
+Cl. Cc1ccc(
+Cc1onc(C)c1 c2ccc3
+Cc1ccc(cc1)S(=O)(=O) NC(=O)N
+CS c1ccc(O
+COc1ccc( CCC
+COc1cc(OC)cc( \C=C\
+COc1 c(Br)cc(
+COCC1 CN(C
+CO c2c1
+CO [C@@H]7
+CN(C)C1=O) C(C)C
+CCn1 cnc2
+CCOC(=O)C1=C (C)N(C
+CCOC(=O) [C@H](C)N
+CCCN 4C(=O)
+CCCC c1ccc2
+CCC(C) S
+CCC c2c1
+CCC #
+CC(=O)N1 CCN(CC
+CC(=O)N1 2)C
+CC \C(=C\
+CC C(=O)C
+CC 2CCN(CC
+C=C 2C(=O)
+C2 Cc3ccccc3
+C(=O)O[C@@H]( [C@H](C)[C@@H](O)CC1=O)
+=C(N C1=O)
+8 =O)N
+6 )C(F)(F)F
+5CC6CC(CC4C 6)C5
+3CCCCC3 )cc2
+3CC (CCl
+3)c4 ncccn4
+3)c4 cccc(Cl)c4
+2)c3ccccc3 n1
+1CCN(CC1)c2 ccc(
+1)c2ccc( nc2
+.[O-] S(=O)(=O)C(F)(F)F
+)c3ccc(F)cc3 F
+)c n4C)cn3C
+)\ S\C(=C/
+)N 3CCCCC3
+)CC =C
+)C3 (C)C
+)C(C #N
+)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CC CCN
+)C(=O)N[C@@H](CCC(=O)N )C(=O)N[C@@H](CCC(=O)N
+(O) CCCCC
+(CCC 5
+(CC O)C
+(C) CCN
+%5 0
+no c3
+n7 cnc8
+cn( nn2)c3
+cccc(C )c2
+ccc( [nH]
+cc(O) cc(
+cc(Cl)cc(Cl )c3
+c7ccc(O)c( c7)c8
+c5ccccc5 )c1
+c5cccc6 ccccc56
+c3cccc n2
+c3ccc(C)cc3 C
+c3ccc(Br)cc3 )cc1
+c2nc3 cc(Cl)ccc3
+c2ccccc2)C(=O)N 1
+c2ccc(cc2 F)c3
+c2ccc(F)cc 2C
+c2ccc( O
+c2ccc [nH]2
+c2cc( nc(C)
+c2 CS(=O)(=O)
+c1 cccnc1
+c1 )N
+c(Cl )c3
+c%10ccccc%10 )C(=O)N
+\C(=N\ S(=O)(=O)
+\C(=N/ OC
+[C@](O)([C@H](C)C[C@@H]2 OC)C(=O)C(=O)N3CCCC[C@H]3
+[C@H]5C(C)(C)[C@H](CC [C@@]56C[C@@]46CC
+[C@H](OC(=O) c3ccccc3)
+[C@H](O) [C@@H](O
+[C@H](C) CCC
+[C@@]1 4C
+[C@@H]9CCCN 9C(=O)
+[C@@H]6CC[C@@]5(C) [C@]4(C)CC
+[C@@H]6 [C@@]7(C)CC
+[C@@H](O)[C@H](N)C[C@H](N) [C@H]3O[C@H]4
+[C@@H](O) [C@@H](
+Oc2 c(NS(=O)(=O)
+Oc1n c2ccccc2n1
+ONC(=O) CCCCCCC(=O)N
+OCCN (
+OCC3 c4ccccc4
+O=C1 NC(=S)N
+O [C@@H]1CC[C@H](CC1)N
+NC(=O)c1 s
+NC(=O)C2(C) CCN2C(=O)
+N1 CCCCC1
+N 5CCOCC5
+N 3CCC
+F )c4ccccc4
+Cn1cc(C 2=C(C(=O)NC2=O)
+Cl. NC1
+Cc1ccc(C )c(N
+Cc1cc(C )c(cc1
+COc1ccc( \C=N\NC(=O)c2
+CO[C@H]1 \C=C\
+CC[C@H]1 OC(=O)C[C@@H](O)
+CCN(CC) CCN(C
+CCN(CC c2c(N
+CCCN (C)C
+CC2 CCC(C
+CC1(C) CCC[C@]2(C)
+CC1 CCCC(NC(=O)
+CC(C) [C@@H](N
+CC(C) CC(=O)
+C6 =O
+C2CCCCCC 2
+C) O
+=C3 c2n
+5) [C@@H](O)
+3CCN(CC3)c4ccccc4 F
+3=C 2C(=O)
+2CCN(CC2)c3 cccc(C)c3
+2)c3cccc(c3 )C(F)(F)F
+2)c3ccc( nc3
+2)NC(=O)[C@H](C SSC[C@H](NC(=O)[C@H](C
+2 CN(C
+)ccc2 4
+)c6 ccc(O)cc
+)c4ccc( N
+)c3 ccc(Cl)c(Cl)c3
+)c(C)c1 CC(=O)O
+)c(C#N )c3
+)CC2 =O
+)CC2 .OC(=O)C(=O)O
+)C(=O)N[C@@H](C c1ccc(O)cc1
+)C (O
+(C)C (O)C(C)C
+(C c5ccccc5
+n5 ccnn
+n cccc6
+cn( nc3
+ccc %11
+cc(O) c(nc3
+cc(Cl)c(Cl )cc1
+c4ccc(Cl)cc2 4
+c4ccc(Cl)cc 4CCc5
+c3nc(oc3C )c4ccccc4
+c3ccccc3C2 )cc1
+c3ccc(cc3)N (C)C
+c3cc( I
+c2cccc3 ncccc2
+c2cccc(CC 3=NNC(=O)C4=C3CCCC
+c2ccc3 nnc(
+c2ccc(cc2)c3ccccc3 S(=O)(=O)N
+c2cc(ccc2 C)C(=O)N
+c2cc(cc(c2 )C(F)(F)F)C(F)(F)F
+c2cc(Cl)c(O) cc2
+c1n s
+c1ccccc1 Cl
+c1cc( nn1
+c%3 2
+\C 2=O)cc1
+[C@](C) (CC(=O)
+[C@H](C[C@@H]1 CCNC1=O)
+[C@@]2(O) [C@H]3C
+[C@@H]4 [C@@H]3CC
+[C@@H]3C [C@H]3
+[C@@H]1C )C
+[C@@H](NC(=O)[C@H](CC (=O)N)NC(=O)
+[C@@H](NC(=O)[C@H](C c2ccccc2)NC(=O)
+O=C(N1 CCN(C
+NC(=O)c2csc(n2)c3 cc(O)c(nc3
+NC(=O)c1cccc2 c(NC
+NC(=O)N C2=O
+N=C3C =CC=CN3
+N=C3C =CC(=
+Fc1ccc2 c(noc2c1)C3
+Cc1cnc( nc1
+C[C@@]1 (C
+COc1ccc( NC(=O)COC(=O)
+COC(=O)C1=C(C[C@@H]2CC[C@H]1 N2C(=O)N
+CCN(CC) CCCNC(=O)
+CCCCCCCC CC(=O)O
+CCCCCC NC(=O)N
+CCCC CCN(CCCCCC
+CCC OC1
+C3=N CCN
+C )c1C
+C )CC
+4 )C2=O)
+4 )C1
+2)c3ccc(cc3 )c4ccccc4
+2)S(=O)(=O) c3ccc(C)cc3
+)n3 n2
+)cc3 )c2
+)c4 cnccn4
+)c1 2)C(=O)N
+)\C 3=O)
+)N 5CCOCC5
+)CC3 )c5
+)C(=O)N[C@@H](C(C)C )C(=O)O
+)C(=O) c4
+) [C@@H](CC
+(CC Cl)CCCl
+(CC CCN)
+(C)CC OCC
+(C)C )c(O
+(=C)C )
+nc( c3ccccc3)c(
+n5cnc(C )c5
+n4 n3
+n1cnc2 c(N)ncnc1
+ccc1 C(=O)N
+cc3 O
+cc(O)c2 C1=O)
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+c3ccc(Br )s
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+c2c[nH]c3ccccc2 3)cc1
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+[C@H]3 [C@H](CC
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+[C@H]2 OC(=O)C(=C)
+[C@H]2 CC(=O)
+[C@H](CN (O)C=O)
+[C@H](C)C O
+[C@@H]5CC[C@@]34C)[C@@H]1 2)C(=O)N
+[C@@H]3 CC(=O)
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+O) \C=C\
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+C(=O)N3 c5ccccc5
+=CC(=NC %1
+=C(C 2
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+4) OC3=O
+3CCN(CC3)c4 ccc(F)cc
+3CC SCC3
+2)N 3C(=O)
+1CCC [C@@H]1
+1)N2CCc3c2nc(nc3c4cnc(N)nc4 )N5CCOCC5
+)nc( n1)c3
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+) /C
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+[C@]2 (CO)
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+[C@H](C) N1
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+[C@@H]5[C@@]6(C)CC [C@H](O)C(C)(C)
+[C@@H]4[C@H]5[C@@H](CC [C@@]5(CC[C@@]34C
+[C@@H](CC N1
+[C@@H](C [C@H]5
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+SC(C)(C) [C@@H](N
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+OC[C@H]1O [C@@H](
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+NC(=O)[C@H](C c2ccccc2)N
+NC(=O)N )\
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+N2 C(=O)N(C
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+F)c2 c1
+Cl )C2
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+CC(CO) (C
+C=C(C)C(=O)NC %4
+C3CCN(C c4ccccc4)CC
+=CC =C(C
+9 )C
+8CCCCC 8
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+5CC5 )c4
+4CCNCC 4)n
+4CCN(C c5ccccc5)CC4
+4CCCCC4 )c1
+4) CC1
+3)n c(N
+2)c3 cc(ccc3
+2)C #
+1CC OCC1
+1CC OC1
+1)c2cccc( O)c2
+)n4 cccn4
+)cc2 C
+)c5 ccccc4
+)c4ccc(OC )cc4
+)c3n2C(C)C)c5 cc(Cl)ccc5
+)c( cc2c1
+)S(=O)(=O)C )cc1
+)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC (=O)O)NC(=O)[C@H](CC(=O)N)NC(=O)[C@H](CC
+)C2 CCCCC2
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+)C(=O)N[C@@H](CO)C(=O)N[C@@H](CC CCN
+(C) nc2
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+n c(Cl)c(
+l )C
+cs c2
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+c6 cccnc6
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+c3cn cc(c3
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+c(O) c4C(=O)
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+[C@](C)(O)[C@@H]( [C@H](C)O%10
+[C@](C)(O)[C@@H]([C@H](C)O%10 )N(C)C)C(=O)OCc%11cccc(c7%11)n6
+[C@H]3 [C@H]4
+[C@H](CN (C)S(=O)(=O)
+[C@H](C c4ccccc4)NC(=O)
+[C@H]( [C@H]2
+[C@@]2 (O)CC
+[C@@H]2 4
+[C@@H](N)CC(=O) O)C
+[C@@H](C) [C@H](C)CC
+[C@@H](C c4c[nH]c5ccccc45
+S(=O)(=O) c1ccc2
+S(=O) (=N
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+O[C@@H]1 CCN(C
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+OC )C(=O)O)
+O=C(NCC1 CCCO
+NCCCC [C@H](NC(=O)[C@H](C
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+N c1ccc(N
+Cl. CN(C\C=C\
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+Cc1cn n(CC2
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+Cc1cc(C)cc(c1 )S(=O)(=O)
+C[C@@H]1CC[C@@]23CCC(=O)[C@H]2 [C@]1(C)[C@@H](C[C@@](C)(C=C)[C@@H](O)[C@@H]3C)
+COc1ccccc1 NC(=O)
+COc1cc2 N
+CO \N=C/1\
+CNC(=O) \C(=N\OC)\
+CN(C) CCN(C)
+CCOc1ccc(cc1)N (CC(=O)N
+CCOC(=O)\C=C\ [C@H](CC
+CCOC(=O)C1 C(N
+CCC(CN S(=O)(=O)
+CC1(C)CC [C@@]2(CC
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+CC(C)C[C@H](NC(=O)[C@H](CC CCN)NC(=O)[C@H](CC
+CC(C)C c1n
+CC(C)(C) C(=O)O)C(C)(C)
+CC(=O)N (C)c2
+C5 (C)C)C
+C2=C (CC
+C2(CC C2
+C(=O)N[C@@H](CC (C)C)C(=O)N[C@@H](C
+C(=O)N CC2
+C(=C(C 1=O)
+Br c1cccc2
+=O) cc1
+=CC(= CN(C)C
+7CCCCC 7
+7=C (CC(C)(C)CC
+6 =O)N
+3)C(=O) O)cc1
+2CCC [C@@H]2C
+2)c3ccc( cn
+2)c3 ccn
+)cc3 OC
+)cc2 )cc1OC
+)cc1S(=O)(=O)N 3CCOCC3
+)cc(C)c1 Cl
+)c5cccc( F)c5
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+)c3ccc( Br)cc3
+)c2 ccc(F)cc2
+)C(F)(F)F) n2
+)C(=O)N[C@@H](CC(=O)O) C(=O)CO
+)C(=O)N 3C
+)C(=O)N (C)
+)C(=O)C=C (NC(=O)
+)C(=O) OCC(=O)
+)C(= N1
+) B(O)O
+(c3ccccc3) (c4ccccc4)c5ccccc5
+(O) C1=O
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+(CCC (C)C
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+%3 3
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+c8 c[nH]
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+c4ccccc4)C c5
+c4ccc(cc4 )C(=N)N
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+c4c3 )c1
+c4 ccsc2
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+c2c1 )c4
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+[C@] (CC)
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+[C@@H]3 [C@H](CN1C(=O)
+[C@@H]2CN(C [C@@H]2C1
+[C@@H](C)C(=O)N [C@@H](C)C(=O)N
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+O) ccc2
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+NC(=O)N(CC Cl)N
+NC(=O)CN2C(=O) CO
+NC(=N S(=O)(=O)c2c1)C3=C(O)
+N2CCN(CC2)C(=O) [C@@H](C
+N C3=O
+Cl. COc1cc(CN
+Cc1onc(C)c1 S(=O)(=O)N
+Cc1nc(s c1C(=O)N
+Cc1ccc2 [nH]c3
+C[C@@H]1 CC2
+CSCC[C@H](NC(=O)[C@H](CC (C)C)NC(=O)CNC(=O)
+CS(=O)(=O) c1ccc(C
+COc1ccc2 N
+COc1ccc( Nc2ncnc3
+COc1cc2 OC(=CC(=O)
+CN1 N
+CN(CC (=O)
+CN(C)C(=O) n1
+CCS SC[C@H](NC(=O)[C@H](CC
+CCN(CC)CCNC(=O)c1 c(C)
+CCN(C1)c2 nc3
+CCC c1ccc2
+CC1(C)S [C@@H]2
+CC(C)C[C@H](NC(=O) [C@@H](NC(=O)[C@H](C
+CC(C) NC
+CC(=O)N (
+CC [C@H]1O
+C3CCN(CC 3)
+C2CC2)n3ncc(\C=C\ 4/NC(=O)NC4=O)
+C1=O) N
+C1 (CCCC1
+C(=S )O
+=C1 N
+6CCC 6
+6 )CC1
+4CC4 )c1
+2CCCO 2
+/C/1=N\ C(=O)
+.F [B-](F)(F)F
+)n(n1 )c3ccccc3
+)c3cccc(c3 )C(F)(F)F
+)c2 C(=O)N
+)c(OCC )cc1
+)c [nH]
+)C(=O)N[C@@H](CCCCN )C(=O)N[C@@H](CC(=O)N
+)C( NC(=O)C
+(c1ccccc1)c2ccccc2 )N
+(F)( F)c2
+(CO CCN(C
+(CCC (C)(C)C
+(CC CC(=O)N
+(C)C)NC(=O)[C@H](CC(=O)N)NC(=O) [C@@H]4CCCN4C(=O)[C@H](CC
+o c3ccccc3n2
+ncc(nc3c2 )c4cnn(C)c4
+nc4 C
+n3 )CC2
+cnc3 nc(N)nc(N
+ccccc3 n2
+cccc( F)c3
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+c6cc( ccc6
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+c4ccc(OC )cc4)
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+c1 O)
+c(nc( NC(=O)N
+c(F)cc3C(=O) C(=
+\N=C(N )N
+[N+](=O)[O-] )c1
+[C@H]8 C
+[C@H]2 COC(=O)
+[C@H](O)[C@@]3(C)[C@@H]4CC [C@H]5C(C)(C)[C@H](CC[C@@]56C[C@@]46CC
+[C@H](N )CC
+[C@H](C)O 3)
+[C@H]( [C@@H](C)O)C(=O)N
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+[C@@H]5CCCN5C(=O) [C@H](CC
+[C@@H](O) [C@H]1CC[C@@H](O1)
+[C@@H](CO) NC(=O)CNC(=O)[C@H](CC
+[C@@H](CC (C)C)N
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+OC1=C(C (N
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+N(C =CC=C
+F)c( F)c1
+F c1cccc(c1)S(=O)(=O)N
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+Cc1cccc(c1 )N2
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+Cc1ccc(C (=O)
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+CS c1ccc(cc1)c2
+CN1C(=O) CO
+CN(C) CCC1
+CCc1nc2 c(C)cc(C)nc2n1C
+CCOC(=O)c1 [nH]c2
+CCN(CC)C C
+CCCC Oc1ccc(cc1
+CCCC CCOC(=O)
+CCC(=O)N( c1ccccc1)C2
+CC1(C) Oc2ccc3
+CC(C)(C) CN1CCC2(CC1)CN(c3ccccc3
+CC(C) Nc1nc(N
+CC(=O)N c1ccc(cc1
+CC \C(=C(/
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+C(N(C(=O) C1=O)
+C(C O
+C(=O)O) n(C
+C(=O)C(=CO c2c1
+C(=O) NC(=O)c5
+Br c1ccc(cc1
+7 =O)N
+4 )cc1
+3)N 4
+2)c3cccc(c3 )C(=O)N
+)cc 2)NC(=O)
+)c4ccc(OC (F)(F)F)cc4
+)c3ccc(OC )c(OC)c3
+)c(C)c(C) n2C
+)C(=O)N[C@@H](CC(=O)O) C(=O)O
+)C(=C([C@@H](O) C3=O)C5(C)C)C
+nn( cc1
+nc3 O
+n1 )S(=O)(=O)
+n1 )C2CC
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+cccc( O)c3
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+ccc( nc1
+ccc [nH]4
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+c4cn( nn
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+c4cccc( Br)c4
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+c2n c3cccnc3
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+c2ccc(Oc3 ccc4
+c2 ccsc2
+c1 c23
+c(cc( nc2
+c(F)cccc3 F
+\C(=N\C#N )\
+\C(=N/ O)\C
+[C@H](O)[C@H](C)C \C=C\C)N(C)C(=O)
+[C@H](NC(=O)[C@H] (N
+[C@@]2(C)[C@H]1CC [C@]3(C)[C@@H]2CC
+[C@@H]5CC[C@@]34C) [C@@H]12
+[C@@H]2C [C@@H]3
+[C@@H]1O) n2
+[C@@H](CC CN=C(N)N)NC(=O)
+[C@@H]%1 6
+Oc3 cc(OC
+OC1 C(O)
+OC(=O)CN1 C(=S)S\C(=C/
+OC (F)F)
+O=C( [C@@H]1
+O1 )[N+](=O)[O-]
+O)C(=O)N[C@@H](C c2ccccc2
+N=C(N)N C
+N(C c4ccccc4)C(=O)
+Cc1oc(cc1 )C(=O)N
+Cc1n nc(S
+Cc1cn2 c(
+C[C@]1 2CCC
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+COc1cccc(c1 )N2CCN(CC2)C(=O)
+COc1ccc( F)cc
+COc1cc(C )c(
+COC(=O) C(
+CN1C2CCC 1CC(C2)
+CCOP(=O)(OCC )
+CCN1 CCC(CC1)N
+CCCCC1 )N
+CCCC c1nnc(
+CCC(CC )(
+CCC(=O) OCC(=O)N
+CC2 =O)N
+CC1(C)[C@@H]2CC [C@@]1(CS(=O)(=O)N
+CC1 CCCO
+CC(O c1ccccc1
+C3 (CCCCCC
+C1=O) C
+C1 =C(C
+C(=O)N(C c5ccccc5
+C(=C (NC(=O)
+=CC(=O)C=C[C@]4(C)[C@@]3 (F)[C@@H](O)C
+5)C 2
+4CC (F)(F)C
+3CCC 2C3
+3)C c4ccccc4
+2CC3CC (C1)
+2C (SCC2=O)
+2)c3 cnc(N
+1)n2 cnc3
+1)c2 csc(N
+1) /
+1 N
+)n 7
+)ccc3 4
+)c5ccc(cc5 )C(F)(F)F
+)c4cccc(c4 )C#N
+)c3n oc(
+)c3ccc(cc3 )S(=O)(=O)N
+)c3ccc(cc3 )C(=O)O
+)c3ccc( OCC
+)c nc13
+)C3 CCN(CC3)C(=O)
+)C(=C1C )C2
+(Cc3ccccc3 )C
+(C)C)C (C)(C)C
+(C c2cccnc2
+#N) (C#N
+oc4ccccc4 c3
+nn nc3
+ncnc(N c4ccc(OC
+n5 cc(nn
+n2)c3ccc( nc3
+n(c4 )C5
+cccc1 c24
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+5 )C1=O
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+)NC(=O)[C@@H](NC(=O)[C@H](CC CNC(=N)N)NC(=O)[C@H](CO)NC3=O)
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+)C(=O) c3ccccc13
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+) [C@@H]4CC
+) [C@@H](O)C
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+[S @@+]
+[N+](=O)[O-] )\N
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+C(=O)N C=C
+=CC(=[N+](C)C)C =C3
+=CC(=O)C(= CC1=O)
+=C 1)N
+=C (C)C)
+7=C(CC(C)(C)CC 7)c8ccc(Cl)cc8
+5)C(=O) CC
+5) OC4=O
+4=O) c5ccccc5
+4) OC)\C
+4 )c(n2
+3 C1=O)
+1CCCC 1)c2
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+)c12) N
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+(=S )NC(=O)
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+[N-] S(=O)(=O)
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+[C@H]([C@H]( c2cccc(Cl)c2
+[C@H](O) C1
+[C@H](C) [C@H]5
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+[C@@]2(C) [C@H]1C
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+[C@@H](N 1C(=O)
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+I )C(=O)N
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+CS c1ccc(C
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+3)\C 1=O
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+1=O) c2
+/C/2=N /
+)cc1 )C(=O)N
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+)\C 1
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+)C2 (C)C)C(=O)
+)C(=O) CS
+) on2
+(CCN(CC 1)C(=O)
+(CC 2CC
+(C OC3=O)
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+c4cccc(c4 )C(=O)O)
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+c(C) cccc1
+\N=C(\N)/ NCCC
+\C(=C (\C
+[C@H]2 CCCN(C
+[C@H]2 C(=O)O)
+[C@H]( [C@@H]1O)N
+[C@@]2 (OC(=O)C)
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+O=C(N CCC
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+N2 CCCCC2
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+N NC(=O)c1
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+Cl )c1
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+COc1ccc( c(C)c1)c2
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+CO\C(=C/1\ NC(=O)[C@@H](
+COC(=O)Nc1nc2cc(ccc2 [nH]1)C3(O)
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+CC[C@H](C)[C@@H]1NC(=O)[C@@H]2CSSC[C@@H]3NC(=O)[C@@H](NC(=O)[C@H](CC (C)C)NC(=O)[C@H](CC(=O)N)NC(=O)[C@@H]4CCCN4C(=O)[C@H](CC
+CCOC(=O)C1=C(C) N=C2
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+C2 CCC3
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+C(=O) c3
+C(=C (C)C(=O)N
+Br )S(=O)(=O)N
+=CC(= CC2=O)
+3C=C (C)C(=O)N
+2CCN(CC2)C(=O) CC
+)n [nH]1
+)c2 O
+)c(C(=O)N C
+)NC(=O)[C@H](CO) NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC
+)NC(=O)[C@H] 1
+)CC c2c1
+)C1=O) O
+(F) (C
+(C)C)N )N
+(C)C (C
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+[nH] nn
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+[C@H](O)[C@@H](C OP(=O)(O)O)
+[C@H](O) C=C
+[C@@H]3 [C@@H](CC
+[C@@H](NC(=O) [C@@H](CC(=O)N
+[C@@H](C OC(=O)C)
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+OC(= C2
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+O=C(C c1cccs1)N
+O)C(=O) C
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+C(=O)C=C 3
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+5 CCC(C
+4)C (C)(C)
+4 C(=N
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+)n3 cc(nn
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+)c3cccc( NC(=O)
+)\C l
+)CC O2
+)C(=O)N[C@@H](Cc3ccccc3 )C(=O)N
+(CN 3C(=O)
+(C)C 3CCC1
+(C)C )C(=O)N[C@@H](C(C)C
+(C(=O)C (C)C)C(=O)
+(C (O)
+%2 4C
+s ccn2
+o c4
+nn cc2
+ncnc4s cc(
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+n5c(cc6 c(F)cccc5
+cccnc5 6
+ccc3 C
+ccc(OC (F)(F)F)cc
+ccc(Cl)c n2
+cc(c1)c2 ccc3
+cc(CC C(=O)N
+cc( n1)N
+cc( cnc2N
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+c4ccc(OC )cc3
+c3cccc(Cl)c3 F)c4
+c3ccc4 OCC(=O)N
+c3cc(ccc3 C)C(=O)N
+c2o c(CC
+c2n 4
+c2ccc(O)cc2 )C(=O)O
+c2ccc(F)cc2)c( [nH]1)c3
+c2cc( NS(=O)(=O)
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+c1cccc n1)C(=O)
+c( ccc3
+\C=N\ OC
+[nH]n cc1
+[nH]c(nc1 2)C3
+[OH -].
+[O-] S(=O)(=O)
+[C@]34C )C1CC
+[C@]2 3CCC
+[C@H](OP(=O)(O)OC [C@H]3O[C@H](C
+[C@H](C) C(=O)N[C@@H](C
+[C@@]4 6OO
+[C@@]2(C) [C@H](C
+[C@@]2 5
+[C@@H]2CCCN2C(=O) [C@@H]3CCCN3C(=O)
+[C@@H]1 NC(=O)C
+[C@@H](OC(=O)C) [C@@H](OC(=O)C)
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+NC(=O) c1ccc(cc1
+N=C 3CCCC
+N(C 3=O)
+N CCc1c[nH]
+FC(F)(F) Oc1ccc2
+Cl. NC
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+C\C(=N/ N=C
+COc1cc2 ncnc(
+COCC CO
+COC(=O)C1 =C(
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+CN1 C(=C(O)
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+CCN C2=O)
+CCCN C3
+C=C (C)C(=NC
+C3 O[C@H](CO)[C@@H](O)[C@H]3O
+C(=O)N C1
+C(=O) c3ccc(Cl)cc3
+C c2ccc(NC(=O)
+C )C(C)(C)C
+5)c6 ccc(F)cc6
+4CC4 )c3
+3CCCC3 )cc2
+3)c4c[nH] c5ccccc45
+3) [nH]
+3 )c(OC)c1
+2)c3ccc( F)c(
+)n4 nc(C)
+)n2 N
+)c6 ccc7
+)c5 cocc5
+)c4ccc(cc4 )C5
+)c4 ccccn4
+)c n3)
+)C(F)(F)F )c2
+)C(=O)N[C@@H](CC(=O)O) C(=O)N
+)C(=O)N(C 3=O)
+)C(=O) C2=C
+) (c3ccccc3)c4ccccc4
+(O)C(O) C2
+(CC 3CCCO
+(C)C)C(=O)N 1C
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+c7 cccc(
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+c5 c([nH]
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+c3ccc(F)cc 13
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+c2ccccc2 N(C
+c2ccc(cc2)N (C)C
+c1nc2ccccc2 [nH]1
+c1 sc(nc1
+c1 C#N
+c1 2)C3
+c(OC)c(OC )c(OC
+c( nn(
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+\N =N/
+[nH] cc(C(=O)C(=O)N
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+[C@H](OC(=O) CC
+[C@H](O)[C@@H](CO) O[C@@H](O
+[C@H](CN 1
+[C@H]( [C@H]2C(=O)N
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+[C@@H]1CC[C@H](CC 1)
+[C@@H](NC(=O)[C@H](C c3ccccc3)NC(=O)[C@H](CC
+[C@@H](C) CN
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+C[C@@H]( OC
+CSCC [C@H](N
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+CN c1ccc2
+CN C1=N
+CCCCC [C@H](O)\C=C\
+C2=O) c(OC)c1
+C1=N N(C
+C(=C (C)C(=O)
+C )CC2
+5CCC4CC 5
+5CCC 4C
+3CCCC 3)c4ccccc4
+3C [C@@H](C
+3)c4 ccsc4
+2CCc3c(C 2)
+2)N S(=O)(=O)
+2) [C@@H]3
+2 [C@@H]3C[C@@H]3C
+1C =CC
+)n2 c1
+)n cc2Cl
+)cc2 Cl)c1
+)cc 7
+)c6 cocc6
+)c5ccc( n5
+)c2ccc3 OCOc3c2
+)c(cc1OC )[N+](=O)[O-]
+)c(Br )c1
+(O 2)C(=O)O
+(C)C )C1=O
+( [C@@H]1
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+c5ccc6ccccc6c5 )NC(=O)C
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+c2cc(cc(c2)C(F)(F)F )C(F)(F)F)c3
+c2c(O) cc(O)cc2
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+c1ccc(cc1 )C(=
+c1c(O) ccc2
+c(Cl)cc cc6
+\C(=N/ C#N)\N
+[C@]3(C) [C@H]2
+[C@]2 (NC(=O)
+[C@](C)(O) [C@@H]1O)
+[C@H]7 O
+[C@H]3 CC(=O)
+[C@H](O) [C@@H](CC
+[C@H](C[C@H](O)[C@H](C c2ccccc2)NC(=O)
+[C@H](C) C(=O)O)
+[C@@]2(CC N(C
+[C@@]1 3C(=O)N
+[C@@H]5 O[C@H](CO)[C@@H](O)
+[C@@H]4C [C@H]5
+[C@@H]2C )[C@H](C)C
+[C@@H]2 O1
+S c3ccccc3N
+S c1nc(
+Oc3ccc(NC(=O)N c4ccc(cc4)C(F)(F)F)cc3
+O[C@H]1 O[C@H](C
+O[C@H](CO)[C@@H](O) [C@H]3
+O[C@@H](CNCC c1ccc(NS(=O)(=O)
+OCC (O
+OC(OC 2C
+O=C(C Oc1ccc2
+O)C(=O)N[C@@H](CC CCN
+O) c2c1
+O [C@@H]5C
+NC(=O)c2csc(n2)c3cc(O)c(nc3 c4csc(n4)[C@@H]5COC(=O)c6c7CO[C@@H]([C@H](NC(=O)c8csc1n8)c9nc(cs9)C(=O)N5)[C@H](O[C@H]%10C[C@](C)(O)[C@@H]([C@H](C)O%10)N(C)C)C(=O)OCc%11cccc(c7%11)n6
+NC(=O)c1 nc(
+NC(=O)[C@H](CC CN=C(N)N)NC(=O)
+N)NC1=O) [C@@H](C)O
+N 4CCC
+FC(F)(F) c1cc(N
+Cc1nc2 cccc(c3
+Cc1cnc( cn1)C(=O)N
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+COc1cc(N c2c(cn
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+COc1c( ccc2
+COc1 cc(C)cc2
+CN3C(=O) C4=C(C=C3c2n
+CN1CCN(CC1)c2 nc3
+CN(C) c1ncnc2
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+CN 4CCCC4
+CCN1 CCC(CC
+CCN(CC1 )S(=O)(=O)N
+CC(C)(C)OC(=O)N 1CC
+CC c1ccc(cc1)C(=O)
+C(=O) c4ccccc4
+C(=CN1 c2
+=CC(=O)N (C=C
+4CCN(CC4)c5 ccccn5
+4CC5 CCC(C
+4C(=O) CCC4=O
+4) s2
+4 )C(=O)OC(C)(C)C
+3/ C(=O)N
+3)C(=O)N 4CC
+3) cc(c1
+2CCN(C)CC 2
+2CCCCC2 )c1
+2CCCC 2)c3ccccc3
+2 =C(
+2 .OC(=O)C(F)(F)F
+1) [nH]
+)cc3 CC1=O
+)c4 cc3
+)c3ccc(cc3 )S(=O)(=O)N
+)c1 .OC(=O)C(F)(F)F
+)N 4CCNCC4
+)CC2 )c4ccccc4
+)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCCNC(=N)N )C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CCCCN
+)C(=O)N (C)C1=O)N
+(CN1 C(=O)
+(C) CCCC1
+(=O)N )NC1=O)C(=O)N
+%1 3)
+ns nc1
+nn c(CC
+nn 5
+nccn 3
+ncc(C(=O) C4CC4
+nc3 ccccc13
+nc2 n1
+n3 cccn
+n2 )C(F)(F)F)
+cnc( cc2
+cn ccc5
+cc(ccc2 nc1
+cc(CC CCN
+c5n ccc(
+c5ccccc5 )c2c1
+c4ccccc4N 3
+c4ccccc34 )C2=O
+c4cccc(c4 )C(=N)N
+c4cccc( n4)N
+c4ccc(Cl)cc 4C
+c4cc(OC )c(O)
+c3cs c(C)n3
+c3ccc( OS(=O)(=O)
+c3cc(F)ccc3 F
+c2nc3ccccc3[nH] 2)cc1
+c2ccccc2C1=O) c3ccccc3
+c2cccc( NS(=O)(=O)
+c2cc(C)o n2
+c2c(O) c3
+c1ccccc1 [N+](=O)[O-]
+c1ccc( F)c(F)
+c(O) cccc3
+c(CC=C(C)C )c3
+\C=C \C=C\C(=O)N
+\C(=N\ NC(=O)
+[nH] 1)c2
+[I-]. C[N+]1
+[C@H]1 CN(C(=O)O
+[C@@]3(O S(=O)(=O)
+[C@@]3(C)CC [C@H]4
+[C@@H]4CC [C@@H](N)
+[C@@H]3 [C@@H](O)[C@H](N)C[C@H](N)[C@H]3O[C@H]4
+[C@@H]2 [C@H]3CC
+[C@@H]2 [C@@H]1
+[C@@H](O) [C@H]2
+S 5
+P(=O)(O)O) c2n1
+OCC 5
+OC1 CCN(CC
+OC(=O) CCCCC
+OC(=O) C2=C
+OC c1ccccc1)c2
+OC [C@@H]3
+O) S(=O)(=O)N
+O c%10
+Nc1cnc(cn 1)c2ccc(C
+N(C)C(=O) NC(=O)c2
+N C4=O)
+Cc1ccc(NC(=O) c2cccc(c2)C(F)(F)F)cc1
+C[C@H]1 CCCN1
+C[C@@H]1 O
+C[C@@H](CO)N1C[C@H](C)[C@H](CN (C)C
+COc1cc(ccc1 c2cn[nH]c2
+CCN(C(=O) c23)c4ccc(cc4
+CCN( [C@@H](C
+CCCN (C)CC
+CCCC C
+CC1(C)CCC(=C(C1)c2ccc(Cl)cc2)CN3CCN(CC3)c4ccc(C(=O)NS(=O)(=O)c5ccc( NC
+CC1 (NC(=O)N(CC(=O)N
+CC(=O)c1 c(O)
+CC(=O)N1 CCN(C
+C=C 5
+C4CCN(CC 4)C(=O)
+C2CCCC (=O)N
+C)\ O
+C(=O) OC1
+C c3occc3
+C S(=O)(=O)C
+Br. [Br-].
+=CC(=CC 4=O)
+= NC(=O)N
+3CCC(CC3)c4 no
+3)c4ccc( F)c(
+2)c3ccc( NC(=O)
+2)c3 cccc(Cl)c3
+1CCC(CC 1)O
+)ccc1 F
+)c5ccc( F)c(
+)c4ncccc4 C(F)(F)F
+)c4cnn(C )c4
+)c3 ccn
+)c(O) c(O)
+)NC(=O)[C@@H](NC(=O)[C@H](CC CN=C(N)N)NC(=O)
+)N =[N+]=[N-]
+)CCOCC 1
+)CC 3)C(=O)
+)C(=O)OC 4
+)C(=O)N(C 1=O)
+(C 4
+%2 1C
+% 7
+#N )c2
+nc(nc2 4)c5occc5
+cn n(CC
+cn c%10
+ccc(Cl)cc2 4
+ccc(C) nc4
+cc3 c(N
+cc(Cl)cc (C(=O)N
+c4ccc(F)cc4 )c3
+c4cc(cc(c4 )C(F)(F)F)C(F)(F)F)
+c4cc(F)ccc4 F
+c3cn [nH]c3
+c3cccc (CC
+c3cc(O) cc(O)c3
+c3cc(Cl)ccc3 Cl
+c2n c(CN
+c2ccc3ccccc3c2 )cc1
+c2cc(ccc2N1C(=O) c3ccc(O)cc3
+c2cc( cs
+c2c1 )c3ccccc3
+c2c(Cl)cc cc2
+c2c( F)c(F)
+c2 cc(Cl)c(
+c2 )c3
+c1ccccc1)C(=O) O)C(=O)N
+c(C) onc4
+\C=C\2/ CCC\C(=C/
+[nH] c(CN
+[C@]67 O)C)
+[C@]4 (CCN3CC
+[C@]2(C) [C@H]1
+[C@](C) (CCC
+[C@H](C c3ccccc3
+[C@@] (N)
+[C@@H]4 [C@H](CC
+[C@@H]2C [C@H]2
+[C@@H](O)[C@H](C c3ccccc3)NC(=O)
+[C@@H](O) CC3
+SSC[C@H](NC(=O)[C@H](C c5ccccc5
+Oc1ccc( CC2
+OC2 =C1
+OC2 (CCN(C
+OC(C)(C) O[C@@H]2
+Nc1n c(C
+NC(=S)N C1
+NC(=O)CN S(=O)(=O)
+N2 S(=O)(=O)
+FC(F)(F) c1cnc(N
+F)c n4
+Cc2 o
+Cc1n nc(N
+Cc1cccc (CC
+Cc1ccc( s1)C(=O)N
+Cc1c( [nH]
+C[C@H]1 N(CC
+COc1cc2ncnc(N c3ccc(F)c(Cl)c3)c2cc1
+COc1cc2CCN(C c2cc1OC)C(=O)
+COc1cc(CC =C
+CO\C=C(\C(=O)OC)/ c1ccccc1CO
+CN(CC(=O)N c1ccc(C)cc1
+CCCC(=O) c1cn
+CCC2 =C(C
+CC(C) CN1C(=O)N(C)C(=O)
+CC(=O)N[C@@H](CC (C)C)C(=O)N
+CC(=O)N[C@@H](C c1cc(F)cc(F)c1
+C4=C )\CCC
+C4 5CC6CC(CC(C6)C4)C5
+C3 (CCN(C
+C2CC3CC(CC1C3 )C2
+C2= S
+=C(N)N (C
+9 )c%10
+8 OO
+6 =O
+4CCc5 cccc(C(=O)N
+3CCN(CC O)CC
+3CCC4(CC 3)N
+3C4 O[C@H](CO)[C@@H](O)
+3C (N
+2)C3 CCCCC3
+2)C(=O)NC c3ccccc3
+1)C 2C
+)c4nc5 cc(F)ccc5
+)c4cccc( O)c4
+)c (CC(=O)N
+)\N C2=O
+)Oc3 cc(ccc3
+)CC [C@H]1
+)CC 2CC
+)C(=O)OCC )C
+)C(=O)NC c5ccccc5
+) \C=C
+([O-] )C
+(F) F)cc3
+(COC (=N
+(C)c2 C1=O
+( Oc3
+sc( NC(=O)C
+s c3c2
+ncc(C(=O)N )c(N
+nc(n3)N 4CCOCC
+n6cn c7
+cn c3
+cn c%5
+ccn 4
+ccc(Cl)c( F)c3
+cc8 [C@H](NC6=O)
+cc2c1 N
+cc(F) ccc6
+c5ccc( cn5
+c4nn nn4C
+c4cs cn4
+c4ccc(Cl)c n4
+c4c( F)cc(
+c4 c(CN
+c3oc(nn 3)c4ccccc4
+c3ccccn3 )C
+c3cccc( NC(=O)N
+c3ccc(F)cc3)c4 ccc(F)cc4
+c3c(C)cc(C) cc3C
+c3c( n2)N
+c2occc2 )C
+c2ccncc2 )cc1
+c2ccc(C#N )c(c2
+c2ccc( OCC=C
+c2cc( ccn2)c3
+c2 c(Cl)cc(Cl)cc2
+c1s c(CC
+c1cccc( NC(=O)
+c1ccc(C (=O)
+c(c2 )c34
+c(N)ncnc2 n1
+c(N)n c5
+c(F)cccc5 F
+c(C)c(C )c4
+\C=N\NC(=O)c2 ccncc2
+\C=N\ O)
+\C(=N/O)\ c2cccnc2
+[O-][N+](=O) c1cc(cc(c1)[N+](=O)[O-]
+[C@H]3 [C@@H]2
+[C@H](O [C@@H]1
+[C@@H]2CC[C@H](CC 2)NC(=O)
+[C@@H]([C@H]3 C)
+[C@@H](OC c3ccccc3)
+[C@@H](O)C NC(=O)CO)
+[C@@H](O) [C@@H](O)[C@H](O)
+[C@@H](CO) N(CC
+[C@@H](C) C(=O)NC
+[C@@H]( [C@@H]3
+[C@@H]%1 7
+P(=O)(O)O) P(=O)(O)
+Oc3cccc(c3 )C(=N)N
+Oc1ccc2C[C@H]3N(CC4CC4)CC[C@@]56[C@@H](Oc1c2 5)
+OC(=O)c1 cccc(C
+OC (c3ccccc3)c4ccccc4
+O=C1NC(=N C(=C1
+O=C(N c1ccc(cc1)C2
+O=C(N [C@H]1
+O c2cccc(c2
+Nc1nc(N )c2nc(CN
+Nc1 c(cnn1
+NS(=O)(=O) C(F)(F)F
+NC(=O)c4sccc4 c23
+NC(=O)c2cc3 cc(Cl)ccc3
+N(C)C(=O)N(C)C(=O) c13
+Clc1ccc2 c(ccn
+Cc1ccccc1 S(=O)(=O)N
+Cc1c2COC(=O) c2ccc1
+C\C(=C/C=C/ C(=C/
+C[C@H]1 C(=O)N
+COc1cccc(c1 )C2=C
+COc1ccc(OC )c(CC
+COc1cc2 c(C
+COC1CCC2(CC1)C c3ccc(cc3C24
+CN(CC #
+CN [C@H]1
+CN 3CCCCC3
+CCOC(=O)c1 c2CCCCc2sc1
+CCN 4
+CCCS c1nc(
+CCC(C) [C@H]1O
+CC1 CCCN(CC
+CC(C) [C@@H](C)
+CC(=O)N(C 1=O)
+CC 4CCCO4
+C6=O) C
+C4CCCCC 4)n
+C4 CCN(C)CC
+C3CC4CC(CC2C 4)C3
+C3=O) c1
+C3=N CCS
+C) cc(OC)c1OC
+Br c1cccc(c1
+Br )C(=O)O
+=O) cc2
+=N N
+=C(N)N (CC(C)C)C(=O)N
+5)C 6CC6
+4)n5 cn
+4)C 5CC5
+3CCC(CC 3)n4
+3 )C2=O
+3 )C2=O)
+2CC SCC
+2C 1
+1)C(=O)N CC
+)n4 ncnc4
+)n(n1 )c4ccccc4
+)n c5
+)cn2 C
+)ccc3 n2C
+)cc2 )C(=O)
+)c7ccc( cc7
+)c6 cccc(
+)c2Cl )nc3cc(C(=O)N
+)[C@@H](C) O)C(=O)O
+)NC(=O)[C@@H](NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CO)NC3=O) [C@@H](C)O)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](C
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+[C@H](O[C@H] 2C
+[C@H](C OC(=O)N
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+[C@@H]2C [C@]2(NC(=O)
+[C@@H]2 [C@H](C)OC(=O)
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+C) NC(=O)
+C(=O)NC (
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+=CC(=O)C=CC 4
+=C(C (
+=C (C)C(=O)
+6/ CO[C@@H]7
+6) ccc5
+6 )c2c1
+4)n c12
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+3 )C1=O)
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+2CCC(CC 1)
+2CC3CN(CC3C2 )C(=O)
+2)c3ccc(cc3 )C(=O)N
+1 )C(=O)N[C@@H](C
+/C (C)C(O)C(C)C(O)C(C)C
+)nnc1 3
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+)ccc1 OCCN
+)cc2) s1
+)cc1 I
+)c4n cccn4
+)NC3=O) C(=O)N[C@@H](CCCNC(=N)N
+)CC (O)(C
+)C(=O)NC (
+)C(=O) OCCC
+) [C@@H]5
+(OC(=O)C )C(OC(=O)C
+(N =C3
+(CC(=O) [O-]
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+(=N N1
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+c1c(C) nc2
+c(I )c2
+\C(=C\C=C/ 4\C
+\C(=C\ C(=O)
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+[C@]3 (CN
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+[C@@H]2 CCN(CC
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+[C@@H](O [C@@H]8
+[C@@H](C)OC(=O) [C@H](CC(=O)
+[C a
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+S CCN2C(=O)
+Oc3 c(
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+COc1cc(ccc1 Nc2nc(N
+COc1cc( Br
+CNC(=O) [C@@]12C[C@@H]1[C@H]([C@H](O)[C@@H]2O)n3cnc4
+CN1C(=O)C=C(N=C1 N2CC
+CN(C)S(=O)(=O) c1ccc(cc1)C(=O)N
+CCS SCCNC(=O)
+CCNC(=O) [C@@H]1CCCN1C(=O)[C@H](CC
+CCN(C c6ccccc6
+CCN 2CCC
+CCCCCCCCCCCC n1
+CCCCCCCC NC(=O)
+CCCCCC C
+CCCC O)
+CCCC =C
+CCCC (C)N
+CCC c2sc1
+CC(=O)N C[C@@H]1
+CC c5
+CC [C@@H](
+C2=O) c4ccccc4
+C(=O)N[C@@H](CC (C)C)C(=O)O
+C(=C(N) Oc3
+=CC(=O)NC %4
+8 CCCC
+5)n n2
+5 )C(=O)C
+4CCc5cccc(C(=O)N c6nc7ccccc7s6)c5
+4CCC(CC3) (CO
+4CC 7CC7
+4 (CCCC
+3) ccc1O
+2CCN(CC2)c3ccccc3 F
+2CCN(CC2)C (c3ccccc3)c4ccccc4
+)ccc1 OCC(=O)N
+)cc2 )C(=O)N
+)c3ccccc3 n2C
+)c2ccc3 ccccc3c2
+)\ C(=O)N2
+)C(F)(F)F )c3ccccc3
+)C(=O)O) C(C)(C)
+) (C(=O)
+(F)(F) F)c4
+(CC (C)C)
+(C(=O) OC(C)(C)C
+s 2)C
+no c(CN
+nn3 4)c5
+n3 c2n
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+ccccn2 c1
+ccccc%1 0
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+c6ccc(OC )cc6
+c5 ccc(CN
+c4o c(C)cc4
+c4csc( n4)c5
+c4cccn c4n3
+c4ccc(F)cc4 )CC3
+c3ccc(cn 3)N
+c3ccc(cc2 3)c4cn
+c3ccc(OC )cc2
+c3ccc(F)cc3 Cl
+c3cc(Br )ccc2
+c2nc3ccccc3 nc2N
+c2cccc(N )c2
+c2ccc3ccccc3c2 )
+c2ccc(cc2 )C(=O)\C=C\
+c2ccc(OC)cc 2)c3
+c2ccc(OC(F)(F)F)cc 2
+c2ccc(N c3c4ccccc4nc5
+c2cc(ccc2 N1
+c1n ccc2
+c(c1 )C(=O)O)
+c(O) c4
+c( co
+c( F)c(N
+\C(=N\O)\ N
+\C(=C/ C
+\C(=C(/ N
+[nH]nc( c3cc(ncn
+[n+] 1C
+[C@H]3 C(=O)NC(C)(C)C
+[C@H](C c6ccccc6
+[C@@](C)(C[C@@H](C)C(=O) [C@H](C)[C@@H](O)[C@]1(C)O)OC
+[C@@H]5 O[C@H](C)C[C@@H]([C@H]5
+[C@@H]3O [C@@]4(C)CC
+[C@@H]3 [C@@H]2
+[C@@H]1[C@@H]2 [C@H](CN1C(=O)
+SC(=C(N 2C1=O)
+O[Si](C)(C)C(C)(C)C) [C@@]3(OS(=O)(=O)
+O[C@H]1[C@@H](O)[C@@H](O[C@@H]1 COP(=O)(O)
+O[C@@H]( [C@@H](O)[C@H](O
+OCC1 OC(CC1
+OCC1 (C
+OC c1ccccc1
+O=C1 OC
+O) [C@@H](O)[C@H](O)
+NC(=S)N \N=C\1/
+NC(=N)NCCC [C@H](NC(=O)[C@H](C
+N=C 4CCCC
+N(C c2ccccc2)C
+F)c2 c1
+F )C(F)(F)F)
+Cn1cccc1 C(=O)N
+Cn1c(Nc2c(Cl)ccc(CNC(=O)C(C)(C)C )c2Cl)nc3cc(C(=O)N
+Cl. NCC
+Cc1ccc2c(c1 )c3
+Cc1c( nnn1
+COc1ccc( CCN2
+COC(=O)N )C(=O)
+CO [C@]1
+CN1 Cc2ccccc2
+CN1 Cc2
+CC[C@H]1OC(=O)C[C@@H](O) [C@H](C)[C@@H](O[C@@H]2O
+CC[C@@H]1\C=C(/C)\C[C@H](C)C[C@H](OC) [C@H]2O[C@](O)([C@H](C)C[C@@H]2OC)C(=O)C(=O)N3CCCC[C@H]3
+CCOC(=O)c1c(C)[nH] c(C(=O)
+CCCN(CCC )C1CC
+CCCCN (C)C
+CCC2(CC1)C N(C
+CCC2 (CCN(CC
+CCC1 =C(
+CCC(C)(C) C(=O)
+CC1=C SC2=N
+CC1(C)C 2CC
+CC(C)c1ccc2c(CC [C@H]3
+CC(C c1ccc(cc1)C#C
+CC(=O)N c1ccc(cc1)C(=O)N
+CC(=O)N CCC
+CC(=O) OCC2=C1C=C3N(C
+CC(=O) O)c2
+C3CCN(C c4ccccc4)CC3
+C3 CCN(C)CC
+C1=C (C)C(=O)
+C(F)(F)F) N
+=C 2C=C
+4CC4)CCC5 6
+3CCN(CC3)C 4CCCCC4
+3CCCC 3)C(=O)N
+3=O) c45
+3 =CC
+2)c3ccccc3 C
+1)c2ccc( NC(=O)
+)ncn 2
+)c4ccc(cc4 )N
+)c2cc1 OCC
+)c2 ccc(F)cc
+)c(O) c1O
+)c(F) c12
+)\C(=O)N c2c1
+)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](C c3c[nH]c4ccccc34
+)C(=C )CC
+) CCCN
+(OC(=O)C )C2
+(CO)C O
+(CO) O
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+ccnc( c1)c2
+cc(NC(=O) c3ccccc3
+cc nn4
+cc [nH]n
+c6 cn(C
+c4ccccc4 C(F)(F)F)
+c4ccccc4 )c3c2
+c4c([nH] c5ccccc45
+c3ccccc3 )c4ccc(
+c3ccc(F)cc3 )c1
+c3ccc(F)cc 3)c4ccccc4
+c3cc(C) nc4ccccc34
+c3c(OC )cccc3
+c3[nH] cnc3
+c2nc( ccc1
+c2ccccc2Cl )cc1
+c2ccccc2 )nnc1
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+c2c3 ccccc1
+c2c(cn c3ccc(cc23)c4
+c1 c(CN
+\C=C\ C(=O)OC
+\C(=N/ OCCN
+[C@]3(CC CN(C
+[C@]1 (CC)C
+[C@H]4O[C@H]([C@H](O) [C@@H]4O)
+[C@H]4 C3
+[C@H]2 CN1
+[C@H](O)C(C)(C) [C@@H]4CC
+[C@H](C)[C@H](O)[C@@H](C) [C@@H](O)[C@@H](C)
+[C@@]6 7
+[C@@H](O[C@@H]%1 2
+[C@@H](C)C(=O)N[C@@H](CC C(=O)O)
+[C@@H]%11 CCCN%11
+O[C@H](C)C[C@@H]( [C@H]6
+O[C@@H]2 O[C@H](C
+OCCCC CCO
+OCC NC(=O)C
+OC1 CCN(C
+OC(=O)C(F)(F)F. O=C(N
+OC(=O) CCC(=O)
+O) S(=O)(=O)C
+NCCCC CCN
+NC(=O)COC(=O) \C=C\
+NC(=O)C2 Cc3ccccc3
+NC(=O) [C@@H]1CCCN1C(=O)
+N=C N(C1=O)
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+C[C@H](CO)N1C[C@H](C)[C@H](CN (C)S(=O)(=O)
+COc1ccc( NC(=O)C2=C
+COc1cc(OC)cc(c1 )N
+COC(=O)C1 C2CCC(CC1
+CN(C)[C@H]1 [C@@H]2C[C@@H]3C
+CCn1 c(
+CCS(=O)(=O)N 1
+CCOC(=O)C1=C (CN
+CCOC(=O) CN1C(=O)
+CCN CC1)c2
+CCCN(CC 2)C(=O)
+CCCCCCCCCCCC C
+CCCCC NC(=O)[C@H](C
+CC1CN(CCN1 c2cccc(C)c2
+CC1CCCC(C) N1C(=O)
+CC(C)(O)C #
+CC(C) [C@@H](NS(=O)(=O)
+CC(=O)O[C@@]12CO[C@@H]1C[C@H](O) [C@]3(C)[C@@H]2
+CC c2ccc(cc2)S(=O)(=O)N
+C=CC n1
+C2CCCC CC2
+C(C) (C
+8 CCOCC
+7 )ccc5
+5 CO
+4CCN(CC O)CC
+3CCC(CC3)N 4C(=O)N
+3)c4occc4 )cc1
+3 /C
+2CCN(CC Cc3ccccc3
+2CCC[C@H]2C #N
+2)c3ccc4 ncc(nc4c3)c5cnn(C)c5
+2)C =C1
+)nc2 4
+)n2 ccnc2
+)c3 cnc4
+)c1 cc(ccc1
+)c1 \C=N\O
+)c(n2 )C#N
+)c(c2 C
+)c %14
+)\N 1
+)O c2c1
+)C3=C N(C)C(=O)
+)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](C c4ccccc4)C(=O)N
+)C(=O)N[C@@H](C c3ccccc3)C(=O)N
+)C(=O)NC(=O)N C3=O
+)C(=O)N 2
+)C(=O)C 3
+(O)C 5
+(CCOCC 1)c2
+(C)C)N(C)C(=O)[C@@H](NC(=O)[C@H](CC (C)C)N(C)C(=O)
+(C)C)C(=O)N [C@@H](C)C(=O)N[C@@H](C
+(C)C)C (C)C)C(=O)
+(=O)O) CCN(CC(=O)O)CCN(CC(=O)O)
+(=C(N =C3
+nn[nH] n3
+nc3NC 5CC5
+nc(nc24)c5occc5 )n1
+n(C )c3
+ccn3c(cn c3c2)c4cccc(NC(=O)NCC(F)(F)F)c4
+ccn 5
+ccc(Cl)c(Cl )c1
+cc2 3)cc1
+c9 ccc(cc9
+c6ccccc6 n5
+c5c6 CCCC
+c4ncccc3 4
+c4ccccc4 c5ccccc5
+c4ccccc4 C(F)(F)F
+c4cccc( N
+c3ccccc3)C(=O)N 2
+c3ccccc3 )C1
+c3ccc(O)cc3 )C(=O)N
+c3cc4 OCCOc4
+c3cc(ccc3 C2
+c3cc( Oc4
+c2cccc(c2 )C(=O)O
+c2ccc3[nH]n cc3c2
+c2c(F)cccc2 F)c3
+c1o c(CC
+c1cnc(nc1 )N
+c1ccccc1 )C(=O)O)
+c12 )c4ccccc4
+c(cn2 )cnn3
+c(O) cccc2
+c(C=O) c(O)
+\N=C(/NC#N )\N
+\C=N\ NS(=O)(=O)
+\C(=C\ c3ccccc3
+[nH] cc(CCN
+[O-]C(=O)C(F)(F)F. [O-]C(=O)C(F)(F)F
+[C@H]3 CNCC
+[C@H](O) [C@@H]1C
+[C@H](CS c1ccccc1)N
+[C@@H]3C[C@@H](O) CN3C(=O)
+[C@@H]23)C (=C
+[C@@H]1O)n2cnc3 c(NC
+[C@@H](N 2C=CC(=O)NC2=O)
+Oc6 cc(O)ccc4
+OC) C
+OC(=O)CC C(=O)O)
+OC c6ccccc6
+OC )C(=O)O
+O)N C1=O)
+NC1=C(C#N )C2
+NC(=O)[C@H](CO)NC(=O) [C@@H](C
+NC(=O)C(N (C(=O)
+NC(=O)C =C(
+N(CC #C
+N#C \C(=C/
+Cc1nc(O)c2 cc(CN(CC#C
+Cc1ccccc1 )C(=O)N
+Cc1ccc(C )c(C
+Cc1cc(O)cc(C)c1C[C@H](N )C(=O)N2C
+C[C@]12CC[C@H]3[C@@H](CC =C4C[C@@H](O)CC
+C[C@]12CC [C@@H](C
+C[C@]1 (O)CC
+C[C@H]( [C@H]1CC
+C[C@@H]1CC[C@@H](Cn2c(nc3 cc(nc(c4cncc(Cl)c4)c23)C5
+COc1nc( ncc1
+COc1ccc(cc1)c2 csc(
+COc1ccc(cc1 )C#C
+COc1ccc(\C=C\ c2cc(OC)cc(OC)c2
+COc1ccc( OCCN
+COc1ccc( O)c(
+COc1ccc( NC(=O)CC
+COC(=O) c1ccc(OC
+CO [C@H]1CC
+CN1CCCN(CC 1)c2
+CC[N+] (C)(CC
+CC[C@@]1(C[C@H]2CN(CCc3c([nH]c4ccccc34)[C@@](C2)(C(=O)OC)c5cc6c(cc5OC)N(C)[C@H]7[C@](O)([C@H](OC(=O)C)[C@]8(CC)C=CCN9CC[C@]67[C@H]89)C(=O)OC)C1 )NC(=O)
+CCOCC n1
+CCCC [C@H](CN(O)C=O)
+CCC3 =O
+CCC c4c3
+CCC c1nn(C)c2
+CC2 =CC(=O)
+CC(O c1ccccc1)C(=O)N
+CC(C)Oc1ccccc1 N2CCN(CC
+CC(C)(C)n1 nnnc1C(N
+CC(C)(C) c1nc(
+CC(C) c1ccc(cc1)S(=O)(=O)N
+C5CCCCC 5
+C3 (C)CC
+C2CCN(CC 2)
+C2CCCC(=O)N 2C
+C2CC3CCC(C 2)N3C
+C(=O)N 4CCCC4
+C(=O)N (CN
+C(=O) COC(=O)
+C )c5
+Br )n2
+=C(Br )C(=O)N
+7 CCN(C)CC
+6C 5
+6 )C5
+4)c(n2 )c5cc(Cl)ccc5
+3)C (=N)N
+2CCCC C1
+2)c4 ccc(Cl)cc4
+2) N
+1CCC[C@H]1 C(=O)NC
+1 )n
+)cc( Br
+)c4 cc[nH]
+)c3n2C (C)C)C
+)c3ccc4 OCCOc4c3
+)c(s 1
+)c(n2 )C(=O)N
+)c(C#N )c1
+)S(=O)(=O)N (C
+)S(=O)(=O) c2ccc(C)cc2
+)C3 (CC#N)
+)C(=O)O c2c1
+)C(=O)N[C@@H](CC(C)C )C(=O)N[C@@H](CO)C(=O)N
+)C (O)(C
+) CCCCC1
+(S )
+(O[C@H]1[C@H](O)[C@H](O)C O)C(=O)O
+(Cc2occc2 )C
+(CCCCC 4)
+(C)C )cc
+oc(nc2c1 )c3ccc(
+nc( ccc3
+n3 cnc(
+n2cnc3 c(N
+n1 cnc(
+cccn2c1 N(C)C(=O)
+ccccn3 )cc1
+ccc3 C2=O
+ccc(Cl)cc 4Cl
+cc(Cl)cc(Cl )c5
+cc( \C=C\C(=O)N
+c7ccc8ccccc8 c7
+c5ccc(OC )cc
+c4ccc5ccccc5 n4
+c4ccc(cc4 )N5CCOCC5
+c4ccc(OCCN 5CCCCC5)cc4
+c4c5ccccc5 nc6
+c4c(F)cccc4 F
+c4 cccn4
+c3n cc(Br
+c3cccs 3)C(=O)
+c3ccccc3)n (
+c3cccc( NC(=O)C
+c3ccc(OC c4ccccc4)cc3
+c3ccc(Br )cn3
+c3c5 cc(OC)c(OC)cc5
+c2ccccc2Cl )c3
+c2ccccc2 F
+c2ccccc2 )nc(N
+c2ccc(cc2 C(=O)N1C
+c2ccc(O)cc2 )NC(=O)[C@H](C
+c2ccc(F)c( F)c2
+c2cc(o n2)c3
+c1sc(CC (C)C)cc1
+c(C 2=O)
+\C(=C1 O)O)
+[nH]c2 c(OC)c1
+[C@H]6[C@H](C)CC [C@@H]([C@H]3C)
+[C@H]6 C[C@H](CC
+[C@H](OC(=O) [C@H](CC
+[C@H](OC 3=O)
+[C@H](O) C(=O)N3C
+[C@@]3(C) [C@H](CC
+[C@@H]3 4)
+[C@@H]1O)N2C =CC(=NC2=O)N
+[C@@H]1 C(=O)O
+[C@@H]([C@@H](C)O)C(=O)N[C@@H](CC (=O)N
+[C@@H]([C@@H](C) O)
+[C@@H]%10 CCCN%10
+OCCN1 CCN(CC1)c2
+OCC1 c2ccccc2c3ccccc1
+OC [C@@H](O)[C@@H](O)
+O=C1 NC(=O)
+NC(=O)[C@H](C)NC(=O)[C@H](C)NC(=O)[C@H](CC (C)C)NC(=O)[C@H](CC
+NC(=O)[C@H]( NC(=O)[C@H](N
+N 3CCCCC3
+F)cc( cc1
+F)c( F)c2
+F)c (C(=O)
+Clc1ccccc1 C
+Clc1cccc( N
+Cl. CN(C)C
+Cc1n ccc(
+Cc1ccccc1 N2
+C[C@H](N c1nccc(
+C[C@@H]1CN(CC N1C(=O)
+COc1ccc2c(c1 )c(
+COc1ccc( NC(=O)CO
+COc1ccc( CCNC(=O)C
+COC(=O)C1 =C(N
+COC(=O) C[C@@H]1
+CO [C@@]1
+CN2C(=O) c3ccccc3
+CN1CCC(CC 1)n2
+CCc1n cnc(N
+CCc1 c(C
+CCN(CC)CCNC(=O)c1c(C) [nH]c(\C=C\2/C(=O)N
+CCCn2 ccnc2
+CCC 4C
+CCC 4
+CC1=CC(=O)N 2
+CC1(C)CCC(=C(C1)c2ccc(Cl)cc2)CN3CCN(CC3)c4ccc(C(=O)NS(=O)(=O)c5ccc( NCC
+CC(C) CN1C(=O)
+CC(C NC(=O)
+CC(=O)O [C@@H]1CC
+CC(=O)N[C@@H](CC CN=C(N)N
+CC(=O)N1 CCCC1
+CC(=O)N1 CCC(CC1)c2
+CC(=O)N c1ccc(NC(=O)
+CC(=O) c1ccc(NC(=O)
+CC 6
+C3CC S(=O)(=O)C3
+C2CCN(CC 2)C
+C2=N CCN2
+C2 CCCN2C(=O)
+C1 CCN(CC1)C(=O)
+C(CC Cc2
+C(CC Cc1
+@] 1
+=CC(=O)NC %2
+=CC 5
+=C (C)C
+= CCO
+5CC c6ccccc6
+4CCN(CC 4)c5ccc(cc5
+4CCC(CC3)(CO 4)NC
+4CCC(C)CC 4
+4C=CC(=O)N C4=O)
+4) N
+4 (=O)
+3) cc(OC)c1
+2CCN(CC 2)S(=O)(=O)C
+2)c3n cccc3
+2)c3 ccc(CN
+1)c2nn n3cnc4[nH]ccc4
+1)C N
+.OC(=O) CCC(=O)O
+)n5 cnnn5
+)ccc3 [nH]2
+)ccc2 F
+)c4ccc( s
+)c(O) nc1C
+)c(Cl )cc3
+)\ CC(=O)N
+)C2=O) C
+)C1=O) c3ccccc3
+)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H]([C@@H](C)CC )C(=O)N[C@@H]([C@@H](C)CC
+)C(=O)NC (C)(C)
+)C(= CC2=C1
+) [C@H]3C
+) [C@H](O)[C@H](O)[C@H]1O
+(OCC CO
+(C)C)NC(=O)[C@H](CC (=O)O)NC(=O)[C@H](CC
+(C)C)C(=O)N [C@@H]([C@@H](C)CC
+(C(=O) OCC(=O)N
+( \C(=C1O)O)
+nn2 C
+ncn n1
+n3 )c2
+n1 2)C(=O)N
+cnc( nc4
+ccc5 OCOc5c4
+cc3 cc(O
+c5ccccc45 )cc1
+c5ccc(F)cc 5)
+c4ccc(O)c5 O[C@@H]1[C@]2
+c4ccc(F)cc4 F)
+c4ccc(F)c(Cl )c4
+c4c(Cl)cccc4 Cl
+c3nn [nH]n3
+c3cn c(C)c
+c3ccc(cc3 n2
+c3cc(OC )c(O)
+c3cc(Cl)ccc3 n4cn
+c2cccc3 c(cccc2
+c2cccc( OC(F)(F)F)c2
+c2cccc [n+]2C
+c2cc(ccc2 Cl)
+c2c[nH]c3ccccc23)NC(=O)[C@H](C c4ccc(O)cc4
+c1cccc( F)
+c1ccc(cc1 )C(F)(F)F
+c1ccc( s
+c(N)n 1
+c(N c3ccc(NS(=O)(=O)C
+\C(=N/ OC(C)(C)
+[nH]c( cc4c3
+[nH] c6
+[C@](O) (C(=O)
+[C@H]6 CCC
+[C@H](O) C4
+[C@@] %11
+[C@@H]3C[C@@H](C\C=C(/C)\ [C@@H](O[C@H]4C
+[C@@H]2CC[C@H](CC 2)c3nnn4cnc5[nH]ccc5
+[C@@H]1C [C@@H]2CCCC[C@@H]2
+[C@@H](O[C@@H]%1 4
+[C@@H](NC(=O) [C@@H](NC(=O)C
+[C@@H](N)C O)
+S(=O)(=O)C )C(=O)N
+Oc1 cc(OC
+O[C@H]1C [C@@H](O[C@@H]1
+O[C@H](CN )[C@@H](O)
+OP(=O)(O) C(N
+O=C1NC(=O)\C(=C\c2cnn3 c(NC4CC4)n
+O=C1 c2ccccc2C(=O)
+O)C(=O)N [C@H](C
+NC(C (=O)N
+NC(=O)c2 ccncc2
+NC(=O)CS c2oc(nn2)c3
+NC(=O)C 4=N
+N(C)C(=O) C(=C
+F)C (C)(C)C
+Cl. CCCCCCCC
+Cc1ccc2 nc(N
+Cc1ccc(C [C@@H]2
+Cc1ccc( CCNC(=O)
+Cc1c( cc(
+C[C@@H](O) [C@H](
+COc1ccccc1 OC
+COc1cc(OC)c2 C(=O)
+CO CCOCCOCC
+CN=C(N)N)NC(=O)[C@H](CC CN=C(N)N)NC(=O)[C@H](CC
+CN1C(=O)N (CC(=O)N
+CN([C@H](C)C O)C(=O)
+CN(C c3ccccc3)CC
+CN C(C
+CC[C@@H]1NC(=O)[C@H]( [C@H](O)[C@H](C)C\C=C\C)N(C)C(=O)
+CCN2 CCCCC2
+CCN1CC(C)(C)Oc2nc(nc( c3ccc(NC(=O)N
+CCN1 C(=S)N
+CCCn1 ccnc1
+CCCCCCCCCCCCCCCC(=O)N [C@@H](C
+CCC1 (O)C(=O)OCC2=C1C=C3N(C
+CC1(C)C 2
+CC(C)[C@H]1 COC(=O)N1c2ccnc(N[C@@H](C
+CC(C) [C@@]1(CC
+CC(C) Nc1n
+CC(C) CN1CC
+CC(=O)N [C@@H](C)C(=O)N
+CC [C@@H](CO)
+CC 2CCCC
+C4= NC(=O)ON
+C3C c4ccccc4C3
+C2= NC(=O)\C(=C\
+C(C Oc1ccccc1
+C(=O)O[C@@H]([C@H](C)[C@@H](O)CC1=O) \C(=C\[C@@H]4CC
+C(=O)N2C c4ccccc4
+=C( SCC(=O)N
+4CCN(CC4)c5 cccc(Cl)c5
+3CCN(CC3)c4 cccc(C)c4
+3CCCC3 )cc2
+2CCC3(CC2)N(CN C3=O)
+2)c4 ccc(F)cc4
+2)c3ccc(Cl)cc 3)c4ccccc4
+1)C2 CCN(CC
+1) (C
+)c5ccc(OC )cc5
+)c5 cccc(Cl)c5
+)c5 6
+)N (C)C)
+)C(=O)N (
+)C(= S
+) [nH]c3
+) OC2
+(F)(F)F )c2
+(CO 1)c3
+(CCN(C 2)C(=O)
+(C) OC
+oc( nc3
+nc(C(=O)N)c3 CC
+cnc8 [nH]ccc8c7)c4
+cn c%11
+cc(C) nc(
+c5[nH] ccc45
+c3nc( ncc3
+c3n nc(N
+c3cccc(Cl)c3 Cl
+c3cccc( OCc4ccccc4
+c3ccc( F
+c3ccc [nH]3
+c2nnc( NC(=O)C
+c2cccc(c2)S(=O)(=O)N C3=N
+c2cccc(c2)S(=O)(=O)N 3CCOCC3
+c2cccc(Oc3ccccc3 )c2
+c2cccc( n2
+c2ccc3 CC
+c2ccc( CCNC[C@H](O)
+c2c(CC (=O)O)
+c1nn n(
+c1ccccc1 )\C
+c1ccc(cc1)c2 o
+c1c( s
+c(CC=C(C)C )c2
+c( cc(cc1
+[n+]2 ccc(
+[S @+]
+[C@]1 4C
+[C@H](O [C@@H]3
+[C@H](C)O4)[C@@H](C)\C=C\C=C\ 6/CO[C@@H]7
+[C@@]3(C [C@H]3C=C
+[C@@H]4C [C@H]4
+[C@@H]2CCCC [C@H]2
+[C@@H](C) C(=O)O
+Oc4 ccccc34
+Oc3cccc( F)c3
+Oc3 cc(Cl)ccc3
+Oc1 c(Cl)cc(Cl)cc1
+ON1 C(=O)N
+OCC )C(=O)
+OC3 (C
+O=C1 CCCN1
+O=C(NCCN1 CCOCC
+O=C(N c1cccnc1
+O=C(N (C
+O N=C1
+NC(=O)c2csc(n2)c3cc(O)c(nc3c4csc(n4)[C@@H]5COC(=O)c6c7CO[C@@H]([C@H](NC(=O)c8csc1n8)c9nc(cs9)C(=O)N5)[C@H](O[C@H]%10C[C@](C)(O)[C@@H]([C@H](C)O%10)N(C)C)C(=O)OCc%11cccc(c7%11)n6 O)c%12
+NC(=O) c5ccccc5
+N3 CCNCC3
+N# Cc1cccc(c1)c2
+FC( F
+F)c( c1)c2
+F )c4ccccc4
+Cc1ccc2 OC(=O)C(=C
+Cc1ccc(cc1 )C(N
+Cc1ccc(CN 2C(=O)
+Cc1cc(cc(C) n1)c2
+Cc1 cccnc1)c2
+C[C@@H]1CN2[C@H]([C@H](C)O1)C3(Cc4cc5c(noc5 c(F)c2
+COc1ccccc1 N(CC(=O)N
+COc1ccc(CN C
+COc1ccc(CN (C)C(=O)
+COc1ccc(C NC(=O)C
+COc1ccc( \C=N\N
+COC(=O)C1=C(C[C@@H]2CC[C@H]1N2 C(=O)NC
+COC(=O) CN
+CO [C@H](C
+CN(C) c1ccccc1
+CN(C c1ccccc1)S(=O)(=O)
+CCOP(=O) (CC
+CCN(C(=O) C1
+CCCC Oc1ccc(cc1)c2
+CCC(CC) (NC(=O)
+CC1(C)CC(=O) C=C(C
+CC1 CO
+CC(C)(O \N=C(/
+CC(C c1ccc(cc1)C#Cc2
+CC(=O)N[C@@H](Cc1cc(F)cc(F)c1 )[C@H](O)CN
+C3CCCCC3 )cc1
+C2CCN(C c3ccccc3)CC2
+C(=O)N)NC(=O)[C@H](CCC(=O)N )NC(=O)[C@H]3CCCN3C(=O)
+C(=O)N(C)C 3=O)
+C(=O)N CC(=O)N
+C )cc1OC
+Br )C2
+=CCCCC 4
+5CCN(CC O)CC
+5CCC [C@@H]5
+4CCC5(CC4) OCCO
+4)N 5CC
+3CCC(CC3)N 4
+2)c3n oc(
+.F[P-](F)(F) (F)(F)F
+)nc(OC )n1
+)n cc3
+)cc2 )C(=O)NO
+)c5cccc( F)c5
+)c3 cc(OC)c(OC)c(OC)c3
+)c(c3 )[N+](=O)[O-]
+)[C@@H](C)O) [C@@H](C)CC
+)N2CC (CC2=O)C(=O)N
+)N 2
+)C(=O)N[C@@H](CO)C(=O)N[C@@H](CC (=O)N
+)C(=O)N[C@@H](CC(C)C )C(=O)N[C@@H](CCCNC(=N)N
+)C(=O)NC c3ccccn3
+(S CC
+(CC=C )C(=O)
+%11cn c%12
+ncnc(N )c24
+nc( nc(
+n2nn c3
+n1 CC
+csc(N \N=C\
+cnc3 [nH]
+ccccc2 4)
+c5 nn
+c4cnc( cc3
+c4c(O) cccc4
+c3ccn (C)
+c3cccc(c3 )n4
+c3c(C) cc(O)
+c3 c(C(=O)N
+c2nc(cs 2)c3ccc(cc3
+c2cn c3ccccc3c2
+c2cccc3ccccc2 3)C
+c2ccc3ccccc3c2 )c4
+c2ccc(NC(=O)N 3Cc4ccccc4C3
+c2ccc(F)cc 2)c3ccccc3
+c2ccc(Cl)c n2
+c1cn (C)c2
+c(c1 )[N+](=O)[O-]
+c(O)cc(O) cc4
+c(C) onc2
+c( nc(nc2
+c( c3ccccc3)c(
+\N=C/1\C (\C(=C1O)O)
+\C(=C\ C(=O)N
+[O-][N+](=O) c1cccc(C
+[C@H]8 O[C@H](C
+[C@H]2CC [C@@H](O2)
+[C@H]2 OC1=O)
+[C@H]2 CNC
+[C@H](NC(=O) OC(C)(C)C
+[C@H](CC(=O)O) CC
+[C@H](C) [C@H](CC
+[C@@H]7CC [C@@]6(C)
+[C@@H]2CCCN2 S(=O)(=O)
+[C@@H]1 CS
+[C@@H](CC CN=C(N)N)NC(=O)[C@H](C
+[C@@H](C) NC(=O)
+S(=O)(=O)C (C)(C)C
+S 3
+S 2)cc1
+O[C@@H]6 O
+O[C@@H]4 O[C@H](CO)[C@@H](O)[C@H](O)[C@H]4
+OC3 (CCN(CC3)C(=O)
+OC1(CCN(CC 1)C(=O)
+O)C(=O) OC
+Nc1ncnc2c1 nc(S
+NC(=O)c2s c3ccccc3c2
+NC(=O)c1 cccnc1
+NC(=O)C 5
+N=C(C =CC3=O)
+N(C (C)C)C(C)C)N
+N c1cccc(c1
+N c1cc(N
+Fc1ccc( c(F)c1)c2
+Fc1ccc( OCC(=O)N
+F) S(=O)(=O)C
+Cc1ccccc1 C(=O)
+Cc1ccc( \C(=N\O)\N
+COc1ccc(cc1)N 2C
+COc1cc2cc( [nH]c2c(OC)c1
+COc1cc2 c3
+COc1cc(NC (C)CCC
+COc1cc( NC(=O)C
+COc1 c(C)c
+CN(C) c1ccc(N
+CCOCC1 CN(C
+CCN(CC1)C (c2ccccc2)c3ccccc3
+CCN( [C@@H](C)C
+CCCCCCCC 1
+CCCCC (C)
+CCC1 CCCCN1C(=O)
+CC(C)(C)CC 2=O)
+CC(=O)N CCN
+CC =C3
+C3 (C
+C1 )C(=O)O
+C) O
+C(=O)c1 c23
+C )n2
+=N CCCN
+=CC=C N4
+5 (C)C)
+4 )C(F)(F)F
+3CCNCC 3)n
+3CCN(CC3)c4 ccncc4
+3CCC(CC 3)NC(=O)
+3)n n1
+2CCN =C2
+2 CO
+1)C(=O) OC
+)ncc1 C(F)(F)F
+)nc(N)n 1
+)n3 nccc3
+)cc( Br)c3
+)cc 1)c2
+)c6 ccccc26
+)c3ccc(OC(F)(F)F)cc 3
+)c2 O1
+)c(O) cc1
+)c( cnc1
+)c %15
+)S(=O)(=O)N 4CCCCC4
+)CC 2)C(=O)
+)C(=O)N[C@@H](CCC(=O)N )C(=O)N
+)C(=O)NC c3occc3
+)C(=O)C (C)(C)
+) [C@@H]2CC
+(N (C)C
+(CC CCC1
+(C) CCC1
+n2nn c3ccccc23
+n2)c3 cccnc3
+n1 CC(=O)O
+ccccc6 7
+cccc( F)c2
+ccc3[nH] c2n1
+ccc(F)cc 3
+ccc(C) nc2
+ccc n12
+c7ccc(O)c(c7)c8 c(O)cc(O)
+c6 cccs
+c5n c6
+c4ccccc4 [N+](=O)[O-]
+c4ccc(cc4 )C
+c4ccc(cc4 )
+c4ccc(OC c5ccccc5)cc4
+c4cc( ccn4
+c4c[nH] c5
+c3ccccc3 )cc1
+c3c2 )c4ccccc4
+c3c(C)nn(C)c3 C
+c3c(C)cc(C) cc3
+c2ccccc2 n1
+c2ccc(Br )s2
+c2c(OC)c1OC) SC
+c2 cc(Cl)c(Cl)cc2
+c2 C(=O)N
+c1ccc2ccccc2 c1)C(=O)N
+c1ccc( c(F)c1)c2
+c1ccc( SC
+c1 coc(n1)c2
+c(cn n1C
+c(ccnc3 [nH]2)c4
+c(F)cccc3 F)c1
+c(C) ncc1
+\C=C\ OC2(C)
+[C@]3 (C)c2cc1
+[C@]2(C)[C@H]3CC [C@@]4(C)
+[C@H]5 OC
+[C@H]1 CC(=O)
+[C@H]1 2)C
+[C@H](NC(=O) OC
+[C@H](C) [C@@H]1
+[C@H]( [C@H](C)CC
+[C@@H]5 [C@H]6
+[C@@H]3 SC
+[C@@H]2C [C@H](C
+[C@@H](C)C(=O)N[C@@H](CC (=O)O)
+SC(=O)N C4=O)cc3
+S1(=O)=O) C(=O)N
+S(=O)(=O) c2ccc(Cl)cc2
+S /C/2=N\
+Oc3ccccc3 C2=O
+O[C@@H]1 O[C@H](CO)[C@@H](O)[C@H](O)
+OC) cc(OC
+OC(=O)c1 ccc(Cl)cc1
+OC(=O) CCN
+O=C(N CCCCN1
+O) c8
+O) C(=N)N
+O n1
+NC(=O)c2 cs
+NC(=O)\C(=C/ N
+NC(=O)CS c2ccc(nn2)c3
+NC(=O) c3ccncc3
+N=C(N CC
+N2CC (CC2=O)C(=O)N
+N2C(=O) c3ccccc3C2=O
+N(CC C(=O)N
+Fc1 c(F)c(
+F [C@H]1
+Cn1 nc(C(=O)N
+Cc1n onc1
+Cc1ccccc1 )C
+Cc1ccc(cc1)n2nc(cc2 NC(=O)N
+Cc1ccc( nn1
+Cc1ccc( NC(=O)CC
+Cc1cc(O) ccc1
+Cc1 ccc(C)cc1
+COc1cccc(C 2
+COc1ccc2 C(=O)\C(=C\
+COc1ccc(cc1)C2CC (=NN2
+COc1cc(\C=C\C(=O) \C=C\
+COc1 cc(Cl)c(C)cc1
+COc1 c2C
+COC1 \C=C\OC2(C)
+CN1C(=O)C (NC(=O)N
+CC[C@H](C)[C@H](NC(=O)[C@H](CC (=O)O)NC(=O)[C@H](CC
+CCOCC OC(=O)
+CCOC(=O)C1=C N(CC
+CCCCN(CC )c1
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+3)\N 1
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+=C4C[C@@H](O)CC [C@]4(C)[C@H]3CC[C@]12C
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+=C(N)N (C)C(=O)N
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+) [C@@H]1O
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+=CC(=O)C(= CC=C
+=C 4CC
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+)[N+](=O)[O-] )C(=O)N
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+[C@H]3 CNC
+[C@H]1 [C@@H](C
+[C@H](O [Si](C)(C)C(C)(C)C
+[C@H](O 2)
+[C@H](N(C 1)C(=O)
+[C@@](O) (C(=O)O)
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+[C@@H](N C(=N)N
+[C@@H](CCC(=O)N )NC(=O)[C@H]3CCCN3C(=O)
+[C@@H](C) [C@H]2
+SSC[C@H](NC(=O)[C@@H](NC(=O)CNC(=O) [C@@H]2CSSC[C@@H]3
+S CCCS
+Oc1ccc(cc1)C2 =C
+O[C@@H]3 CO
+OC3 CN(C
+OC1 =C(S
+O)c2 3
+O c2ccc(cc2)S(=O)(=O)N
+NS(=O)(=O) c1ccc(cc1)C(=O)N
+Fc1ccc( CC(=O)N
+Cn1 cc(CC
+Cc1cccc (CC(=O)N
+Cc1ccc(C )c(CN
+Cc1ccc( NC(=N
+Cc1 c(F)cccc1
+C[C@]1(C S(=O)(=O)
+C[C@H]1 O[C@@H](O
+CS(=O)(=O)N1CC c2c(C1)c(nn2
+COc5ccccc5 O4
+COc1ccc(NC(=O) CN(C)C(=O)
+COc1cc(OC )c(Cl
+CO [C@H](
+CN1N=C(C =CC1=O)
+CN(CC c2c1
+CCc1c2 CN3C(=O)C4=C(C=C3c2n
+CCNCC (O)CO
+CCN1C=C(C(=O)O)C(=O) c2cc(F)c(N
+CCN) NC(=O)[C@H](C)NC(=O)[C@H](CC
+CCN(C 1CCN(CC
+CCCc1nc2 c(C)cc(cc2
+CCC(CC )(C
+CC1(C) N
+CC c1ccc(cc1)n2
+C=C c([nH]
+C3C4CC(C =C4)C3
+C2CC 3CCCC(C
+C2=O) c3cccc(Cl)c3)c4ccc(Cl)cc4
+C(C)(C)C )C(C)(C)C
+C( c1ccccc1)
+C )cn4C)cn3C
+C )c4ccccc4
+Br)cc 6
+=CC(=[N+] (CC)CC
+=CC =N
+9 CCCCC
+4CC c3c2
+4CC 4)N
+4)c5 ccncc5
+3CCCC N3
+3)c4 ccc(CN
+3)N4CCC [C@@H]4
+2CC3CC (C1)C
+2CC =C
+2)N (C(=O)
+2) OO
+2) /
+2 CCCN2C(=O)
+1C (C)(C)
+1)c2ccccc2 )N
+)c3ccc(Cl)cc 3Cl
+)c3ccc( O)c(O)c3
+)c3 C2=O
+)c(N )c1
+)c(C#N )c3nc4ccccc4
+)c( F)cc4
+)S(=O)(=O)N 3CCCC3
+)C(=O)N[C@@H](C c6c[nH]c7ccccc67
+)C(=O) c4ccccc14
+(C)C)C (=C(C(=O)N
+s 2)C(=O)N
+ncc( Br
+nc(N )c1
+cs cn
+cnc( Oc3ccc4
+cc(C(=O)N CC3=C(C)C=C(C)NC3=O)
+c7ccccc7)c8 ccccc8
+c5ccc6OCC(=O)N c6n5
+c4ccn cn4
+c4cccnc2 4
+c4ccccc4 [N+](=O)[O-]
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+c4ccc(Br )cc34
+c4cc(o n4
+c3nc(cs 3)c4ccccc4
+c3n nc(SC
+c3ccccc3 F)C
+c3ccccc3 )n1
+c3CCCC c3
+c2nnc(s 2)S(=O)(=O)N
+c2ccccc2 )C(=O)N
+c2cc(OC )c(O)
+c2c[nH]cn2)NC(=O)[C@H](C c3ccccc3)NC(=O)
+c2c(O) ccc(O)c2
+c1ccc2 c(C
+c1ccc(cc1 )[C@@H]2
+c1 ccs
+c(O) c(N
+c(Br )c1
+c( ccc5
+\N 1
+[nH] c(C(=O)N
+[N+] 3(CC
+[Li+]. [Li+].
+[C@]4(C)[C@H]3C [C@H](O)
+[C@H]6[C@H](C)CC[C@@H]([C@H]3C) [C@@]46OO
+[C@H](CO) NC(=O)
+[C@@]45[C@@H](Oc1c2 4)
+[C@@]34 [C@@H]5C
+[C@@H]3O[C@H](CO)[C@@H](O) [C@H]3
+[C@@H]3C[C@@H](O) CC(=O)O
+[C@@H]2 [C@@H]3C
+[C@@H]2 C1=O
+[C@@H]1C OC
+[C@@H](O [C@@H]%11
+[C@@H](C c6ccccc6
+[C@@H](C c1c[nH]c2ccccc12)NC(=O)
+[C@@H](C 2)NC(=O)
+[C@@H](C #N
+[As] (=O)
+SCC(=O)N 3
+SCC #N
+SC NC(=O)C
+S(=O)(=O)N 3CCOCC3
+S C2=C(O)
+Oc3 c(NC(=O)
+O[C@@H](CO) [C@H](O)[C@@H](O)
+OP(=O)(O) OP(=O)(O)O)
+OC[C@H]1O[C@H]([C@H](O)[C@@H](O)[C@@H]1O)c2 ccc(Cl)c(C
+OC3 =C2C(=O)
+OC1 CCCCC1
+OC(=O)\C(=C\ C(=O)
+O)[C@@H](O) [C@H]3O
+Nc1nc(O) c2ncn(
+NCCCC [C@H](N)C(=O)N
+NC(=O)[C@H](C)NC(=O) [C@@H](NC(=O)[C@H](CC
+NC(=O)N 3
+NC(=O)CNC(=O) [C@@H](NC(=O)
+NC(=O)CN (C)C
+N=C 5
+N1 C
+F) n2
+Cc2 c(O)cc(O)
+Cc1onc( c1C(=O)N
+Cc1ccccc1 OC
+Cc1cc( OCC
+C[C@@]1(CO) [C@H](O)CC
+C[C@@]1 (O)C
+COc1ccc2 c(OC
+COc1cc(\C=C\ C(=O)OC
+CO \N=C(/C)\
+CN1 N=C(
+CCOc1ccc(cc1 )S(=O)(=O)
+CCOc1ccc( NS(=O)(=O)
+CCN3 C
+CCN 4CCCC4
+CCCN 4CCOCC4
+CCCC COC(=O)
+CCC )
+CC3 CCN(C
+CC(=O)N c1ccc(C
+CC(=O)N C(CC
+CC# CCOc1ccc(cc1)S(=O)(=O)N
+C2=C N(C
+C(C (C)C)C(=O)N
+C(=O)N[C@@H](CCCCN )C(=O)N
+C(=O)N(C =N
+C(=O)C )C
+C(=CC(=O) Oc1
+Br c1cc2
+5)c6 ccc(Cl)cc6
+5)c6 ccc(Cl)cc6
+4 CO
+4 )c3c2
+2CCC2 (C)C(=O)N
+2CCC(CC2) Oc3ccc(cc3
+2)NC(=O)[C@H](CC CNC(=N)N)NC(=O)[C@H](CO)
+.OC(=O)C(F)(F)F .OC(=O)C(F)(F)F
+)n3 )cc2
+)cc(c2 )C(F)(F)F
+)cc (CC
+)cc (C(=O)N
+)c5 cccnc5
+)c4ccccc4 O
+)c4ccc(cc4 )C#N
+)c4ccc( Br)cc4
+)c3nccs 3
+)c3 n[nH]
+)c2 ccc(C)cc2
+)c( c5ccccc5
+)Oc3cc(ccc3 S1(=O)=O)
+)NC(=O)[C@@H](NC(=O) [C@@H](CO)NC(=O)CNC(=O)[C@H](CC(=O)O)NC(=O)[C@@H](C)
+)N6CCOCC 6
+)CC2 )cc1Cl
+)C3=C c4ccccc4OC3=O
+)C3 =CC(=O)C(=
+)C(F)(F)F) n1
+)C(=O)N[C@H]1 CCNC(=O)[C@@H](NC(=O)[C@H](CC
+)C(=O)NC 5CCCCC5
+(O)C(O) C3
+(O)C =O
+(N C3=O)
+(N 2C(=O)
+(CN (C(=O)
+(C NC(=O)N
+(=O)N)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC CCN)NC(=O)[C@H](CO)NC(=O)[C@H](CCC(=O)N)NC(=O)[C@H](C
+(=O)N)NC(=O)[C@H](CCC(=O)N )NC1=O)C(=O)N
+%2 2C
+o cnc2
+o c3ccccc3
+nc(o c2C
+n7cnc8 c(N)ncn
+n4 n3
+n4 cc(nc4
+cccc n1
+ccc3cccc4ccc1 c2c34
+ccc(Cl)cc1 3
+cc2 OC
+cc(Cl)ccc2 3
+cc( OC(=O)
+c5 cc(C)ccc5
+c4n nc(s
+c4cccc(c4 )C(=O)O
+c4ccc5 c(c4
+c4ccc(C )c(C)c4
+c4 c3n
+c3n cs
+c3ccccc3 )C1=O
+c3ccc(N c4
+c3cc[n+] (Cc4ccccc4
+c3c[nH]cn 3)C(=O)N
+c2occc2 )cc1
+c2ccccc2)c3 ccc(Cl)cc3
+c2ccc3OCOc3c2 )c1
+c2ccc3 nc(N)nc(N
+c2ccc3 CCCN
+c2ccc(cc1 2)[N+](=O)[O-]
+c2ccc(Cl)cc 2)N
+c2cc( F)c(F)c(
+c2cc( F)
+c2 4
+c1ccccc1)NC(=O)[C@H](CC (C)C)NC(=O)
+c1ccc(NC(=O) c2ccccc2
+c(nc( nc1
+c(n[nH] c3c2
+\C(=N\ OCC
+[nH]c( cc3c2
+[O-][S+] 1
+[C@]13 C)OC
+[C@]12C) OC(=O)C
+[C@]1 (O
+[C@H]4CC [C@]23C
+[C@H]3CC CO
+[C@H](CC CN
+[C@H](C) OC2=O
+[C@H](C) N(C
+[C@@](C)(CCC [C@]23C
+[C@@H](S )C
+[C@@H](OC(=O)C) [C@H](C)C
+[C@@H](O) C=C
+Oc3c(C)c(O)c4c(O)c(NC(=O) \C(=C/C=C
+Oc1ccc2 C(=O)
+Oc1cc(O) c(cc1
+O[Si](C)(C)C(C)(C)C)[C@@]3(OS(=O)(=O) C=C3
+OP(=O)([O-]) OCC[N+](C)(C)C
+OC(C NC(=O)
+OC c1ccccc1)
+O=C(N [C@@H]1
+O=C(C NC(=O)c1
+N[C@@H](C (=O)N
+NCCCC N(C
+NC(=O)C3=C 2CCCC
+N=C1 SCC(=O)N
+Fc1cccc(c1 )C2
+Fc1cccc( NC(=O)N
+F)cc1 3
+F )C1=O
+Cl )cc4
+Cc3ccccc3 Cl
+Cc2 coc(n2)c3
+Cc1ccc(cc1 Cl
+Cc1cc(C) cc(NC(=O)
+Cc1cc( NC
+Cc1 cccn2
+C[C@@]1 2C
+COc1cc2nc(nc(N)c2cc1OC)N 3CCN(CC3)C(=O)
+COc1cc2 nc(N
+CN=C(N)N )C(=O)N[C@@H](CCC(=O)N
+CN2CC (C
+CN )C(=O)N[C@@H](C
+CCN(CC =C
+CCN(C c7ccccc7
+CCCC[C@H](NC(=O)C )C(=O)N[C@H]1
+CCCCOC(=O)NS(=O)(=O) c1sc(CC(C)C)cc1
+CC2 CCC(CC
+CC1=CN([C@H]2C [C@H](OP(=O)(O)OC[C@H]3O[C@H](C
+CC1(C) CN
+CC1 CC(C)CN(CC
+CC(C)[C@H](NC(=O) OCc1ccccc1)C(=O)N
+CC(C)[C@@]1(CC [C@H](C1)N
+CC(C)O c1ccc(cc1)C(=O)N
+CC(C)(C)C 3
+CC(C)(C)C 1CC
+CC(= S)N
+C4 CCC(CC
+C3C 4CC
+C2 CCC(=O)N
+C(=O)N[C@@H](CCCCN )C(=O)O
+C(=O)C(C)(C) [C@@H]4CC
+C(=C(N )OC
+=CC(=O)N C1=O)
+4CCN(CC4)C(=O) c5occc5
+4CC 4)c5ccccc5
+4C [C@H]5
+3CCN(CC3)c4 ccncc4
+3CCC 3
+3)c4 ccc(Cl)c(Cl)c4
+2CCC3(CC2)C N(C
+2)c3ccc4 ccccc4c3
+2)c3 cccc(Cl)c3
+2)C(=O)N c3ccccc3
+2) cc(c1
+1CCOCC 1)C
+1)c2cc3 c(n[nH]c3cn
+1)C (C)C
+)n5 ccnc5
+)cc3 C
+)c3 ncn
+)c3 c[nH]
+)c3 C(=O)
+)c2cc([nH]c2C1=O) c3ccc(OCC(=O)N
+)c2 ncn
+)c(c2 )C#N
+)c(C)c1 O
+)O [C@@H]5
+)NC(=O)[C@H] (NC(=O)
+)N (C)CC
+)C3 CCCC3
+)C(C) (C
+)C(=O)N[C@@H](CC(C)C )C(=O)N[C@@H](CCC(=O)N
+)C(=O)N[C@@H](C c4ccccc4)C(=O)N
+)C(=O) c2ccccc2
+)C 3C
+(Cl )C3=O)
+(=O)O) NC(=O)CN
+%24 C=C(C)C(=O)NC%2
+s c3n2
+oc( SCC(=O)
+nn(C )c4
+nn cn4
+nc1 O)
+nc(nn1 2)c3occc3
+n2ccnc2 )cc1
+n1)C2 CCN(CC
+ccncc3 )cc1
+ccc(Cl)c(Cl )c1
+cc3 nc(
+cc( cn2
+cc %11
+c5ccc(cc5 )S(=O)(=O)N
+c5ccc(cc5 )C(C)(C)C
+c4s ccc2
+c4ccc(OC c5ccccc5
+c4cc(O)c(O)c(O) c4
+c3n c4c(
+c3csc( n3)c4
+c3ccccc3C (O)(
+c3ccc4[nH]ccc4 c3
+c3 cc(Cl)c(
+c2o ncc2
+c2ccccc2S c3ccccc13
+c2cccc 3CCCC
+c2cc1 C(=O)N
+c2c(C) cc(cc2
+c23)C (C)(C)C
+c2 4)cc1
+c1s c2ccccc2c1
+c1ccc(cc1)c2ccccc2 c3nn[nH]n
+c1ccc(Cl)cc1 )\
+c(ncn c2cc1
+c(N )n2
+\C=C\C(=O)O)cc 6
+[nH] 3)N
+[nH] 2)C(=O)N
+[C@]3(C) [C@H]4CC
+[C@]3 (CO
+[C@H]6 C(=O)N
+[C@H](O) [C@H](C)O
+[C@H]%11 O)
+[C@@](C)(OC )[C@@H](O)
+[C@@H]4O[C@H](CO)[C@@H](O) [C@H]4O)
+[C@@H]2 [C@]1(C)CC
+[C@@H]2 [C@H](O)C
+[C@@H]([C@H]1 C)
+[C@@H](O[C@@H]%1 3
+[C@@H](C)O [C@@H](C)C
+[C@@H]( c2ccc(Cl)c(Cl)c2
+[Br-]. Cc1
+SC [C@H](NC(=O)C
+Oc3ccccc3 C2=O)
+Oc1cccc( N
+Oc1ccc( CC
+OC[C@@H]1 [C@@H](O)[C@H](O)[C@@H](O)
+OCC OC(=O)
+OC1 CCCC
+OC1 (CCN(C
+OC(=O)\C=C/ C(=O)O.
+O=S(=O)(N 1
+O=C(N 1CCC(CC1)N
+Nc2nccc(n2)c3 c(nc4
+N[C@@H](CC C(=O)N
+NC1=C(C#N )C(
+NC(=O)c2 cnc3
+NC(=O)N 1CCC
+NC(=O)CC (NC(=O)
+NC(=O)C2(CCC2 )NC(=O)
+H 2
+FC(F)(F) c1ccc(cc1)S(=O)(=O)N
+F) CC1
+Cn1c( ccc1
+Clc1ccccc1 C2
+Cl. CN(C)CCCN(C(=O)
+Cl. CC(C)(C)
+Cl )C#N
+Cc2c(CN 1C(=O)
+C[C@]12CC[C@H]3[C@@H](CC C4=CC(=O)CC
+C[C@H]1 [C@@H]2CC
+C[C@H]1 N
+CSc1 sc(cc1
+CO CCN(C)
+CNC(=N)N)NC(=O)[C@H](C c6ccc(O)cc6
+CN=C( c3ccccc3Cl
+CN(CC c3c2
+CCn1 cnnc1
+CCc1 c(CC
+CCCCC\C=C/C\C=C/C\C=C/C\C=C/ CCCC(=O)N
+CC1(C) OC2
+CC1 N
+CC(C) CCn1c(CN
+C(=O)NC (N
+C(=N)C=C N2
+C(=N N(C
+C( NC(=O)C
+Br c1cccc(c1)c2
+Br )C(C)C
+7 )O
+5CCCC5 )cc4
+4CC5CC 3C
+3CCN(CC3)c4 ccc(F)cc4
+3CCC(CC 3)C(=O)N
+3)n4 cnc(
+3)n cc1
+3)c4ccccc4 F
+3)C(=O) O)C(=O)O
+2CCN(Cc3ccc4OCOc4c3 )CC2
+2)c4 ccncc4
+2)c3ccccc3 )c4ccccc4
+2) ccc1O
+2 (=O)=O)
+1)C#N )n2cc(C(=O)N)c(N
+1)C (C)(C)C
+)n4 cncn4
+)cc(c1 O)C(C)(C)C
+)c6 cc7OCOc7cc26
+)c5 ccc(C
+)c4ccc( Oc5ccccc5)cc4
+)c(O) c2
+)C1 =CC(=O)
+)C(=O)N 7CCC[C@H]7
+(O)C [C@H](OC(=O)
+(N=C(N )O
+(CC=C )
+%10 cnc%11
+n4 cccn
+n4 c5ccccc5
+n2)n3 ccnc3
+n2)c3ccc( N
+ccn2 5
+cc(cn c45
+cc(F)ccc4 F
+c6ccc( \C=C\C(=O)O)cc6
+c6 ccn
+c5ccccc4 5)c6ccccc6
+c5cccc(c5 )C(F)(F)F)
+c5ccc(cc5 )c6ccccc6
+c4n 3C
+c4ccc(OC(F)(F)F)cc 4
+c4cc5OCOc5 cc34
+c4c3 C2=O
+c4c(C)cc(C) cc4C
+c3cccnc3 )c1
+c3ccccc3 CO
+c3ccccc3 )ccc12
+c3cc(C(=O)N c4cc(C(=O)N
+c3cc( NC(=O)c4
+c2cn( nn2)c3
+c2cccs 2)c1
+c2ccccc2)C N
+c2c1 )n3
+c23 )c5ccccc5
+c1nn n2
+c1ccc2c(c1)c(C)c(C)n2C c3ccc(cc3)c4ccccc4
+c1ccc(Cl)cc1) (
+c1ccc( nn1)c2
+c(c1 CN
+c(C O
+c%11 ccccc%11
+c%10 ccc(O)cc%10
+\N=C\C 2=C
+\C(=C/ C(=O)
+[nH] ccc3
+[O-] [N+]
+[C@]3(C) O
+[C@H]4CC CO
+[C@H]4 [C@@H](C
+[C@H]1O [C@@H]2
+[C@H](C)CC )C(C)C)C(C)C
+[C@H](C 2)N
+[C@@]4(C [C@H]4C=C
+[C@@]3(C)[C@@H](CC[C@]4(C)[C@@H]3CC =C5
+[C@@]2(C)C3 =C(C(=O)
+[C@@H]3CC [C@H]2C3
+[C@@H]2 CCCCN
+[C@@H](Cc4ccccc4 )N(C
+[ M
+Oc3cn cnc3
+Oc2ccc( cn2)
+OP(=O)(O)OC [C@H]6O[C@H](C[C@@H]6
+OCC (C)C
+Nc1n cc(cn1)c2
+NS(=O)(=O)C )c3
+NC(=O)c2csc( n2)c3ccc(nc3
+NC(=O)CN 2CC
+NC(=O)C (C)N
+N4CCN(C)CC 4
+N(CC )C
+N(C1=O) C(=C(C
+Cc3ccccc3 Cl
+Cc1csc( n1)c2
+Cc1cccc(c1 )N2C(=O)
+Cc1cccc(C)c1 OCC(=O)N
+C\C(=N/ NC(=N)N)\
+C[C@H](N c1cc(nc(C)n1)c2ccc3
+COc1ccccc1 Oc2c(NS(=O)(=O)
+COc1ccc2c(c1 )C(=O)
+COc1ccc2 C3
+COc1ccc(cc1 CN
+COc1ccc(C[C@H]2 NC(=O)\C=C\C
+COc1cc(cc(Cl)c1 O)c2ccc3
+COc1cc( \C=C/
+COP(=O)(O) O
+COC(=O)[C@]12 CCCCC
+COC(=O) [C@@]12C
+COC(=O) [C@@]12
+CN1 N(C
+CN(C)CCCN c1
+CN )NC(=O)[C@@H](NC(=O)
+CCc1ccc( OCC(=O)N
+CCc1c(C) nc(O)
+CC[C@H](C)[C@@H]1NC(=O)[C@H](C SSC[C@H](NC(=O)[C@@H](NC(=O)CNC(=O)[C@@H]2CSSC[C@@H]3
+CC[C@@]1 2C
+CCOC(=O)N1 CCN(CC1)S(=O)(=O)
+CCN1 c2ncccc2
+CCN 3CCOCC3
+CCCCc1n c(Cl)c
+CCCC C(=O)O)
+CC1= CC(C)(C)
+CC1(C)CC(=O)C2=C(C 1)
+CC1 CCC2
+CC(C)(C)c1cc( NC(=O)N
+CC(=O)N[C@@H](CC (=O)N
+CC(=O)NN=C1 c2ccc3nc(oc3c2
+CC c1ccc(OC
+CC COc1ccc(cc1)c2
+C4CCCC 4)n
+C3 CS(=O)(=O)CC3
+C1 CN(CC
+C(CN1 CCCC1)
+C(=CN(C 5CC5
+C(=C (Br
+C c2ccccc2)
+C \C=C\C
+C 5CC5
+C (O)(
+=S 4(=O)
+6) (O
+4)C(=O) c5ccccc5
+4) ccc12
+3CC(CCl )c4
+3CC S
+3)c4 ncn
+3)S(=O)(=O) c4ccc(Cl)cc4
+3) O
+3 =
+2CCC3(CC2) CCN(C
+2)c3ccc( OCC
+2)c3 cc(C)
+1CCCC(C 1)C(=O)N
+1CCC (C)CC1
+1CC1 )N2
+1)c2ccc3 ccccc3c2
+)n c5ccccc15
+)n c3c2
+)cn 7C
+)cc( OCC
+)c4 cco
+)c3 [nH]
+)c2ccc( cc1
+)c1 ccc(Cl)c(C(=O)
+)\N =N/
+)OCCCC [C@@H](C)
+)CCC #N
+)C1 O
+)C [C@@H](C)C
+) cc(OC)c1O
+(N )C(=O)O)
+(CN C4=O)
+(C1 )S(=O)(=O)
+(C1 )C3
+(C) nc4
+(C(=O)N CCS(=O)(=O)N
+sc( nc4
+oc(C) cc2
+noc( n2
+n5c(cc6c(F)cccc5 6)c4n3
+n5 cncn
+n4 cccc4
+n3 cc(CN
+n2c( nnc2
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+cccc( F)c4
+ccc(Cl )c(c3
+cc[n+] (CCC
+c7 cc6
+c5cc(o n5
+c4nccn 4C
+c4cn [nH]c4
+c4ccccc4 F)c5
+c3ccccc3 )s
+c3ccc4ccccc4 n3
+c3ccc4 nc(
+c3ccc(cn 3)C#N
+c3ccc(cc3)S(=O)(=O)N 4CCOCC4
+c3ccc(N )nc3
+c2nc3 N(CC
+c2ccccc2N=C1 SCC(=O)N
+c2ccc(C#N )c(c2)C(F)(F)F
+c2 c(Cl)cc(cc2Cl
+c2 O
+c1nc2ccccc2 cc1
+c1cccc n1)C
+c1 coc(
+c1 CO
+c(Cl)cc cc3Cl
+c( nc5
+\C=C\C(=O)O) cc1
+\C(=C\CC \C(=C\
+[n+] 7
+[C@H]6 7
+[C@H](O)[C@@H](N)C N1
+[C@H](O)[C@@H](N)CN1 CC(=O)N(CC1
+[C@H](N (C)C)C(=C(C(=O)N
+[C@H](CN 3C
+[C@H](CC (C)C)NC(=O)[C@H](C
+[C@@]6(CC [C@@]45C
+[C@@H]4[C@@]5(C)CC[C@H](OC(=O) CC(C)(C)C(=O)O)C(C)(C)
+[C@@H]4 [C@@H]2CC
+[C@@H]2 NC(=O)
+[C@@H](NC(=O) [C@@H]1CCCN1C(=O)
+[C@@H](CC (C)C)C
+S(=O)(=O) c2ccc(C)cc2
+Oc7 cc(O)ccc5
+Oc4 cccc(Cl)c4
+Oc2ccc(cc2 )S(=O)(=O)
+Oc1cccc(c1 )C(=O)
+OCC (O)C(O)C
+OCC (N
+O=C(C 1CCC
+O)C(=O)O) C(C)(C)
+O \N=C(\
+Nc2n cnc(N
+Nc1ncc(cc1OC(F)F)c2 cc(nc(n2)N
+NC(=O)c2cc( NC(=O)
+NC(=O)c2 ccc(Cl)cc2
+NC(=O)[C@H](N )CC
+N 3CCCC3
+Fc1ccc(cc1)c2 nc3
+Cl. OC(C
+Cc1onc( c1)c2
+Cc1n sc(
+Cc1cccc2nc([nH] c12)c3cccc(c3
+Cc1cccc( n1)C(=O)N
+C[C@](C c1c[nH]c2ccccc12)(NC(=O)
+C[C@@]1 (O)CC
+CS(=O)(=O)N 1C
+COc1nc( ccc1
+COc1ccc2[nH] cc(C
+COc1ccc2 ncc(
+COc1ccc2 CC3
+COc1cc(ccc1 OCC(=O)N
+COC(=O) [C@H]1C
+COC [C@@H](NC(=O)N
+CO CCN(C)C
+CN(C)C \C=C\C(=O)N
+CN(C) c1cccc(
+CN(C [C@@H](CCN1
+CN CCC
+CN 3CCOCC3
+CCOc1 ccc(Cl)cc1
+CCOC(=O)C1 (CC
+CCOC(=O) c1ccc(cc1)N
+CCN c1ncc2
+CCCN1 C(=O)N(C
+CCC [C@](C)(
+CC(C) Oc1cc2
+CC(=O)c1ccc(cc1)N 2CCN(CC2)C(=O)
+CC(=O) c1cn
+CC(=C)[C@@H]1CC[C@@]2(CC[C@]3(C)[C@H](CC [C@@H]4[C@@]5(C)CC[C@H](O)C(C)(C)
+C1=O) \
+C(N C1
+C(F)(F)F) S(=O)(=O)
+C(C)C )c4
+C(=O)O)NC(=O)[C@H](CC (=O)O)NC(=O)[C@H](CC
+C(=O)O) NC(=O)[C@H](C)NC(=O)[C@H](CC
+C c2ccc(cc2)C(=O)N
+Br)cc 5
+=CC2 =CC(=O)
+=CC2 =C(N
+=C(C#N )C(C
+=C N
+9 =O)N
+5) [C@@H]4
+4)C (=N)N
+3CC C(=O)N
+3)n 5
+3)c4cccc( Br)c4
+3)C(=O)N 1
+3 N=C(S
+2CCOCC2 )C1=O)
+2=C(CC CC2=O)
+2)c3n ccs
+2)c3ccccc3 C1=O
+2)C3 CCN(C
+1CC(C 1)c2
+1)c2 ccc(F)cc2
+)c5ccc6 ccccc6c5
+)c4ccccc4 C
+)c2cc( ncn
+)c2 cccnc2
+)c(F)cc 3C(=O)
+)c( [nH]
+)N 4CCCC
+)C1 =N
+)C(C)C) ccc1OC
+)C(=O)NCC O
+)C(=O)NCC =C
+)C(=O)C 5CC5
+)C(=O) c23
+)C(=O) Oc12
+(C)c2 ccccc2
+ncn n5
+n3 c(nnc1
+n2cncn 2
+cnnn 4
+cn ccc3
+cccn c7
+cccc(OC c3c(Cl)ccc(c3Cl)S(=O)(=O)N
+cc(C(=O)NCC3=C(C)C=C(C)NC3=O) c2C
+c7 ccc(F)cc7
+c6 ccncc6
+c5 ccnc(
+c5 ccc(Cl)c(Cl)c5
+c5 cc(O)c(O)c(O)
+c4ccccc4C3=O) CC2
+c4ccccc4) NC(=O)[C@H](C)NC(=O)[C@@H](NC(=O)[C@H](C
+c4ccc(Cl)cc 4)C
+c4c3 )C
+c3nc5 CCCCC
+c3n s
+c3ccccc3)n c12
+c3ccccc3 N
+c3cccc(C)c3 C
+c3ccc4 CCCc4c3
+c3ccc(cc3 )C(=O)NCC
+c3 nn(
+c3 cc[n+](C
+c2occc2 )\
+c2ncnc3 c2cnn3
+c2ccccc2)c3 ccccc1
+c2ccccc2 C#N
+c2ccccc2 )C(=O)N[C@@H](Cc3ccccc3
+c2ccc3 C(=CC(=O)Oc3c2
+c2ccc( CC(C)NC(=O)C
+c2 c4ccccc14
+c1cc( F)c(
+c1c[nH]c2ccccc1 2)C(=O)
+c(N)n c1
+\C=C\ C(=O)O
+[nH] 4)cc1
+[n+]1 ccc(cc1)c2
+[Ca +2].
+[C@]2(C) [C@@H]1CC
+[C@@]4 (O)CC
+[C@@]2 (CO)
+[C@@](C) (N)C
+[C@@H]4[C@@]5(C)CC[C@H](O) [C@@](C)(CO)
+[C@@H]3C 4
+[C@@H]3C #N
+[C@@H]2C [C@H]3
+[C@@H]([C@H]1C) [C@@]24OO
+[C@@H]( [C@H]4
+[Br-]. CCCCCCCCCCCC
+SSC [C@H](NC(=O)C
+SSC [C@@H](N
+S(=O)(=O) c2c1
+Oc3cc( F)c(
+OC(=O)C1=C N(
+OC(=O)C) C
+OC [C@](O)(CO)
+O=C(CC CCCC
+O2)C =C
+O) s
+Nc1 s
+Nc1 ccc(Cl)cc1
+N[C@@H](CC (=O)O)
+NS(=O)(=O) c1ccc(cc1
+NC(=O) C(=N
+F)C (C)(C)C
+Clc1ccc( NS(=O)(=O)
+Clc1 cc(Cl)cc
+Cc1n[nH] c2
+Cc1ccc2 ccccc2c1
+Cc1ccc(cc1 C(=O)N
+Cc1ccc(cc1 )N2C
+Cc1cc(C) cc(N
+C[C@]12CC[C@H]3[C@@H](CC c4cc(
+C[C@H]1 O[C@H]([C@H](O)
+CS(=O)(=O) NC(=O)c1
+COc1cccc2 cc(o
+COc1ccc( cn1
+COc1ccc( CCN2C(=O)
+COP(=O)(S)O [C@H]%1
+CN(C)CCCNC(=O) c1cc(
+CCc1 c(C)c2cc3
+CC[C@H](C)[C@@H]1NC(=O)[C@@H]2CSSC[C@@H]3NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(=O)N)NC(=O)[C@@H]4CCCN4C(=O)[C@H](CC CNC(=N)N)NC(=O)[C@H](CSSC[C@H](NC(=O)[C@@H](N)CC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)N
+CC[C@@]1(C[C@H]2CN(CCc3c([nH]c4ccccc34)[C@@](C2)(C(=O)OC)c5cc6c(cc5OC)N(C)[C@H]7[C@](O)([C@H](OC(=O)C)[C@]8(CC)C=CCN9CC[C@]67[C@H]89)C(=O)OC)C1)NC(=O) c%10
+CCOC(=O)C1 =C(N)N
+CCN1C=C(C(=O)O)C(=O) c2cc(F)c(
+CCN C4
+CCCN 3CCOCC3
+CCCCCCCCCCCC CCC(=O)N
+CCCCC (O)
+CC1(C) [C@H]2C
+CC(C)n1cc(C(=O)c2cn cc(NC(=O)
+CC(C)(C) Nc1
+CC(=O)O) CC(=O)O)
+CC COc1ccc(C
+C3CCC 3
+C2CCCCC2 )C(=O)
+C2= NC(=O)N(C
+C(=S )C
+C(=O)NC(=O)N(C 2=O)
+C(=O) CN(CC=C
+C [C@@H](NC(=O)C
+C )cc2
+5CC6 CCC(C
+4CCN(CC 4)
+4CCC(CC4)N 5CCCCC5
+3CCCC3 )c2
+3CCC4(CC 3)C=C
+3C [C@@H]4
+3)c4 ncccn
+3)N 4CCCC4
+2CC(C 1)c3
+2C(=O) S
+2) NC(=O)N
+1)c2cc3 ccccc3
+/C/ 4
+)n cc23
+)c4ccc( O)c(O)
+)c3ccc(cc3 )[N+](=O)[O-]
+)c1 cccc(c1
+)c(Cl )cc1S(=O)(=O)N
+)c(C)c(C) n3C
+)c( c6ccccc6
+)c( [nH]1
+)c (CC=C
+)NC3=O)C(=O)N[C@@H](CCCNC(=N)N )C(=O)N[C@@H](CCCCN)C(=O)N[C@@H]([C@@H](C)O)C(=O)N
+)NC(=O)[C@H](C c2ccccc2)NC(=O)
+)N(C(C(=O)N C2CCCC
+(O)C OC
+(C)CC 2CCCCC2
+(C)C)NC(=O)[C@H](CC (C)C)NC(=O)
+(C)C )c4ccccc4
+(C) nc1
+# CCC
+nc3ccccc3 n2C
+nc1 s
+nc(N)s c5
+n6 ccnc6
+n2c( SCC(=O)N
+n1)C2 CCN(C
+n( c5
+cccc 5
+ccc(F)cc2 F
+ccc(Cl )c(c1
+cc(Br )c1OC
+c8 c7
+c7 nc1s
+c6ccc( O)cc3
+c5ccc(O)cc5 O
+c4csc(n4)c5 csc(n5
+c4ccc( NS(=O)(=O)C
+c3nn nn3
+c3ccnc4cc(Cl)ccc3 4
+c3ccc4OCC c4c3
+c3ccc(F)cc3)c4 ccc(F)cc4
+c3ccc(C) nc3
+c3c(C) nc(N)nc3
+c3 cc(Cl)ccc13
+c2oc( Br)cc2
+c2nc3ccccc3 s2)cc1
+c2cccc( \C=C\
+c2ccc(F)cc 12
+c2ccc(F)c(Cl )c2
+c2ccc(Cl)cc2 )n1
+c2[nH] c3ccccc3c2
+c1nn cs
+c1cccs 1
+c1ccccc1) [C@H](O)
+c1cccc(c1 )C#N
+c1cccc (CC(=O)N
+c1ccc2ccccc2c1 )N
+c1ccc( NS(=O)(=O)C
+c1 ccc
+c(cc( cc2
+c(N)ncn c%11
+c(N)n c2
+c %6
+\N=C(/S)\ NC
+[n+]2 ccccc2
+[O-][N+](=O)c1ccccc1 S(=O)(=O)N
+[O-][N+](=O) c1cc(Cl)ccc1
+[C@H]2CCCN C2
+[C@H]2 OC(=O)C)
+[C@H]2 O)cc1
+[C@H]1CC[C@H]2[C@@H]3CC =C4C[C@H](CC[C@]4(C)[C@H]3CC[C@]12C
+[C@H](O)[C@H](C c1ccccc1)NC(=O)
+[C@@H]6 O[C@H](C)C[C@@H]([C@H]6
+[C@@H]3[C@H](O)C c4ccccc34
+[C@@H]2[C@H]1 C
+[C@@H]2CC [C@@]3(CC
+[C@@H]1C 2
+[C@@H]([C@H]1 C(=O)N
+[C@@H]([C@@H](C) O)C(=O)N
+[C@@H](OC c4ccccc4)
+[C@@H](C) C(=O)N1CCC[C@H]1
+P(=O)([O-] )OC
+Oc1 ncccc1
+Oc1 cccc(CC
+O[C@@H]9 O[C@H](CO)[C@@H](O)[C@H](O)[C@H]9
+OC(CO)C(O)C(O) C2
+OC(=O) Cc1ccccc1
+O=C(N CCc1ccccc1
+O=C(CN1 C(=O)N
+Nc1nc(O)c2ncn (C
+NC(=O)c2csc(n2)c3ccc(nc3 c4csc(n4)c5csc(n5
+NC(=O)[C@H]( NC(=O)[C@H](C
+NC(=O)O [C@]13C)
+NC(=N)c1 cccc(C
+N=C(C S
+N(C(=O) C(=C2C(=O)
+N 4
+Fc1ccccc1 C2=N
+Fc1cc( F)c2
+FC(F) Oc1ccc(Cl)cc1
+Cn1nc( cc1C(=O)N
+Cn1cc(cn 1)c2cc(
+Cc1ccc( s1)c2
+Cc1ccc( c(C)c1)c2
+Cc1cc(ccn1)c2n[nH] c3cc(
+COc1ccc(cc1)N 2CCN(CC2)C
+COc1ccc(cc1 )C(=O)C
+COc1cc2c(Oc3ccc( NC(=O)C4=N
+COc1cc(\C=C\C(=O) CC(=O)\C=C\
+CNc1nc(N c2ccc(cc2
+CNc1n ccc(
+CN1CCN(CC1)c2 ncc3
+CN1CCCN(CC 1)C(=O)
+CN1C(=O)NC(=O)c2c1 nc(N
+CN c4
+CCn1 ccc(
+CCc1 oc(
+CCS c1n
+CCOC(=O)N1 CCN(CC1)c2
+CCOC(=O) CCCC
+CCNC(=O) CC
+CCCNC(=O)\C=C\ c3ccc(Cl)c(Cl)c3
+CCCN 2CCOCC2
+CCCCCCCC N1C(=O)
+CCCCC1=NN(C(=O)N1C c2ccc(cc2)c3ccccc3
+CCCC(C 1)c2
+CCCC [C@]1(CC)C
+CCC2 C3CCC4
+CCC (F)(F)F
+CC1=C N(CC(=O)N
+CC(O) (C(=O)N
+CC(N )C(=O)N
+CC(C)CN (NC(=O)
+CC(C)C [C@@H](NC(=O)
+CC(C)(C)CC [C@@]7(CC[C@@]56C
+CC(=O)N[C@@H](CC (=O)O)C(=O)N[C@@H](C
+C3=C(CCCC 3)C(=O)
+C3 CCNCC3
+C2CCN(C c3ccccc3)CC2
+C2CC c3c(C2)
+C(=O)c4c(O) cccc4
+C(=O)[C@]2(C) [C@@H](O)C[C@H]3OC[C@@]3(OC(=O)C)[C@H]2
+C(=O)NC c3occc3
+C(=O)N 3CCC
+=C1 2
+6CCC[C@H]6 C(=O)O
+6 =CC
+5 )c8
+4CCOC [C@@H]4C
+4CCN(C c5ccccc5)CC4
+4)N 5CCCC5
+4) ccc2F
+4 )C(F)(F)F)
+3CCN(CC3)c4ccccc4 OC
+3CCC(CC 2)
+3CC c2c1
+3CC OC3
+3)N 4CCCCC4
+3 4)c5
+2[C@H](C c3ccccc3)
+2CCN(CC2)c3nc(NCCOCCOCCOCC#C)nc(n3)N 4CCN(CC4)C(=O)
+2CCN(CC2)C(=O) CO
+2CCC(CC2)N3CC(CC#N)(C3)n4cc(cn4)c5ncnc6[nH]ccc5 6
+2CCC(CC N
+2)ccc1 C
+2)c3 ccc(Cl)c(Cl)c3
+/C(C)C(O)C(C)C(O)C(C)C (OC(=O)C)C1
+)n3 cccn3
+)cc3 C(=O)c2c1
+)cc1 C(=O)O
+)c5cn c(N)nc5
+)c5ccc6 OCOc6c5
+)c4 nnnn4C
+)c2ccc(OC )cc2
+)c( SC
+)O [C@H]5
+)O 3
+)NC(=O)[C@H](CO) NC(=O)
+)N =[N+]=[N-]
+)C4 CCNCC4
+)C4 (CC
+)C3 =
+)C(=O)O) cc3
+)C(=O)N[C@@H](Cc2ccccc2 )C(=O)O
+)C(=O)N[C@@H](C c7c[nH]c8ccccc78
+)C(=O)NCC 3CCCO3
+)C(=O)NC 5
+)C(=O)C )cc1
+)C(=O) C(=O)NC
+(c3ccccc3 )c4ccccc14
+(S CC1=O)
+(F)(F)F) C(=O)N
+(C)c3 ccccc23
+(C)C)N(C)C(=O)[C@@H](NC(=O)[C@H](CC(C)C)N(C)C(=O) CN(C)C1=O)C(C)C
+( OC(=O)CC
+%12 )c%13
+oc(C) cc3
+nnc( n2)c3
+nnc( SCC(=O)
+ncn(C 3
+ncccc4 c3
+nc(s c6
+nc(sc6 COC
+nc(sc6COC )[C@@H](NC(=O)
+nc(sc6COC)[C@@H](NC(=O) c7nc1s
+nc(C(=O)N )c4
+n3 )c2
+n( n1)c2
+cccc1 c25
+cc(Cl) cc(
+c8 ccc(Cl)cc8
+c6 nc(sc6COC)[C@@H](NC(=O)c7nc1s
+c5cccc(c5 )c6
+c4ccccc34 )c2
+c4 ccc(C(=O)N
+c3nn nn3C
+c3n c4ccc(cc4s
+c3n c(C)c(C)
+c3cccc( NC(=O)C=C)c3
+c3ccc(cc3 )C(F)(F)F)c4
+c3ccc(OCCN 4CCCCC4)cc3
+c3ccc(OC )cc13
+c2no [n+]([O-])c2
+c2ccc(s 2)S(=O)(=O)N
+c2ccc( CCC(=O)O)
+c2c(F)cccc2 F
+c1cn ccn1
+c1ccc(N )cc1
+c1ccc(Cl)cc1 Cl
+c1ccc( Br
+c(cn n2C
+c(C)n( nc2
+\C=N/ NC(=O)
+\C=C\ [C@@H](C)
+\C(=N/OC(C)(C) C(=O)O)\
+\C(=C/ CC
+[n+]3 [O-]
+[P+]( c1ccccc1)
+[C@]34C)[C@@H]1CC [C@@H]2
+[C@]3 (CO)
+[C@H]8O[C@H](C [C@@H]8
+[C@H]1C NC(=O)c2
+[C@H](O)C [C@]3(C)C(=CC
+[C@H](Cc3ccccc3)NC(=O) OC
+[C@@H]7CCCN 7
+[C@@H]5 O
+[C@@H]3CC [C@H]2C[C@H](C
+[C@@H]3C=C [C@H]([C@H]2C(=O)N
+[C@@H](CC SC)
+[C@@H](C) CCC
+[C@@H](C )c2ccccc2
+S(=O)(=O) C[C@@H]1
+S =C
+P(=O)(O) O)C
+Oc4ccc(F)cc4 F
+O[C@@H]1[C@@H](O)[C@@H](C c2ccccc2)N(C
+OC[C@H]1O[C@H]([C@H](O) [C@@H](O)[C@@H]1O)
+OCCO c5c4
+OC1=C OC(=CC1=O)
+OC(=C [C@H](N
+OC [C@@H](N
+O=C(N c1ccncc1
+O=C(C NC(=O)
+O) c12
+NS(=O)(=O) c1cc(ccc1
+NCCCC [C@H](NC(=O)[C@H](CCCNC(=N)N)NC(=O)
+NC(=O)c2ccccc2 Cl
+NC(=O)CS c2nc3
+NC(=O)C c3ccccc3
+NC c3ccccc3
+N(C)CC c7
+N c1ccc(cc1)S(=O)(=O)
+Fc1ccc(cc1)C(=O) CCCN
+FC(F)(F)c1 ccc(Cl)c(
+Cn1c( SC
+Clc1ccc2c(N CCCN
+Cc2 ccc(Cl
+Cc1n nc(s
+Cc1cn n(CC
+Cc1ccc(cc1)c2 o
+Cc1ccc( NC
+Cc1 [nH]c(\C=C\2/C(=O)N
+C[C@H](CO)N1C[C@@H](C)[C@H](CN (C)S(=O)(=O)
+C[C@@H]1 CCC[C@H](N
+COc1ccccc1 c2ccc3
+COc1cccc(CN (C)C(=O)
+COc1ccc2 n
+COc1ccc2 c(CN
+COc1ccc(cc1)S(=O)(=O)N 2CC
+COc1ccc(cc1 NS(=O)(=O)
+COc1ccc(cc1 CO)
+COC(=O)N1 c2nc(N
+COC(=O)C1 (C
+CO CC(C)
+CNC(=O)C[C@@H]1 NC(=O)c2csc(n2)c3ccc(nc3c4csc(n4)c5csc(n5
+CN(C)CC Oc1ccc2
+CN(C c1ccc2
+CN c1ccccc1
+CC[C@H](C)[C@@H]1NC(=O)[C@H](C c2ccc(O)cc2)NC(=O)
+CC[C@@H]1NC(=O)[C@H]([C@H](O)[C@H](C)C\C=C\C)N(C)C(=O) [C@H](C(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)
+CCO c3ccccc3
+CCCN(CC 1)c2
+CCCN(C)C 2
+CCC(N )C
+CC1=C N(CC
+CC(N (C)C
+CC(C)[C@H](CO)N Cc1nc(ccc1
+CC(C)(O)[C@H](F)CNC(=O)c1cnc( cc1N
+CC(C) c1cccc(
+CC(C c1ccccc1)N
+CC(=O)O c1ccc(
+C3CC3)n4ncc(\C=C\5/NC(=O)NC5=O)c4 n2
+C3 CO
+C(=O)N[C@H]1[C@H]2SCC (=C(N2C1=O)C(=O)[O-])C
+C(=O)N(C (=N
+C(=O) CS
+C(=C) [C@@H](OC(=O)C)[C@H](C)C
+C \C=C\1/
+C [C@@H](CN
+Br )c5
+5CCOCC5 )C(=O)N3
+5CC (F)(F)C
+5C =CC(=O)N
+5)c6 cccnc6
+5)C 3
+5 )C(=O)NC
+4CCCCC4 )C3=O)
+4)N =C
+4)C5 (C)C)
+3CC3 )CC
+3)NC(=O) C
+3)C N
+2CCC(= CC2=O)
+2)c3cccc(c3 )C#N
+2)c3cccc( O)c3
+2)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CO) NC(=O)[C@H](CCCCN)NC(=O)
+2)N 4CCOCC4
+)c5ccccc5 )c6ccccc6
+)c4ncccc4F)n( n2)c5ccccc5
+)c3ccccc3 )C(=O)N
+)c3 cnc(s
+)c2cc1 OCC
+)c2 ccc(C
+)c(C) nc2
+)S(=O)(=O) O)S(=O)(=O)
+)O [C@H]1
+)N 2CCCC
+)N 2C=C
+)C1 CCC2=O
+)C(=O)N[C@@H](CCCN=C(N)N )C(=O)O
+)C#N )CC1
+) (O
+(CC=C )C
+(C) CCN(CC2)C(=O)
+onc( n2)C3
+o1 cccc1
+ncc2 ccccc1
+n3c2 C1=O)
+n1)N C
+cnc([nH] 6)
+ccnc( c2
+ccn c4cc(
+c7ccc(O)c(c7)c8c(O)cc(O) cc8[C@H](NC6=O)
+c5ccc(cc5 )C(=O)N
+c4cccn 4C
+c4cccc n4)c5
+c4cc( nn4
+c4c(C 1=O)
+c4c(C1=O) n(C)c5ccccc45
+c3oc( Br)cc3
+c3nn c(C)
+c3ncn c4ccc(cc3
+c3cccs3 )cc2
+c3cccc (C(=O)N
+c3ccc(F)cc3 )c([nH]2)c4
+c3ccc(F)cc 3)\
+c3ccc( nc2
+c3c[nH]c4ccccc34)NC(=O)[C@H](C c5ccccc5
+c2onc( n2)c3ccccc3
+c2ncccc1 2
+c2nc(O) c3ccccc3n2
+c2cn 1
+c2ccc( NC(=O)CN
+c2c3CCCC c3sc2
+c2 nc(N)nc(N
+c( ccc[n+]
+\C=N/ N
+\C=C\C2=C(C)CCCC2 (C)C
+\C=C\ CO
+\C(=N/ CC
+\C(=C/2\ C(=O)N
+[nH]2 )c4ccccc4
+[nH]2 )C(F)(F)F
+[n+]1 CC(=O)N
+[M g
+[C@]9 (CC)C=CCN
+[C@]45 O)C
+[C@H]2C(C)(C) CCC[C@]2(C)
+[C@H](CC1CCCCC1 )NC(=O)[C@H](CC
+[C@H]( OP(=O)(O)
+[C@@]12 O
+[C@@H]5 [C@H](O)C
+[C@@H]3[C@@H]2CC [C@]4(C)[C@H](CC[C@]34
+[C@@H]3O[C@H](CO)[C@@H](O)[C@H](O)[C@H]3 O)c4
+[C@@H]3CCCN3C(=O) [C@@H]4CCCN4C(=O)
+[C@@H]3 NC(=O)
+[C@@H]2[C@H]3 [C@H](C(=O)N
+[C@@H]1 CCCC
+[C@@H](OC(=O)C )C(C)(C)
+[C@@H](OC(=O) c5ccccc5)
+[C@@H](O)[C@H](O)[C@@H](CO) O[C@H]2
+[C@@H](CC [C@]4(C)[C@H]3CC[C@]12C
+[C@@H](CC 1
+[C@@H](C)O) [C@@H](C)O)
+[C@@H](C 1)NC(=O)
+[C@@H]( OC2
+S(=O)(=O)N 4CCOCC4
+S(=O)(=O) C=C
+S c2ncnc3
+S c1nc2
+Oc2ccc3 nc(
+Oc2ccc(C (=O)
+Oc1nc2 cc(
+Oc1c( cccc1
+OC(=O)c1 cccnc1
+OC(=O)[C@H](C c1ccc(NC(=O)c2
+OC(=O) COc1ccc(Cl)cc1
+NC1 CCN(CC1)c2
+NC(=O)C2=C (O)
+NC(=O) c5cn
+NC(=O) c2ccccc2)/
+NC(=O) c1ccc(C
+N=C (CC(=O)N
+N1 C=C
+N )c3
+FC(F)(F)c1cccc(c1 )N2
+FC(F )C(F)(F)
+Cn1 nnc(
+Cc2ccccc2 )cc1
+Cc1cc(C)cc(c1 )C(=O)N
+Cc1cc( Nc2nccc(
+C\C=C(\C)/ C(=O)O[C@H]1
+C[C@H](N1CC [C@@]
+C[C@@H]1CN(CCN1 C(=O)C
+C[C@@H](CO)N1C[C@@H](C)[C@H](CN (C)S(=O)(=O)
+COc1ccccc1 OCC
+COc1ccccc1 COCCCO
+COc1cccc2 ccccc1
+COc1cccc(c1 )N2C(=O)
+COc1cccc(NC(=O) CN(C)C(=O)
+COc1ccc(cc1) [C@@H]2C
+COc1ccc(cc1 O)C2
+CNC(=N)N)NC(=O) [C@@H](N)CCC(=O)N
+CN1CC[C@]23[C@H]4 Oc5c(O)ccc(C[C@@H]1
+CN1CCC=C(C 1)c2
+CN1C(=O) C=C(
+CCOC(=O)c1c(C)[nH]c(C)c1 S(=O)(=O)N
+CCOC(=O)C1 =
+CCO CCN
+CCCN 4CCC
+CCCC c1ccc(cc1)c2
+CCCC c1c(
+CCCC 1C
+CC(C) (CC(=O)N
+CC(=O)O[C@H]1C(=O)[C@]2(C) [C@@H](O)C
+CC( OC(=O)C
+CC COc1ccc(cc1)C(=O)N
+C=C )C(=O)NS(=O)(=O)C
+C4(CC 4)C#N
+C2(CC 2)C(=O)N
+C(N (CC
+C( c1ccccc1)c2ccccc2
+6)c7 ccc(
+5) (C
+4CC 3
+3\ C(=O)N
+3)c4n ccs
+3)N4C =CC(=CC4=O)
+2CCCCC2 )c3ccccc3
+2CC (C2)
+2)c3cn cc(c3
+2)c3cccc( NC(=O)
+2) (c3ccccc3)c4ccccc4
+/ C(=O)
+)ncnc2cc1 OCC4
+)cccc2 C1=O
+)cc3 )c1
+)cc(c1 )C(=O)O
+)c3c2 )c1
+)c3 )cc2
+)c2 c3
+)c(OCC 4CC4)n2
+)c(O) c3
+)c nn4
+)C2 CCCN(C
+)C(=O)N[C@@H](CC(C)C )C(=O)N[C@@H](CC(=O)N
+)C(=O)C=C 2
+)C(=O) CC(C
+)C =CC1=O
+) c1ccccc1
+) [nH]2
+(F) F)cc1
+(CC CCCCCCCC
+(C)N(C 1=O)
+(=O)O)NC(=O) [C@@H](NC(=O)[C@H](CC
+nn3 c2
+nn n(C)
+nn 4CCCC
+nc([nH] c3c2
+n1 nc(
+cnc(N )c(OC
+ccc[n+] 2C
+ccc4 ccccc34
+cc6 ccccc6
+cc4 5)
+cc(o c1
+cc(OC )c4
+cc(F)cc( F)c4
+cc(Cl)cc(Cl )c3
+cc(C(=O) O)c(O)
+c8 ccccc6
+c5cocc5 )
+c4ccccc4)n n3)
+c4ccccc4 )cc2
+c4ccc(F)cc4 )cc3C1=O
+c3ccccc23 )c1
+c3ccc(Cl)cc3 )cc2
+c3ccc(Cl)cc 3CC
+c2ccncc2 )c3
+c2cccc3ns nc23
+c2cccc(c2)c3 cn4
+c2ccc3 C(=O)N(CC
+c2ccc(cc2)c3 cs
+c2ccc(OC c3cc(C)nc4ccccc34
+c2c(C)onc2 C)
+c1occc1 )C(=O)N
+c1n cc(s
+c1ccc2 OC
+c1ccc(cc1)C(=O) OCC(=O)N
+c(c1)c2 3
+c(C) cc(O)
+c%13 %1
+\C(=C\ N
+[nH]n cc5c4
+[C@]7 (CC
+[C@H]2C (C)(C)CCC
+[C@H]2 CN(C(=O)O
+[C@H](O) [C@@H]3
+[C@H](O) C(=C)
+[C@H](CO) O
+[C@H](C OC4=O)
+[C@@H]1 N(C)C(=O)
+[C@@H](NC(=O)[C@H](CC (C)C)
+[C@@H](C)C [C@H]1
+[Br-]. CCCCCCCCCCCCCCCC
+S(=O)(=O) c3ccc(C)cc3
+Oc3ccc(cc3 )C(=O)
+Oc3ccc(cc3 )C#N
+O[C@H]4 CO
+O[C@H](CO)[C@@H](O)[C@H](O) [C@H]5O)
+OCCN(C c1ccccc1)C(=O)C
+OC [C@H]4
+O=C1N(CC CN
+O) CC2
+O 4)
+NC1=N [C@@H](CC
+NC(=O)[C@H](CC CCN
+NC(=O)C 2
+NC(=O) NC(=O)
+NC (N
+N3 [C@@H](C
+F) CC2
+Cn1ncc(NC(=O)c2nc(sc2N)c3 c(F)cccc3F)c1
+Cc1s c2ncnc(N
+Cc1onc(c1CO c2ccc(cn2)C(=O)N
+Cc1cn( cn1)c2
+Cc1cccc( Nc2n
+CS(=O)(=O)N 1CCC
+COc1ccc(cc1 OCCN
+COc1ccc(\C=C\2/ SC(=S)N
+COc1ccc( \C=C
+COc1cc2cc([nH]c2c(OC)c1 OC)C(=O)N
+CO\C(=C/1\NC(=O)[C@@H]( NC(=O)c2csc(n2)c3cc(O)c(nc3c4csc(n4)[C@@H]5COC(=O)c6c7CO[C@@H]([C@H](NC(=O)c8csc1n8)c9nc(cs9)C(=O)N5)[C@H](O[C@H]%10C[C@](C)(O)[C@@H]([C@H](C)O%10)N(C)C)C(=O)OCc%11cccc(c7%11)n6O)c%12
+CNC(=O) c4c5
+CN1C(=O)C=C(N=C1N2CC O[C@H](C
+CN(C)S(=O)(=O) c1ccc(N
+CCn1 ncc(
+CCc1n c2ccccc2n1
+CCOC(=O)c1cc( o
+CCCCCCCC(=O) O[C@@H]1
+CCCC(C 1)N
+CCCC CCc1
+CCC NC(=O)N1
+CC2 N(CC
+CC2 =CC(=O)N
+CC1 C2
+CC(C)n1 cnc2c(N
+CC(=O) C(=O)N
+CC [C@H](CC(=O)
+C1 CN(CCN1
+C(=O)N2 C
+C(=O)C(= CN(
+C(=O) c2c[nH]
+C(= CC2=O)
+C c2ccc3OCOc3c2
+C (C)CC
+=CC(=O)N(C)C =C1
+6CCC [C@@H]6
+5CCNCC 5)n
+5CCCCC5 )c4
+4CCN(CC4)c5 ncccn5
+4)C 6
+3CCCC 3C
+3CCC (F)(F)
+3)N (C)C
+3) on1
+3 )cccc4
+2CCN(CC2)C (c3ccccc3)c4ccccc4
+2CCC(CC2)N 3C(=O)
+2CCC (CO)
+2)c3ccc(OC (F)(F)F)cc
+1)c2 ccc(Cl)c(Cl)c2
+1)C(=O)N (C
+)n2 ccnc2
+)cn 6C)cn5
+)c5ccccc5 F
+)c2ccccc2 Cl
+)c12)C 5CCC5
+)c(OC )cc1Cl
+)c( \C=C\
+)c nn1C
+)NC(=O)[C@H]1 CCCN1C(=O)
+)N6CCCC 6
+)C(=O)OC )c2
+)C(=O)N[C@@H](CC(C)C )C(=O)N[C@@H](CCCCN
+)C(=O) c6
+)C(=O) C(=O)c2c1
+(Cl) (Cl)
+(CC =C(C)C
+(C)C )C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CCCCN
+( n3
+s 5
+onc( n2
+n2cc(nc2 s1)c3o
+n2c(cc3 ccccc2
+n2 nc3
+cnc([nH] 8)
+ccn 1
+cccc( NC(=O)N
+ccc2 N
+ccc(Cl)c( c5
+cc(F)cc( F)c3
+cc(F) c1)C(=O)N
+cc(Cl)c( F)cc3
+c6ccc(C)cc 6
+c6c7 c8ccccc8
+c5nc6ccccc6 [nH]5
+c5ccc(cc5 )[N+](=O)[O-]
+c4n c(C)cc(C)
+c4ccc(F)cc 14
+c4cc( cc(C
+c4c5 C(=O)NC(=O)c5
+c3o c(C)nn
+c3nc[nH] c3
+c3n nc(SCC(=O)N
+c3ccccc3)N C
+c3ccccc2 3)c4ccc(
+c3ccc(cc3 )C(=O)C
+c3ccc(Cl)cc3 )CC2
+c3ccc(Cl)cc3 )C1=O
+c3c(C 2=O)
+c3 cccnc1
+c3 4)C(=O)
+c2n cc(C)
+c2n c(C)c(s
+c2cs cn
+c2ccccc2)C(=O)N CC(=O)N
+c2ccccc2 c1N
+c2ccccc2 C1
+c2ccccc2 )C(=N
+c2cccc(C(=O)N (C)C)c2
+c2ccc3 [nH]nc(
+c2ccc(cc2)c3 noc(
+c2ccc(O)cc2 )N
+c2ccc(Br)cc2 )c1
+c2cc3ccccc3 [n+]
+c2cc( F)cc
+c2CCCC c2n
+c2 CC
+c2 C1=O
+c1n[nH] c2cc(NC(=O)N
+c1n ccc(n1)c2
+c1ccccc1 )C(=O)NC
+c1cccc(c1)c2 cc(NC(=O)[C@@H]3CNC(=O)C3)nn2
+c1c(cccc1 C(C)C)C(C)C
+c(Cl )c4
+c(Cl )c1Cl
+c%12 ccccc%11
+\C=C\C(=C\ C(=O)\C=C\
+[nH]ncc5 c4
+[n+]1 cccc(
+[C@]5(C) [C@H]4
+[C@H]6 O[C@@H]7
+[C@H]4C [C@H]5
+[C@H]3C [C@H]4CC[C@@H](C
+[C@H]2O [C@H]3
+[C@H]2C [C@H]2
+[C@H](O)C(=C[C@@H](C(=O)O3) [C@]67O)C)
+[C@H](N 3
+[C@H](C)O2)[C@H](C)[C@@H](O [C@@H]5O[C@H](C)C[C@@H]([C@H]5
+[C@H](C [C@@H](C)
+[C@H]( SC
+[C@@H]6C [C@H]5
+[C@@H]6 CCC(=O)N
+[C@@H]5CC[C@H]4C 5)C#N
+[C@@H]5 CCCC
+[C@@H](N C4=O)
+S(=O)(=O) c1ccc(C)cc1
+Oc4 c3
+Oc3c2 )c1
+OCCN1 CCN(CC1)C(=O)
+OC2=O) c1
+OC(C 2)C(=O)N
+OC(=O) c1cccc(N
+O=C(N Cc1cccs
+O [C@]
+Nc2 ccnc(N
+NS(=O)(=O) c1n
+NS(=O)(=O) c1cc2
+NC(=O)NC1 C
+NC(=O)C2 CCCN2S(=O)(=O)
+NC(=O)C )c2c1
+N1 )C
+N [C@H]1CC
+N 4CCCC
+FC(F)(F)c1cc( nc2
+F)c3 c2
+Cn1nnnc1 SCC2
+Cl. Cc1ccccc1
+Cc1ncoc1 c2nnc(S
+Cc1ccnc( c1
+Cc1ccc(cc1 )N2CCN(CC
+C[C@]1 (O)
+C[C@H]1 C[C@H](C
+CS(=O)(=O) c1ccc(CN
+COc1ccc2cc(ccc2c1 )C
+COc1ccc(cc1OC 2CCCC2)
+COc1cc( Nc2ncc3
+COc1 ncn
+COCCCOc1cc(C[C@@H](C[C@H](N) [C@@H](O)C[C@@H](C(C)C)C(=O)N
+COC(=O) [C@@H](NC(=O)[C@@H](NC(=O)
+CO CCc1
+CN1 C(CC
+CN(c2ccccc2 )C1
+CN(C) \N=N\
+CN(C 1CCCC1)C(=O)
+CCc1n nc(NC(=O)
+CCOCCOCC NC(=O)
+CCOC(=O)c1 sc2
+CCOC(=O) [C@]12CCCC
+CCN)NC(=O)[C@H](CO)NC(=O)[C@H](CCC(=O)N)NC(=O)[C@H](C c4ccccc4)NC(=O)C)C(C)C)C(C)C)C(=O)N
+CCN(CC CNC(=O)
+CCCN 3CCCC3=O)
+CCCN 3
+CCCCn1 c2ccccc2
+CCC NC(=O)CC
+CC2 CCC
+CC1 CN(C(=O)
+CC1 CCC(CC1)N
+CC(C)c1n oc(
+CC(C) [C@H](C
+CC(C) NC(=O)OC
+CC(=O)N[C@@H](C c1ccc(OP(=O)(O)O)cc1
+CC(=O) S
+CC(=O) C=C(C
+C3=C(O)[C@]2(O)C(=O)C(=C1 O)C(=O)N
+C3 CCCN3
+C2=O) C(=O)O
+C1=C (C(=O)
+C1 )C(=O)
+C(F)(F) F)cc1
+C(C) (OC(=O)
+C(=O)N(CCN (C)C)C(=O)
+C(=C(O)O1) S
+=CC 3=C
+6CC7CC(CC(C7)C 5)C6
+5CCN(CC 5)c6ccccc6
+4CCC(CC3)(CO4)NC c5ccc6OCC(=O)Nc6n5
+4C=C (C)C(=O)NC4=O)
+4)N 5CCOCC5)c6
+3CCC 2
+3)c4nc5ccccc5 [nH]4
+3)c4ccc(cc4 )c5
+3)C(=O) C(=O)N
+2CCC(CC2)c3 onc(n3)c4
+2CC (=NN2
+2)C (=C)C
+1CCCCC1 )c2ccccc2
+1)C (C
+)nc(C) c1C
+)n n6
+)cc2 Cl
+)cc cc5
+)c6 cc(Cl)ccc6
+)c5 C
+)c4ccccc4 C(F)(F)F
+)c4 cn[nH]c4
+)c2 )n1
+)c(Cl )c
+)c(C)c1 S(=O)(=O)N
+)[C@@H]2 3
+)NC(=O)C (C
+)CC3 )cc2
+)C2 3CC4CC(CC(C4)C2)C3
+)C(C)(C) S)
+)C(=O)N[C@@H](CCC(=O)O) C(=O)N[C@@H](CC(C)C
+)C(=O)NC(=O)N C1=O
+)C(=O) C(=C3
+(c3ccccc3 )c2c1
+(O)C1 O)
+(CCN3CC6CC6 )c45
+(CC 2)C(=O)N
+(C)C )n(C
+(C)(C) N
+(C) CN
+(C(=O)C )C(=O)
+(=C(N3C2=O) C(=O)O)
+(=C (C)N
+%1 2
+s1 )S(=O)(=O)
+s 3)cc1
+ncn(C(C)C)c3 n1
+nccnc2C3CN(C 3)c4ccc5ccccc5n4
+nc(C) n3
+n2 cc(C(=O)
+ccnn c4
+cccc(OC )c2
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+COc1ccccc1 NC(=O)C2=C
+COc1ccc2nccc([C@@H](O)CN 3CCC(CC3)NC
+COc1ccc2C[C@H]3 N(C)CC
+COc1ccc(C )c2
+COc1cc2 OC(C)(C)
+COc1cc(ccc1O) [C@H]2
+COc1cc(NC (C)
+COc1c(O) ccc2
+CO CCCc1
+CN1CCN(CC1)C2=N c3cc(Cl)ccc3
+CN(C)C c5
+CN(C c1ccc(F)cc1)C(=O)
+CC[C@H]1OC(=O)[C@H](C)[C@@H](O[C@H]2C[C@@](C)(OC )[C@@H](OC(=O)
+CC[C@@]1(O)C(=O)OCC2=C1C=C3N(C c4c(
+CCOc1cccc( c1)C(=O)N
+CCOc1ccc( CC
+CCO[C@H]1 OC(=C
+CCOCCOCC OCCNS(=O)(=O)
+CCOC(=O) C(C)(C)
+CCN1 CCN(CC1)S(=O)(=O)
+CCN(CC1)C(=O) [C@H](C
+CCN(CC )C(=O)N
+CCCCCCCCCCCCCCCC (=O)OC
+CCCCC [C@@H]1
+CCCC c3n2
+CCC (=N
+CC3C 4C
+CC1 )\C
+CC(C)Nc1nc( cc2N=CN(C)C(=O)
+CC(C)(C)OC(=O)N 1C
+CC(C)(C)CN1CCC2(CC1)CN(c3ccccc3 NC(=O)N
+CC c1cccc(c1
+C=C (\C)/
+C4 (CCOCC
+C(=O)N(C (=O)N
+C(=O)C=C (C)
+C(=C(N )O
+C(=C )C(=O)N
+C#N )C#N
+C N
+=CC(=CC3=O) OC
+=C6C[C@H](CC[C@]6(C)[C@H]5CC[C@]4(C)[C@H]3 [C@@H]2C)O[C@@H]7
+=C( Oc4cc(O)cc(O)c4C3=O)
+=C 4C(=O)
+=C 2
+= O
+7OO 8
+5 )c3
+4CCCC4 )C3
+4)c(n2)c5cc(Cl)ccc5 OC(F)F
+4)C2 )C3
+3CCC [C@@H](C
+3CC4CC (CC2C
+3CC(C 3)
+3C =N
+2C[C@@H]3C OC
+2CCOCC 2)\
+2CCN(CC2)S(=O)(=O) c3ccc4
+2CC 2
+2 OC(C
+)n3 )cc1
+)cc 4C3
+)c4ccc( cc2
+)c3ccc4 ncc(nc4c3)c5cnn(C)c5
+)c2cc( o
+)c2 ccccn2
+)c (CCN
+)NC1=O) C(=O)O
+)C2=N c3ccccc3
+)C(C)(C)CCC3 (C)C
+)C(=O)O) C(=C)C
+)C(=O)N[C@@H](CC(=O)O)C(=O)N [C@@H]([C@@H](C)CC
+)C(=O)N[C@@H](C c6ccc(O)cc6
+)C(=O)N [C@@H](C)C(=O)N[C@@H](CCCCN
+)C 3CC3
+(O) (CN
+(CC=C (C)C)C(=O)
+(C(C(=O)N C2CCCC
+(C [C@H](N
+(=O) (=O)
+%14 cnc%15
+nc3 N
+n4 cccc4
+cc(OC )ccc6
+cc(NC(=O) c7
+cc(Cl)ccc4 c3
+cc(Br )cnc3
+cc( nn3
+c8 ccc(Cl)c(Cl
+c8 )c9
+c6 cccc(Cl)c6
+c5ccccc5 )ccc4
+c5ccc( Oc6ccccc6
+c5 ccnc(N
+c5 ccccc1
+c5 cc(OC)c(OC)cc4
+c4cccc5 cccnc45
+c4ccc(F)cc 4)c5ccc(F)cc5
+c4ccc(Cl)cc 4)C(=O)
+c4ccc( NC
+c4c[nH] cn4
+c3ccc(cc3 )C(C)C
+c3ccc(O) c4
+c3ccc(F)cc 3)n1
+c3ccc(Cl)cc 3C
+c3ccc(Cl )s3
+c3ccc(Cl )nc3
+c3cc(nn3 c4ccccc4)C(=O)NC
+c3c(C)cccc3 C
+c2ccccc2 OC1
+c2ccc3c(ccn 3C
+c2ccc3 cn
+c2ccc(cc2)S(=O)(=O)N 3CCCCC3
+c2cc3ccccc3 nc2
+c2cc(Cl)ccc2 OC
+c2c3 CCC
+c1ccc2c(c1 )C(=O)
+c1ccc(O)cc1 )NC(=O)[C@H](CO)NC(=O)[C@H](C
+c1c[nH]cn 1)C(=O)N
+c(cn 3
+c(N)n [nH]
+c(Cl )c(OC
+c(C)c (CC(=O)N
+c(C(=O) CC(C)C
+c( ccnc2
+\C=C/ CC
+[nH] 7)c(
+[nH] 1)C
+[C@](O) (CC)
+[C@H]9 O[C@H](C
+[C@H]7 O
+[C@H]4O[C@H](C[C@@H]4OP(=O)(O)OC [C@H]5O[C@H](C[C@@H]5
+[C@H]2C [C@@H]3
+[C@H]1C c2ccccc2
+[C@H](O)[C@@H](O) [C@H](O
+[C@H](CC =O)
+[C@H](C c3ccccc3)C(=O)N
+[C@H](C c3c[nH]c4ccccc34
+[C@H]%10 O[C@H](C
+[C@H]%1 7
+[C@@]1 5
+[C@@H]5CC[C@@]34C)[C@@H]2[C@H]1C )C(=O)O
+[C@@H]3 [C@H](OC(=O)
+[C@@H]2CC[C@H]1C[C@H](C 2)c3cccc(c3)C(=O)N
+[C@@H]2C [C@@H]1
+[C@@H]1Cc2ccccc2 CN1
+[C@@H]1 CCC(=O)N
+[C@@H](NC(=O)CNC(=O)[C@H](CCC(=O)O) NC(=O)[C@H](C)NC(=O)[C@@H](N)C
+SC(=N 1)N
+S CC(=O)O
+S C(=O)C(C)(C)C
+OCc3ccccc3 )cc1
+OC(=O)C1 CCCCC1
+OC(=O) CC1=NN(C
+OC c2ccccc2)C(=O)N
+O1 )C(=O)O
+O O2
+Nc1 sc(
+NS(=O)(=O)C) ccc3
+NC1=N C2
+NC(=O)c1cccc2 [nH]c(nc1
+NC(=O)[C@H] (CN
+NC(=O)CN2C(=O)CO c3ccc(cc23)S(=O)(=O)N
+NC(=O)C c2ccccc2
+NC(=O) c1cccc(c1)c2
+NC(=O) CCCN2C(=O)
+N1 C(=O)N(C
+N(CC N(CC(=O)O)
+N(C )c3ccccc3
+N 2CCOCC2
+Cl. C(N
+Cc2c(CN 1
+Cc1onc(c2ccccc2Cl )c1C(=O)N
+Cc1ccc(cc1 NC(=O)c2cn
+Cc1ccc(C )c2
+Cc1cc(ccn1)c2n[nH] c3ccc(cc23)C(=O)N
+Cc1cc(O)cc(C)c1C[C@H](N)C(=O)N2C c3ccccc3C
+Cc1cc(N )nc(C)c1C
+Cc1cc(C) cc(C
+C[C@]12CC [C@H]3[C@@H](C
+C[C@]12CC [C@@H](O)
+C[C@H](NC(=O)C) c1ccc(OC2CCN(C
+COc1cccc(CC 2
+COc1ccc(C[C@H]2NC(=O)\C=C\C [C@H](OC(=O)[C@H](CC
+COc1 nc(N)nc2
+COc1 ccn
+COC(=O)c1c(O)cc(O) c(Cl)c1
+CNC(=O)c1cc( Oc2ccc3
+CN1 [C@@H](CC
+CN(CC c1ccccc1)C(=O)
+CN(C)CC NC(=O)c1
+CCc1 cc(C(=O)C
+CC[C@H](N )C(=O)O)C(=O)N
+CCOC1 =CC(=O)N(C)C=C1
+CCOC(=O)N c1ccc2
+CCOC(=O)N 1C
+CCOC(=O) [C@H](CC
+CCNC(=O)[C@H]1 O[C@H]([C@H](O)[C@@H]1O)n2cnc3c(N
+CCN(CC)CCCC(C) Nc1
+CCCC c6
+CCC=C (C)C
+CCC(=O)N1CC c2cc(ccc12)S(=O)(=O)N
+CC2 CN(CC
+CC2 3CC4CC(CC(C4)C2)C3
+CC1=CC(=O)Oc2c1 ccc3
+CC1=CC(=O)Oc2 cc(OC
+CC1(C) C(=O)N
+CC(O) CC(O)
+CC(C) c1ccc(NC(=O)N
+CC(= CCC1
+C=C(C)C(=O)NC %19
+C=C(C(=O) Oc1
+C2CC S(=O)(=O)C2
+Br c1cccc(C
+5CC SCC
+5 (CCN(CC
+4CCN(CC4)C(=O) c5ccccc5
+4)c5ccccc5 )cc1
+4) OC
+3Cc4ccncc4 C3)cc2
+3CCN(Cc4ccc5OCOc5c4 )CC3
+3C [C@H](C
+3=O) CC2
+3 .OC(=O)C(F)(F)F.OC(=O)C(F)(F)F
+3 )c(n1
+2CCN(CC2)c3 cccc(Cl)c3
+2CCCC2 )c1
+2=N c3ccccc3C(=O)N2
+2 [C@@H](
+1 [O-]
+. CO
+)nc(n1)N (C)C
+)c5 cn[nH]c5
+)c2ccc( O)cc2
+)c(C)cc1 N
+)NC(=O)[C@H](CCCCN )NC3=O)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H]([C@@H](C)O)C(=O)N
+)N(C)C (=N)N
+)C=C 2)C=C1
+)C(=O)N[C@@H](CCC(=O)N )C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](C)C(=O)N[C@@H](CCCCN
+)C(=O)NC c3ccc(F)cc3
+)C(=O)NC (C)(C)C
+)C(=O)N 4CCN(CC4)c5
+)C(=N)N )C(=O)N
+)C#N )S(=O)(=O)
+)C c2ccccc2
+(c4ccccc4)c5ccccc5 )c6
+(OC )C
+(O) C(CO)
+(N =C1N
+(CCN 2C
+(CC CNC(=O)
+(C)C)NC(=O) [C@@H](CC
+(C)C )c2c1
+(C(=O) C(=O)N
+(C c1ccccc1)C
+%19 C=C(C)C(=O)NC%19
+%1 1)C
+s 3)c5
+nn3 c(nnc3s2)c4
+n cccc
+cn 2)C(=O)N
+ccc( NC(=O)N
+cc4 Cl
+cc3 n2
+cc(C )c2
+c5[nH] ccc45
+c4ccccc4[nH] c13
+c4ccccc4 [nH]c2
+c4ccccc4 )n2
+c4ccc(O) cc3
+c4cc(O)c(O) cc4
+c3o cnc3
+c3nc( cn2
+c3cn c(NC
+c3ccccc23 )C1=O
+c3cccc(C) n3
+c3ccc4 N(C)C(=O)
+c3ccc(Cl)cc3 )cc2
+c3cc(C )c4
+c3c(F)cc( F)cc3
+c3 c(Cl)cc(
+c2o c3ccccc3c2
+c2nc( ns
+c2nc( OC
+c2n ccc[n+]2C
+c2cn c(NC(=O)
+c2ccc(cc2)N (CCCl)CCCl
+c2ccc(O) c(Cl)c2
+c2ccc(Cl )s2
+c2ccc(Br )cc12
+c2cc(C)ccc2 C
+c2c( nn
+c1nnc( SCC(=O)N
+c1ncn c2c1cnn2
+c1cn c2ccccc2c1
+c1ccccc1)NC(=O)[C@H](C c2cnc[nH]2)
+c1 cco
+c(O)nc3 cc(Cl)c(
+c(C )c4
+c( OC(=O)C
+c%10 )c%11
+\N =[N+](/[O-]
+\C=C\ S(=O)(=O)
+\C=C/ CCC(=O)O)
+[N+]1 (C)
+[C@]2(C)[C@H]3CC[C@@]4(C)[C@@H](CC =C4
+[C@H](N =[N+]=[N-])
+[C@H](C [C@H]3OC[C@@]3(OC(=O)C)[C@H]2
+[C@H](C 2)NC(=O)
+[C@H]%10O[C@H](C [C@@H]%10
+[C@@]2(C)C =CC1=O
+[C@@]2(C)C 3=C(CC
+[C@@H]4 5
+[C@@H]3CCCN3C(=O) [C@H](C
+[C@@H](N) CC(=O)O)
+[C@@H](CC C(=O)O)C(=O)O)
+[C@@H](C [C@@H]1
+SC[C@@H]2 NC(=O)N
+SCC C(=O)O)
+SC )C(=O)O
+S c2c1
+Oc1c( Br)cc
+O[C@H](CO)[C@@H](O)[C@H](O) [C@H]2O
+OOC(O O
+OC3 (CCN(C
+OC(=O)c1ccccc1 N
+OC(=O)c1 [nH]
+O=C1 NC(=O)C(C
+O=C(N c1cccnc1)c2
+O)c(OC )c1
+NC(=O)c2cc( cc(c2
+NC(=O)c1cn(nc1 Nc2ccnc(F)c2
+NC(=O) [C@@H](NC(=O)C
+NC (C)(C)C
+N=C 4C
+N=C (C)c3
+N(CC )CC
+N( [C@H](C
+N# Cc1cn
+N c3ccccc3
+N =O
+N )C(=O)N[C@H]1CCNC(=O)[C@@H](NC(=O)[C@H](CC
+Fc1ccc(C S
+F)c2 cc1OC
+Cn1c2 CC
+Clc1ccc( \C=C\C(=O)
+Cc1ccc( \C=N\
+Cc1cc( OCC(=O)N
+Cc1c(sc2ccccc12)N (C
+C[S+]([O-] )C
+C[C@]12C )[C@@]4(C)CC
+CS(=O)(=O) c1ccc(cc1
+COc1cnc( cn1)C(=O)N
+COc1ccccc1 CNC(=O)C
+COc1cccc(c1 )
+COc1ccc(cc1 N
+COc1ccc( NC
+COc1cc2 c(ncnc2cc1
+COc1cc(OC )c(C
+COC(=O) CCCC
+CN1C(=O)N (C)C2
+CN1 Cc2c([nH]
+CN 4CCOCC4
+CN (NC(=O)
+CC[C@H](C)[C@H](NC(=O)C NC(=O)[C@H](C)NC(=O)[C@H](C)NC(=O)[C@H](C
+CCOC(=O)N1 CCN(C
+CCOC(=O) c1c[nH]
+CCOC(=O) \C=C\1/
+CCN 5CCCC5
+CCC(C)(C) n1
+CCC(C) CC(C)
+CCC [N+](C)(C)C
+CC3 (O)
+CC1=CC(=O)N (N
+CC1(C)CC(CC(C)(C)N1 )NC(=O)
+CC(NC(=O) C1
+CC(C)Oc1ccccc1 N2CCN(C
+CC(C)OC(=O) [C@H](C)N
+CC [n+]2
+CC OC(
+C=C 2C(=O)N
+C3 )C
+C1=NC(=O)N(C =C1)
+C(C(=O)N CCS(=O)(=O)N
+C(=O)c4ccccc4C(=O) c3c2
+C(=O)OC c2ccccc2
+C(=CC (=N
+C( NC(=O)N
+C )n3
+=O) n
+=CC(=O) CCC3
+=C3C =CC=CN3
+=C(C 1=O)
+5CCC (F)(F)CC
+5CC CO
+4CCCC 4)C(=O)N
+4C 5CC
+4)N (C)C
+3c4ccccc4 S
+3)c4 ccnc(
+3) [C@H](C)C
+2CCOCC 2)C3
+2CCC(CC2)N3C c4ccccc4NC3=O)
+2)c4 cccc(c4
+2)N3 C
+2 =
+1) \C=C\
+)ccc1 2)C(=O)O
+)c6 cccnc6
+)c4nc5ccccc5 [nH]4
+)c4ccccc4 )c5
+)c3n ccc(
+)c3 cccc(C)c3
+)c2 cc(ccc2
+)c2 )C(=O)N
+)c(s 1)c3
+)S(=O)(=O)[O-] .C
+)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CCSC) NC(=O)[C@H](CO)NC(=O)[C@H](C
+)NC(=O)C (CC
+)C(=O)N[C@@H](Cc5ccccc5 )C(=O)N
+)C(=O)N[C@@H](C c2cc(F)cc(F)c2)[C@H](O)
+)C(=O)NC (C)C
+)C(=O) c5ccccc5
+(O) (O)
+(F) F)c
+(F) C(=O)
+(CN C2=O)
+(CC(=O)N 2[C@@H]3C[C@@H]3C
+(C1 )NC(=O)
+(C #N)
+%20 =O)N
+n7cnc8c(N)ncn c78
+n4 )cc1
+n1 nn
+cocc 7
+cnc%10 c(N)ncnc9%10
+ccnn 4C
+cccs 5
+ccc(C [C@H](NC(=O)
+ccc n1)N
+c7 c8
+c6ccc(cc6 )c7
+c5csc(N)n 5
+c4ccccc4) CCC
+c4ccc(F)cc4 )cc3
+c4cc5ccccc5 nc34)C2=O
+c3n2 )C(=O)N
+c3n cc(Cl)c(
+c3cn(nn 3)
+c3cccnc3 )c4
+c3cccc(c3 )c4ccccc4
+c3ccc(F)cc2 3
+c3ccc(Br)cc 3)
+c3cc[nH] n3
+c3c(n2 )c4cnc(N)nc4
+c3c(cn n3C
+c3c(OC )c(OC)c(OC
+c2nc3N(CC c3c(n2)c4cnc(N)nc4
+c2cn (C(=O)N
+c2cccs 2)C
+c2ccccn2 )
+c2ccc(cc2 )C(C)C
+c2ccc(I )cc2
+c2cc [nH]c2C1=O)
+c2c([nH] c3ccccc2
+c2c(F)c(F) cc(F)c2
+c1nc2 cc(Cl)c(Cl)cc2
+c1ccc(Cl)cc1 Cl)C(=O)N
+c1ccc(Cl )s
+c1 ccc(C)nc1
+c1 NC(=O)
+\C=N \N=C(N)N
+[P@](=O) (OC
+[C@]5(C)[C@H](CC [C@]45O)C
+[C@]4 7
+[C@H]8 C(=O)N
+[C@H]5 O[C@H]([C@H](O)
+[C@H]4 [C@@]3(C)CC
+[C@H]4 [C@@](C)(CCC
+[C@H]2 \C=C\
+[C@H]1CC[C@H]2 [C@@H]3C
+[C@H](C2) OC(=O)
+[C@H](C [C@@H](O)
+[C@@]3(C)CC [C@@H]2
+[C@@H]4 [C@@H](C
+[C@@H]3CC [C@@H](C
+[C@@H]3 CCN
+[C@@H](O) C(=O)
+[B -](F)(F)
+S2 )c1
+Oc4 c5
+O[C@H] 5CC
+O[C@@H]4O [C@]5(C)CC
+OP(=O)(O) [O-]
+OCCC NC(=O)
+OC3C 4CC5CC(CC3C5)C4
+OC2=C (Cl)C=NN(C
+OC1 CCN(CC1)c2
+OC(=O)[C@H](C c1c[nH]c2ccccc12)NC(=O)
+OC(=O) c1cccc(c1)S(=O)(=O)N
+OC(=O) [C@@H](CC
+O=C(NC1CCCCC1 )N
+O)c(O) c3
+N[C@@H](C c1ccc(O)cc1
+NS(=O)(=O) c1ccc(CN
+NC(=O)c2 [nH]cnc2
+NC(=O)[C@H](NC(=O) [C@@H]3
+NC(=O)COC(=O) C
+N(C)C(=O) NC(=O)
+N(C)C 2=O
+N(C) P(=S)(O
+Fc1ccc(cc1 F)
+Fc1cc( F)c(
+Cl c1ccc(cc1)N2C(=O)
+Cl )cc4
+Cc2 oc(cc2
+Cc1noc( n1)
+Cc1cc( O
+Cc1c(O) ccc2
+Cc1 nc(C(=O)N
+Cc1 cc(Cl)c(
+C[C@H]1 C2C
+COc1ccc(cc1)c2 csc(N
+COc1cc2 OC(=O)C=C
+COC(=O)C (NC(=O)
+CO c2cc3
+CNC (C)(C)C
+CN1CCC[C@H]1 CO
+CN1 C4=C(
+CN(C)Cc5 c(Cl)cc(Cl)cc4
+CN(C)C(=O) C1
+CCc1 cc(C(=O)N
+CCOC(=O) CC1
+CCN(CC1)C2 =C(N
+CCCCCCCC C1
+CCCC c4c3
+CCCC NC(=O)[C@H](C)C
+CCC1(O) CC(=O)OCC2=C1C=C3N(C
+CCC (NC(=O)N
+CC2 CCC1
+CC1(C)CCC [C@@]2(C)[C@H]1CC
+CC(N 1CCN(CC1)c2
+CC(C 1
+CC(=O)c1cccc( NC(=O)N
+C=C(C(=O) Oc2c1
+C3CCN(CC 3)C(=O)C
+C2=C )
+C) S(=O)(=O)C
+C(F)(F)F)cc 2
+C(=S )NC(=O)C
+C(=O)N[C@@H](CC(C)C)C(=O)N CC(=O)N
+C(=O)N(CC CCN
+C(=CC(=O) Oc4c3
+C \N=C\1/
+C \C=C/1\
+=CC(=[N+](CC)CC )C=C3
+=C1 C
+8C )cn7C
+7)C 8
+5CCC(CC 5
+4 CN
+3CCOCC3 )c2c1
+3CCC4(CC3)CC c5ccccc45)
+2CCC[C@H]2 C(=O)O
+2CC SCC2
+2)c3cccc( O)c3
+2)[N+](=O)[O-] )cc1
+2)N (C)C
+1CCN(C c2ccccc2)CC1
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+)C(=O)N[C@@H](CC(C)C )C(=O)N[C@@H](CC(=O)O)
+)C(=O)N[C@@H](C(C)C )C(=O)N[C@@H](CC(C)C)C(=O)N
+)C(=O) c4cs
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+(O)C(=O) \C(=C/
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+(CC(=O) C
+(C)C )n2
+(C n2ccnc2
+# Cc1ccccc1
+o c(C)c(CN
+nn1 C
+n4 cccn4
+n3) ccc2F
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+c5 cc(C)
+c4nn [nH]n4
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+c4 C3(C)C
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+\ c2c1
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+[N+] (C)(C
+[Cl-]. COc1ccc2cc3c4cc5OCOc5cc4CC[n+]3cc2c1
+[C@]2(C)CC [C@]3(C)C(=CC
+[C@]2 (CO)CC
+[C@H]3C [C@H](O)[C@@H](CO)
+[C@H]1CC[C@]2(C) [C@@H]1[C@H](O)C
+[C@H](O) C[C@H]1
+[C@H](CN (C
+[C@@H]4 [C@H](
+[C@@H]3CCCN3 S(=O)(=O)
+[C@@H]2N 4C(=O)
+[C@@H]1 CC(=O)
+[C@@H]1 3)
+[C@@H](O)[C@H](O) [C@H]1CO
+[C@@H](O) [C@H]1O
+[C@@H](N) [C@H]1
+S(=O)(=O) CC4
+Oc5 cc(O)ccc3
+Oc3ccc(NC(=O)Nc4ccc(cc4)C(F)(F)F)cc3 C1=O
+O[C@@H]2[C@]3(CCN 5CC8CC8)c67
+O[C@@H]([C@@H](O)[C@H](O [C@@H]7
+OP(=O)(O)OC[C@H]6O[C@H](C[C@@H]6 OP(=O)(O)OC
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+OC c2ccccc2)c3
+O)N (C)C
+O)C(=O) \C=C\
+N4 C(=O)C
+N1 c4ccccc4
+N c3ncc4
+Cn1 nc2
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+Clc1cccc(c1 )N2
+Clc1ccc(C S
+Cl )c4ccccc4
+Cc2 ccc(CN
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+Cc1n ccc2
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+Cc1ccc( NC(=O)CN2C(=O)
+Cc1cc( nc(
+Cc1cc( [nH]n1)C(=O)N
+Cc1 c(C(=O)N
+C\C(=N\ NC(=S)N
+C[C@@H](O [C@@H]%10
+CS c2oc(nn2)c3
+COc1ccc2 OC(=O)C(=C
+COc1ccc(cc1)C2 =C(
+COc1cc2 N=C
+COc1cc(CN (C
+COc1 cccnc1
+COC(=O)N c1ccc2c(
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+CNC(=O) c1cccc(
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+CN1C2 N(CC
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+CN(C)CC (O)
+CN 3CCCCC3
+CC[C@H](C)[C@H](NC(=O)[C@@H](NC(=O)[C@H](CC (=O)O)NC(=O)[C@H](CC
+CCN)NC(=O) [C@@H](N)C
+CCN(C1)c2 ncnc3[nH]ccc2
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+CCCN 4CCOCC4
+CCCCO c1ccc(cc1)S(=O)(=O)N
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+CCCC CCN1C(=O)
+CCC(CC)O[C@@H]1 C=C(C
+CCC(=O)N1 N=C(CC1
+CCC(=O)N c1ccc2
+CC3 =C2
+CC1 (CN
+CC(C)C[C@H](NC(=O) [C@@H](NC(=O)[C@@H](N)C
+CC(C) OP(=O)(OC
+CC(C) CCn1
+CC(C) (C)c3
+CC(=O)N 2CC
+CC S(=O)(=O)C
+C3CC S(=O)(=O)CC
+C2CCc3c(C2) sc4
+C2=C (O
+C1=O) C(=O)N[C@@H](C
+C1 CCCCC1
+C1 )C
+C) s2
+C(=O)c4 c(O)c3
+C(=O)O[C@H]1C[C@@]2(O) [C@@H](OC(=O)c3ccccc3)
+C(=O)NC(=O) c6
+C(=O)NC(=N c12)c3
+=C 3
+5CCN(CC5 )C(=O)C
+5CCCC5 =O)
+5CCCC CCC5
+5CCCC 5)
+4CCC (C)(C)C
+3CCN(CC N
+3CCC[C@H]3 C(=O)O
+3CCCCC3 )c4ccccc4
+3)c4ccc( Br)cc4
+3)C 5CC5
+3 =CC(=O)
+2CCCC (CO)
+2CCC [C@@H](C
+2CC S(=O)(=O)C2
+2)c3 ncn
+2 (C
+1)C (=N
+)nc2 3)\N1
+)n5 cn
+)n( n1)c3
+)cccc3 4
+)c4cccc(c4 )c5
+)c3 c1CC
+)c3 c(O)
+)c2ccc( OCC(=O)N
+)\C2=O) c1
+)CCC C
+)C3 CCNCC3
+)C(F)(F)F )c4ccccc4
+)C(=O)N[C@@H](CO)C(=O)N[C@@H](C c3ccc(O)cc3
+)C(=O)N[C@@H](CCC(=O)O) C(=O)N[C@@H](CCC(=O)N
+)C(=O)C=C(NC(=O) \C(=C\C=C/[C@H](OC)[C@@H](OC(=O)N)\C(=C\[C@H](C)[C@H]1O)\C)\C)C2=O
+) [C@H]2CC
+) CCN(C
+) Br
+(CN 3CCC
+(CCC n3ccnc3
+(CC1 )n2cc(C(=O)N)c(N
+(CC(=O) OCC
+(C)C3=CC [C@H]4C(C)(C)
+(C)C)NC(=O)[C@@H](C c2ccccc2)NC(=O)[C@H](CC
+(C)C)C (C)C)C(=O)N
+(C)C) ccc1
+(C) CCCN(C1)C(=O)
+(C(=O) OC)
+( c4ccc(Cl)cc4)c5
+sccc1 2)C(=O)N
+nnn1c2nonc2 N)C(=O)N\N=C\
+nn nn3
+nn c(C(=O)N
+ncccc3 4
+nc(o c2
+cs 1)C(=O)N
+cnc( c5
+cn cc1
+ccnc(N)n 3
+ccnc(C )c5
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+ccc[n+] (CC(=O)N
+ccc9 c8
+cc[nH] n3
+cc2 c3ccccc3
+c9ccc(O)cc9 )C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](C
+c7 cc(ccc7
+c7 c6
+c6cc7 ccccc7
+c6 cc(ccc5
+c5ccc(cc5 )C(F)(F)F)
+c5c[nH]c6ccccc56 )NC(=O)
+c4ccccc4 )n1
+c3cccs 3)c4ccccc4
+c3ccccc3N 2
+c3ccccc3C(=O)N 2
+c3ccccc3)c4 ccc(Cl)cc4
+c3ccc(O) c(CN
+c3ccc(C O)cc3
+c3ccc 4CCC(=O)N
+c3cc( sc2
+c2s nc(C(=O)N
+c2cn n(
+c2ccncc2 )c1
+c2ccccc2 N
+c2ccccc2 )\N
+c2cccc3 nonc2
+c2ccc3nc( ccc3c2
+c2ccc3ccccc3n2 )cc1
+c2ccc(cc2 )S(=O)(=O)C
+c2ccc(cc1 2)n3
+c2ccc(cc1 2)[N+](=O)[O-]
+c2ccc(CC(C)NC(=O)C )cc2
+c2ccc( F
+c2cc3 cc(ccc3
+c2cc(C Oc3
+c2cc(Br )cc(c1
+c2c1 ccc3
+c2c(cn n2C
+c2c( F)c(
+c2C )c1
+c1ccccc1 )C(=O)OC
+c1cccc(c1 )N(C
+c1ccc(cc1)c2ccc(cc2)C(F)(F)F)C(=O) CN3
+c1ccc(cc1) [C@H]2C
+c(C) cc4
+c( cc2c1
+c%16 %17
+\C=C/2 \C(=C
+\C(=C(\ N
+[nH]c(cc2c1 )C(=O)
+[nH] n2
+[nH] 6
+[O-][n+]1 nc2
+[C@H]9O[C@H](C [C@@H]9
+[C@H]2C S
+[C@H]1CC [C@@]2(O)
+[C@H](OC(=O)c4ccccc4)[C@]5(O)C[C@H](OC(=O) [C@H](O)[C@@H](NC(=O)OC(C)(C)C
+[C@H](C)CC [C@@H]([C@H]1C)[C@@]24OO
+[C@H] (Cl)C
+[C@@]5 (O)CC
+[C@@H]5 C[C@H](O)
+[C@@H]3 NC(=O)N
+[C@@H](OC(=O)C )C(=O)
+[C@@H](NC(=O)[C@H](C) NC
+[C@@H](NC(=O) OCc3ccccc3
+SC(=O)N C2=O)cc1
+S c3ccccc3C2=O
+S C(=
+O[C@H]1 O[C@H](CO)[C@H](O)[C@H](O)[C@H]1O)
+O[C@@H](C)[C@H](O)[C@@H](O) [C@H]8
+OCCO 4
+OCCC1 CN(C
+OCC2 CO
+OC [C@@H]1C
+O) S2(=O)=O)c1
+O) CC4
+O N(CC
+Nc2n cc(Cl)c(N
+Nc1ncnc2 c1ncn2C
+NC(=O) CN1
+NC(=N)N S(=O)(=O)
+N2CCN(CC 2)S(=O)(=O)
+N(C c4c(F)cccc4
+FC(F)(F)C1 =C
+F) n2
+Clc1ccccc1 NC(=O)N
+Clc1ccc( s1)C(=O)N
+Cl)c3ncccc3 F)n(
+Cc2 oc(SCC(=O)N
+Cc1ccccc1 Cn2
+Cc1ccc( Nc2nc(N
+Cc1ccc( NC(=O)CN
+Cc1cc(o n1)c2
+Cc1c( sc2
+C[C@]12CCC3C (CCC4
+C[C@@H]1CCC[C@H](N 2CCC(=CC2=O)
+CSCC[C@H](NC(=O)C NC(=O)
+CS(=O)(=O) O
+CS c1cccc(N
+CS \C(=N/
+COc1cccc2C(=O)c3c(O)c4C[C@](O)(C[C@H](O[C@H]5C[C@H](N )[C@H](O)[C@H](C)O5)c4c(O)c3C(=O)c1
+COc1ccc(cc1 [N+](=O)[O-]
+COc1cc(OC)c( \C=C\C(=O)
+COc1cc( O)ccc1
+COc1cc( O)cc2
+COCCN1C(=O) C(=N
+CN1CC [C@]2
+CN(C)C(=O) Oc1ccc2
+CC[C@H](C)[C@H](NC(=O)[C@H](C c1ccc(O)cc1)NC(=O)[C@H](C
+CCS(=O)(=O) c1ccc(CC(=O)N
+CCOc1ccc2 ccccc2c1
+CCOC(=O) c1n
+CCN1 CCC(C
+CCCNCCCCN CCCN
+CCCCC1 (CCCC)C
+CC1CN(CC(C)O1 )S(=O)(=O)
+CC1(C) NC(=N
+CC1 .OC(=O)C(F)(F)F
+CC(C)C[C@H](NC(=O) [C@@H](N)C
+CC(C)(C)CC [C@@]5(CC[C@@]34C
+CC(C) CCN(C
+C5 CCN(CC5
+C4CCN(C c5ccccc5)CC4
+C3 CCC3
+C1 2
+C(F)(F) F)c1
+C(CC 1
+C(=O)OC .OC(=O)C(F)(F)F
+C(=O)NCCC n4
+C(=O)NC c3ccccc3
+C(=O)N1 C
+C(=O)C (CN
+C(=N CC
+C(=C (OC
+=C4C[C@H](CC [C@]34C)
+=C(C OC2=O)
+8 cnc9
+5)N6CCOCC 6
+4CCN(CC4)c5 cccc(c5
+4CCC 4)
+4CC7CC7 )c56
+4)n 6
+4 )S(=O)(=O)C
+3)n (
+3)C1 )C2
+3)C(=O)NC c4ccccc4
+2CCN(CC2)S(=O)(=O) c3ccccc3
+2CCC(CC2)c3 no
+2)c4 ccc5
+2)c3 4
+2)C (=S)N
+2 [C@H](CC
+1CCOCC 1)C(=O)
+1CCC(= CC1)c2
+1) NC(=O)N
+/C(C)C(O)C(C)C(O)C(C)C(OC(=O)C)C1 C)\C
+)n ccc2
+)cc4 )cc3
+)c5 ccc(C)cc5
+)c5 cc(OC)c(
+)c4ccc(F)cc4 F
+)c4 ccc(Cl)c(Cl)c4
+)c3ccc(cc3 )C(=O)NC
+)c2 cc(N
+)c(Cl )cc2Cl
+)c(C)n2C(=O) c4ccc(Cl)cc4
+)\C(=O) c2c1
+)\ N(C
+)S(=O)(=O) NC(=O)N
+)NC(=O)[C@@H](NC(=O)[C@@H](CO)NC(=O)CNC(=O)[C@H](CC(=O)O)NC(=O)[C@@H](C) NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC
+)CC2 )C1=O
+)C3 CN(C
+)C(F)(F) P(=O)(O)O
+)C(=O) C(=C2C(=O)
+)C [C@@H]2C
+(c1ccccc1) (c2ccccc2)c3ccccc3
+(O)C2 =C(
+(CCCC) (CCCC
+(CC O)C2
+(C)CC 4
+(C)C)N C
+(C )c3ccccc3
+(=C)C )C(=O)
+o cc(C
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+ncc2ccccc1 2)N(C
+ncc1 C(=O)NC[C@@H](F)C(C)(C)O)
+n3) ccc2
+n2c(C) ncc2
+n2 3)cc1
+cnc([nH] 5)
+cnc(Oc3ccc4 OC(CC
+cnc( c2
+cn c%13
+ccccn 7
+cccc(OC )c3
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+cc(C)cc(C )c3
+cc(C#N )c(nc1
+c6c[nH]c7ccccc67)NC(=O)[C@H](CCCCN)NC(=O)[C@H](C c8cnc[nH]8
+c5 ncc(
+c4ccccc4)c( n3
+c4ccccc2 4)n
+c4ccc(N )nc4
+c4cc(cc(c4 )C(F)(F)F)C(F)(F)F
+c4c( nn(
+c3oc4ccccc4 n3
+c3ncn s3
+c3nc4ccccc4 nc13
+c3coc( n3)c4
+c3ccn (
+c3ccccn3 )c2
+c3ccccc3)c4 cccc(
+c3ccccc3)C(=O)N 1
+c3ccc(C)c(c3 )C#C
+c3c4CCCCc4n c5ccccc35
+c3c([nH] c4cc(ccc34)C#N
+c3c(O) c4
+c3c( c2
+c2cn(C(=O)N )c3ccccc2
+c2ccccc2 CO
+c2ccccc1 2)\
+c2ccc3ccccc3c2 )c1
+c2ccc3ccccc3 n2)c1
+c2ccc(cc2 C(=O)N
+c2ccc(NC(=O)C )cc2)
+c2ccc(F)cc2) CC1
+c2ccc( OS(=O)(=O)
+c1ccccc1)N2 C(=S)S\C(=C/
+c1ccc2c(c1 )[nH]
+c1ccc(cc1 )S(=O)(=O)N
+c1ccc(N C(=S)NC(=O)
+c(Cl )c5
+c(C) nc1
+\N=C(/ C)\C
+\C=C\ c5ccccc5
+\C=C \C=C(/C)\C
+\C(=N\ NC(=O)C
+[nH] ccc7
+[OH-]. [OH-].
+[N+](=O)[O-])[N+](=O)[O-] )\[O-]
+[C@H]3C 4
+[C@H]2[C@@H]3CC [C@H]4C
+[C@H]1CC[C@H]2[C@@H]3[C@@H](C [C@@H]4C
+[C@@H]4 [C@@H](O)C
+[C@@H]3 OC
+[C@@H]2CC [C@H]3
+[C@@H]2 [C@@H](O)[C@H](O)
+[C -]
+SCC #N
+S c2nc(N
+P(=O)(O)[O-] )cc
+Oc4 ccccc14
+Oc2ccc(cc2C#N)S(=O)(=O)N c3ccc(F)c
+Oc1ccc( NC(=O)N
+O[C@@H]%10 O[C@H](CO)[C@@H](O)[C@H](O)[C@H]%10
+OCCCO c2ccc3
+OCC CN(C)C
+OC(COC(=O)C )C(OC(=O)C)C(OC(=O)C
+OC(=O)CC [C@H](NC(=O)N
+OC(=O) c1ccc2c(c1)nc(N
+OC(=O) c1ccc(O
+O=C1 N(C(=O)
+O=C1 CO
+O=C(C [n+]1
+NS(=O)(=O)c1ccc( CC
+NS(=O)(=O) c2ccccc2
+NC(=O)c2cc3 ccc(cc3
+NC(=O)[C@H](C) NC(=O)[C@H](C)
+NC(=O)CSC2=Nc3ccccc3 C4=N
+NC(=O)C(N (C
+N2 C(=N)N
+N c3cccc4
+Fc1ccc( \C=C\C(=O)N
+Fc1cc( cc(c1
+FC(F) Oc1ccccc1
+F)cc 4
+F)c2 ccc3
+Cn1c2CC 3CCC(N3)c2
+Cl. COc1cccc2
+Cl CC(=O)N
+Cc1n c(CC
+Cc1cccc2 ccccc1
+Cc1ccc2 [nH]c(nc2c1
+Cc1cc(ccn1)c2n[nH]c3cc( NC(=O)NC
+Cc1cc(C)n( n1)C(=O)
+C[C@@H](N (C
+COc1ccccc1 N2C(=O)
+COc1cccc( Nc2n
+COc1ccc(C n2cnc3
+COc1ccc(C [C@H](NC(=O)
+COc1ccc( c(F)c1)c2
+COc1cc( \C=N\NC(=O)c2
+COC c1n
+COC (C)
+CNC(=O)[C@@]12C[C@@H]1[C@H]([C@H](O)[C@@H]2O)n3cnc4 c(NC
+CNC(=O) [C@@H](C
+CNC(=N)N)NC(=O)C NC(=O)[C@@H](NC(=O)[C@H](CC
+CN1 [C@H](CC
+CN(C)CC [C@H](CSc1ccccc1)N
+CN [C@H](C(=O)N[C@H](C(=O)N
+CN 4CC
+CCOC(=O)C1=C(C) NC(=S)NC1
+CCN c1cc(cc(c1)C(=O)N[C@@H](Cc2ccccc2)
+CCCO c2ccc3
+CCCCCC 1
+CCCC COc1
+CCC2(CC 1)O
+CCC2 CCN(C
+CCC (C)C1
+CCC (=C)C(=O)
+CC1CC (=NO
+CC1=CN([C@H]2C[C@H](OP(=O)(O)OC[C@H]3O[C@H](C [C@@H]3OP(=O)(O)OC
+CC1(C)O c2cc3
+CC(=O)Nc1n c(C)c(s
+CC(=O)N C(
+CC(=O) c1cc2
+CC [N+](C)(C)C
+C5CCCCC 5
+C2CCCC CCC2
+C2CCC c3c2cnn3
+C1 2CCN(C
+C(=O)OC (C)(C)
+C(=O)N(C c3ccccc3)C2=O)
+C(=NC2 (C)C)N)N
+C( Oc1ccc2
+C (O)C
+=CC=CC 3=O)
+=CC(=O)C=CC4 (C)[C@@]3
+7CCC 7
+7)c8 ccccc8
+5CCCC5 )c4
+4)c5 ccccc35
+4 )c(OC)c1
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+) \C(=N\O)\N
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+=CC=C N2C1=O)
+=C3 N(CC
+=C1 CCCC
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+4 CCCN(C
+3N=C(S C3=N
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+. OP(=O)(O)O
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+) ccc(OC
+) [C@]5(O)C[C@H](OC(=O)
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+(= C(C1
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+[C@@H]2C [C@H](N(C2)C(=O)
+[C@@H]2 OC(=O)C(=C)
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+[C@@H](CCCCCCCCCC C)OC(=O)
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+[C@@H](C) NC[C@H](O)
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+= CCC4
+= CC(C)(C)
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+)[C@H](C) N
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+)C(=O) Oc3c2
+)C(=O) C=C1
+) O)
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+(C) C(=O)O
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+nc(C (=O)O)
+n1)C2 CCCN(C
+n1 cnnc1
+n1 c(C
+n1 4
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+c3c2 )C(=O)C
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+c2ccc(F)cc2 Cl
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+c1csc( n1)c2
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+c1 nc(N)n
+c(ncn c2
+c( n1)N
+c%11 c[nH]
+c 8
+\N=C\3/ C(=O)N
+\C=C\C2=N c3ccccc3C(=O)N2
+\C=C\C1=C (C)CCCC1
+[n+]1 ccccc1
+[Si] (
+[C@]4(C)[C@H](CC [C@H]4[C@@H]3CC
+[C@]3 (O)CC
+[C@]2(O) C(=C(C(=O)N
+[C@H]2C )
+[C@H](NC(=O)[C@H] (
+[C@@]5 (O)C
+[C@@]3 (CO
+[C@@]3 (CCN(C
+[C@@]1 (C)c3
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+[C@@H](O P(=O)(O)O)
+[C@@H](C) [C@H](OC3=O)
+[C@@H]%1 8
+S=C =N
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+OCOc6 c5
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+C[C@@H]3 [C@H](C
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+COc1cc(OC)c(C(=O) \C=C\
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+COc1cc( \C=N\N
+CO\C=C(\C(=O)OC)/ c1ccccc1
+COP(=O)(O)O[C@H] %10
+COC(=O)C (C)N
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+CN CC(O)
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+CCOc1ccccc1 C2
+CCOC(=O) CSC1=N
+CCCN(CC(=O)N c1ccccc1
+CCCC[C@]1(CC)C S(=O)(=O)
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+CCCC(C) COc1ccc(cc1)
+CCCC (C)C
+CCC c2
+CC2 (F)
+CC1(C)CC 2=C(C
+CC1 =CC(=N
+CC(C)C (C
+CC(C)(C)NC(=O) [C@@H]1C[C@@H]2CCCC[C@@H]2
+CC(C)(C)N([O] )C(C)(C)C
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+CC(=O)N (O)
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+CC c1ccc(O
+CC #CC
+C=CC(C)(C) Oc3
+C4=C (CCCC
+C3=O) C(=O)
+C3 (C)C
+C2CC =CCC2
+C2=C (CCC2
+C2 CC(C
+C1= S
+C(=O)C(=C (O
+=N c2c(O)
+=C2 )C1
+5(CC 5)C#N
+4CCN(CC4)c5 ccc(F)cc5
+4 )c(C)c1
+3CCC4(CC3)N (CNC4=O)
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+2)C (C)(C)
+1CCC(= CC1
+1)c2 cccs2
+1)c2 cccnc2
+)ncn 3C
+)n2C )cc1
+)n( c1C
+)cc2 )c4ccccc4
+)c3ccc(N )cc3
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+)NC(=O)[C@H] (
+)C3=CC(=O)C(= CC=C3
+)C2CC 2
+)C1=O) C(=O)O
+)C(=O)N[C@@H](C c7ccccc7
+)C(=O)N[C@@H](C O)C(=O)N
+)C(=O) OCCN
+)C(= CC1=O)
+) [C@H](O)C
+(\ N=N\
+(CCC c2ccccc2)
+(C)C )C2=O)
+(=O)O) NC(=O)[C@H](CCC(=O)N
+(/ C/
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+ccncc3 )c1
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+ccc(C(=O) C
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+c2ccccc2)c3ccccc3 n1
+c2ccccc2)C =O
+c2ccc3 nc(s
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+c2ccc(cc2)N 3CCOCC3
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+c1 2)c3ccccc3
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+c(ccn 4C
+c(N)ncnc3 n2
+c(Cl)cccc4 Cl
+c(C) n(C
+c(C(=O) OCC(=O)N
+c%14 %1
+\N=C(\N)/NCCC [C@@H](NC(=O)C
+\C(=N/ OC)\
+[Zn +2].
+[C@H]3 [C@@H](C)OC(=O)[C@H](CC(=O)
+[C@H]3[C@@H](C)OC(=O)[C@H](CC(=O) c4ccccc4N
+[C@H]2 CCCN2C(=O)
+[C@H]2 4
+[C@H]([C@H](c2cccc(Cl)c2 F)
+[C@H](CN CC3=O)
+[C@@](C)(C=C)[C@@H](O) [C@H](C)
+[C@@H]5C[C@]5(NC4=O)C(=O)NS(=O)(=O)C 6CC6
+[C@@H]4CCCN C4
+[C@@H]2C[C@]2(NC(=O)[C@@H]3C[C@H](CN 3C1=O)
+[C@@H]2C[C@H]1 CN2
+[C@@H](N)C c3ccc(O)cc3
+[C@@H] %30
+SC (
+Oc1ccc( OC
+O[C@H](CO)[C@@H](O [C@@H]8
+O[C@H](CN)[C@@H](O)[C@H](O) [C@H]4N
+O[C@@H]2 O
+OC(C)(C) O[C@@H]3
+O=C1N(C (=N
+O=C1N C(=S)S
+O=C(CN1 CCCC1)N
+O)cc1 3
+O) n2
+NC[C@@H]1O [C@H](O[C@H]2
+NC(=O)C1 CCN(C
+NC [C@@H]1
+N1 C=C(C(=O)O)C(=O)
+N(CCC c4c3
+Fc1ccc( O
+Fc1ccc( CCN
+Fc1 cccc(CC
+FC(F)(F) Oc1ccc(cc1)c2
+F)c n3
+Cl)c3ncccc3F)n( n1)c4ccccc4
+Cl )S(=O)(=O)N
+Cl )N2
+Cc1nn sc1
+Cc1cccc( NC(=O)C
+Cc1ccc( SC
+Cc1cc( nc(N
+Cc1 [nH]c2
+C[C@]12C c3cnn(c3C=C
+C[C@@H]1CC[C@@H](Cn2c(nc3cc(nc(c4cncc(Cl)c4)c23)C5 =NOC(=O)N
+COc1ccc(cc1)n2 ncc3
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+COc1ccc(\C=C/c2cc(OC)c(OC)c(OC)c2 )cc1
+COc1cc2c( o
+COc1cc(OC)c( \C=C\
+COc1cc(O)c2 C(=O)O
+COc1cc( cc(O)c1
+COP(=O) ([O-])
+COCCN =C(S)N
+COC(=O)[C@@H]1 C2CCC(C
+CNC(=N)N CCC[C@H](NC(=O)[C@H](CC
+CCc1nc2 c(OC
+CCc1cc( cc(C)c1
+CCS c1nc(
+CCOC(=O)[C@H]1 [C@H]2CO
+CCOC(=O)N c1cc2
+CCN1C =N
+CCN(CCc2 3)c4ncccc4
+CCCN c1nc(N
+CCCN C2
+CCCCCCC(=O)N O
+CC1CCCN(C 1)S(=O)(=O)
+CC1=CC (=C
+CC1=C (O)
+CC1 C(N
+CC(N )C(=O)O
+CC(C)C[C@H](NC(=O)[C@H](C c1ccccc1)NC(=O)C
+CC(C)C c1nnc(
+CC(C) (O)CC
+CC(=O)N CC1
+CC(=O) NC(=O)N
+C4=C (CCC4
+C4 =CC(=O)C=C
+C3CCCO 3
+C3CC 4CCC(C
+C3C c4ccccc4C3
+C3 )c1
+C2=O) S(=O)(=O)
+C2 3
+C) /
+C(F)(F) F)c(
+C(=O) c6ccccc6
+C(=N )C
+C( Oc1c2
+C#N )C2=N
+Brc1cc( Br)c2
+Br c1c(
+=O) cc3
+5 C(=O)OC
+4CCN(CC (=O)N
+4CCN(C c5ccccc5
+4 5
+3c4ccccc4 C2
+3CC c4
+3)n c12
+2CCOCC 2)n3
+2CCOCC 2)S(=O)(=O)
+2CCC (C)
+2CC 1
+2C c3cc4
+2)C(=O) c3ccccc3
+2) OC(=O)
+2 3
+/C/4 =N\
+)n2 nc1
+)cc2 .OC(=O)C(F)(F)F
+)cc2 )C1
+)c5 ccccc35
+)c(c3 )C#N
+)c(O) ccc3
+)c(C 3CCCCC3
+)c(C )c23
+)c [nH]c12
+)[N+](=O)[O-] )\O
+)NC(=O)[C@H](CO) NC(=O)CN
+)C=C NC3=O)
+)C1 =O.OC(=O)C(F)(F)F
+)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](C)C(=O)N[C@@H](CCCCN )C(=O)N[C@@H](CO)C(=O)O
+)C(=O) CN(C
+) CCN(CC
+(F)F) C(=O)
+(C2CCS(=O)(=O)C2 )C(=O)
+(C)CC 5
+(=C (N)
+%31 C=C(C)C(=O)NC%3
+%21 C=C(C)C(=O)NC%2
+sc3CCCC c3c12)c4
+nc3 c(nn
+nc1 2)
+cs c3ncnc(N
+cn3 )C(F)(F)F
+cn c%14
+cccc(c2 )c3
+cccc n2)N
+ccc2 C1=O
+ccc(Cl )c(c1)C(=O)N
+ccc( O)c4
+c5ccc( OCCN
+c4cn cc(
+c4ccc(Cl)cc4)NC(=O)[C@@H](C c5ccc6ccccc6c5)NC(=O)C
+c4 s3
+c3csc(N)n 3
+c3cnc[nH] 3)NC(=O)
+c3ccccc3 O2
+c3ccccc3 C(=O)c4
+c3ccccc3 )c4ccc(cc4
+c3ccccc3 )C1
+c3ccccc1 3)c4
+c3ccc(OC)cc3 OC
+c3ccc(F)cc3 )cc2
+c3ccc(C)cc3 )c4
+c3ccc( nn
+c3cc4 cc(Cl)ccc4
+c3cc(ccc2 3)c4
+c3c(F) ccc(Cl)c3
+c3c(C) [nH]
+c3 n2)N
+c3 ccc(Cl)c(F)c3
+c2o c(C)
+c2cn cc3
+c2ccccc2CC c3ccccc13
+c2ccccc1 2)c3ccc(cc3
+c2cccc3c2 [C@H]1C
+c2ccc3 N(CCC
+c2ccc(OC )cc12
+c2cc(C) cc(C)c2
+c2c1cn cc2
+c2c1 )C(=O)N
+c2c(C1)s c3ncnc(N
+c2c(C)onc2 C
+c1ncnc2 [nH]cnc1
+c1ccc( F)c(c1
+c1c( n
+c%12 %1
+\C=C\2/SC(=S)N (C2=O)
+\C=C(/C) \C=C\C=C(/C)
+\C(=C\ c2ccccc2
+[nH]c(n1)C2 CN(CCO2)C(=O)
+[nH] ccc9c8
+[n+]2 ccc(\C=N\
+[O-][N+](=O) c1cnc(
+[Na+]. CC(C)
+[NH2+] )N
+[N+]6 7CC
+[N+](CC) (CC)CC
+[C@]3(C 1)
+[C@](O)( [C@H](O)
+[C@H](OC c3ccccc3)
+[C@H](CC N1
+[C@H](C)[C@@H](O) [C@@H](C)
+[C@@H]5CC[C@@]34C)[C@@H]2C1 )C(=O)O
+[C@@H]4CC[C@@H](C) [C@H]4CC
+[C@@H]4 c5ccccc5
+[C@@H]3C [C@H](C
+[C@@H]3C =C(C
+[C@@H]1CC[C@@H](CCN2CCC(CC2)c3 cccc4
+[C@@H](OC(=O)C (C)C)
+[C@@H](C)C(=O)N[C@@H](CC CN=C(N)N)C(=O)N
+[C@@H]( [C@H]1C
+[C@@H]( [C@@H](N
+Oc3 cccc(C)c3
+Oc1ccc( NC(=O)
+OC(C n1cncn
+OC(=O)N (C)C
+OC(=O) [C@H]1C
+OC [C@@H]2C
+O=C1N c2ccc(cc2
+O=C(CN 1C=N
+O.O. O.O.
+NS(=O)(=O) Oc1ccc2
+NC(=O)c2cc(o n2)c3
+NC(=O)C2CCN(CC2)S(=O)(=O) c3ccc4
+NC(=O)C )OC
+NC(=O)C (F)(F)F)
+NC(=O) c3ccncc3
+NC c4ccccc4
+N=CC(=C (Cl)C1=O)
+N4 C(=O)N(C(=O)
+N3 CCNCC3
+N(C=C(C(=O)O)C(=O) c5cc4
+N c2ccccc2
+N C4=O
+FC(F)(F) c1cccnc1
+F)c2 n1
+Clc1 cc(Cl)c(cc1
+Cl. CCCN
+Cc1nc( o
+Cc1nc( cc(n1)n2
+Cc1n cs
+Cc1ccc( \C=C\C(=O)N
+Cc1cc(C)n2 nc(nc2
+Cc1cc(C Oc2ccc(cc2)C(=O)N
+Cc1c( ncc2ccccc12)N(C
+C[C@H](CC (=O)
+C[C@@H](O)[C@@H]1[C@H]2 SC(=C(N2C1=O)
+C[C@@H](O [C@H]1
+C[C@@H](CO)N1C[C@H](C)[C@H](CN (C)S(=O)(=O)
+COc1ccccc1 \C=C\2/
+COc1ccc(cc1OC )C
+COP(=O)(OC )C(OC(=O)
+COC(=O)N)C(=O) C(=C(C)C
+COC(=O)C1=CO[C@@H](O[C@@H]2 O[C@H](CO)[C@@H](O)[C@H](O)[C@H]2O)
+COC(=O) C(N
+CO CC(=O)N1CC
+CN[C@H](CC(C)C)C(=O)N[C@@H]1[C@H](O)c2ccc(Oc3cc4cc(Oc5ccc(cc5 Cl)[C@@H](O)[C@@H]6NC(=O)[C@H](NC(=O)[C@@H]4NC(=O)[C@H](CC(=O)N)NC1=O)
+CN1C(=O)N(C)c2c(C1=O) c(
+CN1 C(=C(C
+CN(C)C1 CCN(C
+CN(C)C(=O) [C@H](
+CN( [C@H](C
+CN [C@@H]1
+CC[C@@]1 (O)
+CCOCC OCCOCCOCCOCC
+CCOC(=O)\C=C\[C@H](CC C(=O)N)NC(=O)[C@H](C
+CCOC(=O)C1 CCN(CC1)S(=O)(=O)
+CCOC(=O) c1nc(
+CCN 6CCOCC6
+CCN 3CCOCC3)n
+CCN 3CCCC
+CCCCCCCC CCNC(=O)
+CCCCCCC \C=C/
+CCCCC [C@H]1
+CCC1 =C(N
+CCC(CC )N1
+CC(N 2CCOCC
+CC(C)n1 cc2
+CC(C)C1=CC2CC3(C=O) [C@@H]4CC[C@@H](C)[C@H]4CC
+CC(C)(C)CC 3=O)
+CC(C) (N)C
+CC(=O)N[C@@H](Cc5ccccc5 )C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCCCN)C(=O)N2)NC(=O)[C@H](CSSC[C@H](NC(=O)[C@H](C
+C4 =CC(=NC(=O)N
+C4=CC(=NC(=O)N 4CCc3c2
+C3=N CCCN3
+C2=C(C)N(C)N(C2=O) c3ccccc3
+C2 N=C(c3ccccc3)c4ccccc4
+C2 CC(C)(C)NC(C)(C)C
+C(C)(C) CC
+C(=O)N[C@H]1[C@H]2SCC (=C(N2C1=O)C(=O)O)
+C(=O)N c1ccccc1
+C(=O)N C3=CC(=O)N
+C(=C1 O)
+C n2ccc3
+C c2ccccn2
+C )C(C)C
+Br c1ccc(cc1)S(=O)(=O)
+B(O) O)
+=CC(=O) C=C3
+=CC(=NC(=O)N 3CCc2c1
+8 =C
+6C [C@@H]7
+6C [C@@H](O[C@@H]%1
+4=CC=CC(=O)N 4C3
+4)c5 ccccn5
+3N =CC(=O)NC3=O
+3CCC(CC3)N 4CCCCC4
+3C [C@H]4C
+3)C5 =CC=CC(=O)N
+2CCOCC2 )c1
+2CCOCC 2)ccc1
+2CCCCC2 )NC(=O)[C@H](C
+2CCCC N2C(=O)
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+2CC(C (=N
+2)C#C c3ccccc3
+1=C (O)C(=O)
+1)N2CC(C2) NC(=O)CN
+)nnc1 c3occc3
+)n2 N
+)cnc2 n1
+)cc2 C1C
+)cc2 C(=O)N
+)cc 4C
+)c3cc(cc(c3 )C(F)(F)F)C(F)(F)F
+)c3 ccsc3
+)c(F)cc4C(=O) C(=CN1c2
+)c( OCC(=O)O)
+)c s1
+)\N=C(/ N
+)O[C@H]4 n
+)C3 CCCC
+)C(=O)N[C@@H](CC(C)C)C(=O)N CC(=O)N[C@@H](CCCCN
+)C(=O)N CC(=O)N[C@@H](CCCNC(=N)N
+([O-] )CC
+(NC(=O) CC
+(F)(F)F.F[P-](F)(F)(F)(F)F.F[P-](F)(F) (F)(F)F.F[P-](F)(F)(F)(F)F.F[P-](F)(F)
+(Cl )C2
+(CN 2
+(CCN (C)C)C(=O)
+(CCC3 )c4noc(
+(C)C)C(=O) C(=C(O)
+(C)C )C(F)(F)F
+%21 cnc%2
+%13 cnc%14
+#N )c3
+s c(C)
+ns nc3c2
+nn cc3
+ncn3 c2
+n4cnc5 C(=O)NC(=N
+n2cnc3 c(NC
+n cccc3C
+n c(Cl)n
+cn1 C)S(=O)(=O)N
+cn cc(c3
+ccc6 ccccc56
+ccc(Cl)c(C Oc3cccc4
+cc(OC)c(OC )cc6
+cc(OC c4ccc5ccccc5n4
+cc(NC(=O)c4cnn5 cccnc45)c(n3)c6cc(Cl)ccc6
+c7c[nH]cn 7
+c7 ccc(C)cc
+c5ccccc5 [nH]c2
+c5c[nH] c6ccccc56
+c4n o
+c4ccc5ccccc5c4 )NC(=O)C
+c4ccc(O)cc4 )NC(=O)
+c4cc5ccccc5 cc4
+c4cc5OCOc5cc34 )c2cc1OC
+c4c(C) cccc4C
+c4 c(O)cc(O)cc4
+c3nc( ccc3
+c3cccc( OCC
+c3ccc(Cl)cc3 Cl)
+c3ccc(Cl)cc3 )c2
+c3ccc( Oc4ccc(cc4
+c3cc(OC)c(OC )cc3CC
+c3 CNCC
+c2s c(C(=O)N
+c2nc3c( cccc3
+c2ccn( c2
+c2ccccc2)c3ccccc3 )c4ccccc4
+c2ccccc2 C)
+c2ccc3c(cc( nc3c2
+c2ccc3CCCC c3c2
+c2ccc3 OC(C)(C)C=C
+c2ccc(cc2)C(F)(F)F) OCCCC[C@@H](C)
+c2ccc(cc2)C(=O)Nc3ccccc3N )O
+c2ccc(cc2 )C(=O)OCC
+c2ccc(C)cc2 )S(=O)(=O)
+c2ccc(Br)cc 2)c3
+c2ccc( c(C)c2
+c1ccccc1 )N(C
+c1ccc(cc1)N 2CCN(CC
+c1ccc(cc1)C2CC (=NN2
+c1N )c2
+c(O)cc(O) cc5
+c(O)cc(O) c3
+c(N)n ccn25
+c( F)c
+c%11c[nH] c%12ccccc%11
+c%10 c[nH]
+\C=C\ [C@@H](O)C[C@@H](O)CC(=O)[O-]
+\C=C(\C)/ C(=O)O)
+[nH]ccc9c8 )c4
+[NH4+]. [NH4+].
+[C@]4 (O)C
+[C@](C)(O 4)
+[C@H]3[C@@H]4CC =C5C
+[C@H]3CO 2)C(=O)C
+[C@H]2 CN3CC
+[C@H](N(C)C)C(=C(C(=O)N )C(=O)
+[C@@]3(C[C@H]3C=C )C(=O)NS(=O)(=O)C
+[C@@H]4CC[C@@H](N)[C@H](F)C O4
+[C@@H](O)C (O)
+[C@@H](O) CC2
+[C@@H](NC(=O)[C@H](CC (C)C)NC(=O)
+[C@@H](NC(=O)[C@H](C (C)C)N(C)C(=O)
+[ T
+SC=C N1
+S1(=O)=O) C(=O)O
+S(=O)(=O) c4ccc(C)cc4
+S(=O)(=O) CC2
+Oc1ccc2 C(=O)\C(=C\
+O\N=C/ 1
+O[C@H] %10C
+OCc3cn nn3CCCC1=O
+OC1 (CC(=O)
+OC(=O) \C(=C/
+OC(=O) CC(C
+O c3ccc(Br)cc3
+Nc1 cccc(C
+N[C@@H](C S
+NC(=O)[C@H]2N [C@@H]3CC[C@H]2C3
+NC(=O)N 2CCC
+N=C(S C2=N
+N3C(=O)C=C c4cc(ccc34)S(=O)(=O)Nc5
+N(CC (C)C)C(=O)
+N(C=CC=C3C )C2=O)
+F) n3
+Clc1ccc( CC
+Cc1ccc( O)cc1
+C\C(=C/ c1ccccc1)
+COc1cn cc(c1)c2
+COc1ccc(cc1)N2 CCN(C
+COc1ccc(C n2c(CC
+COc1ccc(C NC(=O)C2
+COc1cc2 CC(CC
+COC c1ccccc1
+CN(CC CCCC
+CN(C)S(=O)(=O) c1ccc(C)c(
+CN( c1ccc(cc1)C(=O)N
+CN( c1ccc(
+CN Cc1cc(
+CCOc1cc2 ncc(C#N)c(N
+CCN(C 1=O)
+CCN( [C@H](C)C
+CCN 3CCN(CC3)c4
+CCCN 2
+CC1 Oc2ccccc2
+CC1 2C
+CC(C)OC(=O)C1=C (C)
+CC(C)(C) N(C
+CC(=O)O[C@@H]1C [C@H]2C(C)(C)
+CC(=O)N[C@@H]1[C@@H](O)C[C@@] (O
+CC( OC(=O)c1
+C=C (C)C(=O)
+C4 (C)C
+C3CCOCC 3
+C3(CCN(C c4ccccc4)CC
+C3 (CCN(CC
+C23CC4CC(CC(C4)C2)C3 )cc1
+C1 C(N(C(=O)C1=O)c2ccc(cc2)c3
+C(F)(F) F)cc3
+C(=O)O) NC(=O)[C@H](C)NC(=O)
+C(=O)N[C@@H](CCCNC(=N)N )C(=O)O
+C(=O)N[C@@H](C (C)C)C(=O)N
+C(=O)N 4C
+C(=O) c2ccccc2
+C(=N c2ccccc2C1=O)
+=CC(=O)C(=CC=C 4
+=C(N 3C(S
+5=C 4C(=O)
+5 )C4=O)
+4CCN(CC 3
+4CC 4)C2
+4C [C@@H](O[C@@H]%14
+4) O
+3CCCC 3)C(=O)C
+3CC S(=O)(=O)C3
+3C c4ccccc4C
+3C [C@H]4
+3)S(=O)(=O)N 4CCCCC4
+3 CC(C)OC(C)C
+3 )C(F)(F)F)C(F)(F)F
+2CCC(C2) Oc3ccc(cc3)[C@H](C)
+2C(=O) C(=O)
+2)c3ccc4 OCCOc4c3
+2)c3ccc(OC )nc3
+2)C3 =NCCN3
+1C c4ccccc4
+1)c2ccc(cc2 )c3ccccc3
+1)c2 ccc(F)cc
+1) S
+)s 2)c1
+)n c6
+)n c1C
+)cc2 n1
+)cc1 N
+)cc cc2c1N
+)c4ccc5 OCOc5c4
+)c4 cc(OC)c(OC)c(OC)c4
+)c3ccc( Oc4ccccc4)cc3
+)c3 ccccn3
+)c2nc3ccccc3 [nH]2
+)c2cc1 OC)C(=O)
+)[C@@H]2 O
+)N2C (SCC2=O)
+)N c3ccccc3
+)N %3
+)C2 (O)
+)C1 CCC2
+)C1 )C(=O)N
+)C(F)(F) F)c(c1
+)C(C)C)C(=O)N[C@@H](C c2c[nH]cn2)C(=O)N
+)C(=O)O) n1
+)C(=O)N[C@@H](CCCCN C(=N)N
+)C 5CC5
+) [C@@H](C)CC
+(Cl )c3ccccc3
+(CCCN 2CCOCC2)C
+(C)c3 c2
+(C)C)C (O)
+s3)c4 nc5
+ncn c(S
+nc2c1 C
+nc(nc4 c5cnc(N)nc5
+nc( ccc1
+n2C )cc1
+cnc%2 9
+cnc%2 7
+cn cnc2
+ccc( cc8
+cc(NC(=O) c8
+cc(NC(=O) \C=C\
+cc( ncc2
+c[n+] (CC(=O)
+c9ccc(O)cc9)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](C c%10ccccc%10)C(=O)N
+c6ccccc6 )C(=O)O
+c5 C
+c4sc5 CC(CC
+c4o c5ccccc5n4
+c4ccccc4)NC(=O)[C@H](C)NC(=O)[C@@H](NC(=O)[C@H](CSSC[C@H](NC(=O)[C@H](Cc5ccccc5 )NC(=O)[C@H](CO)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](Cc6ccc(O)cc6
+c4ccccc4 nc3
+c4ccc(NC(=O)C )cc4
+c4c3 nc(nc4c5cnc(N)nc5
+c3cn c(cn2
+c3cccnc3 )NC(=O)[C@@H](C
+c3ccccc3)c4 ccccc2
+c3ccccc3 S2(=O)=O)
+c3ccccc23 )cc1OC
+c3ccc(cc3)n4 ccnc4
+c3c(OC )ccc4
+c3 cc(Cl)c(Cl)cc3
+c2nc(N )c3
+c2ccccc2 c1C
+c2cccc(c2)[N+](=O)[O-] )cc1
+c2cccc(c2 )C#C
+c2ccc(cc2)n3 ccnc3
+c2ccc(Cl)c(Cl)c2 )cc1
+c2cc( OCCCN
+c1n 3
+c1ccccc1 )\N
+c1ccc2c(c1 )nc3
+c1ccc(Nc2nccc( n2)c3
+c1ccc(C NC(=O)N
+c1cc( NS(=O)(=O)
+c1c[nH]cn1) NC(=O)[C@H](Cc2ccccc2)NC(=O)
+c1 cocc1
+c(ccn n2
+c(ccc1c2 3)S(=O)(=O)N
+c(O) cc(OC
+c(N)ncn c%4
+c(N (C)C)c1
+c(F)cc cc2Cl
+c(Cl )cc4
+c(C)nn (C)c3
+c( nc(N
+c%11 ccc(O)cc
+[nH]c( S
+[nH] 2)[N+](=O)[O-]
+[S+]([O-] )c2nc3
+[O-][N+](=O) c1ccc(NC(=O)
+[O-] .
+[C@]5(C)CC [C@@]34C)
+[C@] (CO)
+[C@H]2C )[C@@H](C)C
+[C@H]2 S
+[C@H]2 C(=O)O
+[C@H]1CC[C@]2(C)[C@@H]1[C@H](O)C [C@@H]3[C@@]4(C)CC
+[C@H](C)O[C@H](C [C@@]1(C)
+[C@H]%1 3C(=O)N
+[C@@]2(C OC(=O)C)
+[C@@H]4C[C@H]5 C[C@H](C
+[C@@H]4C[C@H]5C[C@H](C [C@@H]3C
+[C@@H]3C [C@H]4
+[C@@H](NC(=O)C )C(=O)N
+[C@@H](C OP(=O)(O)O)
+[C@@H]( [C@@H](
+[C@@H]%10CCCN%10 C(=O)
+SC 3
+S CN4
+S )NC(=O)C
+Oc3ccccc3 )c2
+Oc2c1 )C
+OCc3ccccc3 )OC
+OCCO c2cc1
+OC(COc1ccccc1 C(=O)CC
+OC(=O)C=C (CN
+OC(=O)C(C)(C)C )OC
+OC(=C (O)C(=O)
+OC )cc1OC
+O=C(CN 1CC
+O) c(c2
+O) OC1(C)C
+O c2ccc(F)cc2
+O c2ccc(Br)cc2
+O N=C
+Nc1n c(CC
+Nc1ccccc1 NC(=O)c2ccc(CN
+NC(=O)c2occc2 )cc1
+NC(=O)c2nc( ncc2
+NC(=O)[C@H](C c2ccccc2)NC(=O)[C@H](C
+NC(=O)N(CCCl)N =O
+NC(=O)CO CC(=O)N
+N(CCN 3CCOCC3)C(=O)
+I c1ccc2
+I )c1
+Cl)c2 oc(
+Cc2 cc1OC
+Cc1cccc(CN 2CC
+Cc1ccc2 ccc3cccc4ccc1c2c34
+Cc1ccc(cc1 C)C(=O)
+Cc1ccc(cc1 )C(=O)O
+C[C@]12CC[C@H]3[C@@H](CC =C4C[C@H](CC[C@]34C)
+C[C@H]1 CN
+C[C@@]1 (N=C(N
+CSc1nn (c2ccccc2)c3
+COc4ccccc4 O3
+COc1cnc( nc1
+COc1ccccc1 NC(=O)c2
+COc1ccccc1 CCN
+COc1ccc(cc1OC 2CCCC2)C3
+COc1ccc(cc1)C2 =C(N
+COc1ccc( \C=C(/C#N)\
+COC(=O)c1 sc(
+COC [C@@H]1
+CNC(=N)N) NC(=O)[C@H](C)
+CN3C =N
+CN(C)C c1ccc2
+CN(C) c1ncc2
+CN(C \C(=C/
+CN [C@@H]1CCN(C1)c2
+CCn1 c(S)nnc1
+CCc1n c(CN
+CC[C@H](C)[C@H](NC(=O)[C@H](Cc1ccccc1)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CC CCN)
+CCN(CC1)C(=O) c2ccc(NS(=O)(=O)
+CCN =C2
+CCCn1 c(SCC(=O)N
+CCCN1 C(=O)N(CC
+CCCN 3CCCCC3
+CCCCN1C(=O)N (CCCC
+CCCCN CCCN
+CCCCCCCN(CC CCCCC
+CCCCCCCCCCC C
+CCCCCCCCCC C
+CCCCC [C@H](O)
+CCC2(CC1) CCN(CC
+CCC(CC 1)O
+CC(C)c1cc(C(C)C)c( c(c1)C(C)C
+CC(C)[C@H](NC(=O) [C@@H]1CSSC
+CC(C)C[C@H](NC(=O)[C@H](CC (C)C)NC(=O)[C@H](C
+CC(C)C [C@@H](NC(=O)[C@H](C
+CC(C)(C)C [C@H]1N
+CC(C)(C) NC(=O)c1ccccc1
+CC(C) [C@@H](C
+CC(C) NCC
+CC(=O)N[C@@H]1 [C@@H](O)[C@H](O)[C@@H](CO)
+CC(=O)N[C@@H](CC CN=C(N)N)C(=O)N
+CC(=O) c1cn(CC(=O)N
+CC c3cnc(N
+C=C )c2c1
+C5 =N
+C4 =CC(=O)
+C3 4CC5CC(CC(C5)C3)C
+C2=C (O)C(=O)
+C2(CC CCC2
+C1 CCN(CC
+C(=O)c3ccccc3 C(=O)c2c1
+C(=O)O)NC(=O)[C@H](CC (C)C)NC(=O)[C@H](C)NC(=O)[C@H](C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC
+C(=O)O) ccc2
+C(=O)O) NC(=O)CNC(=O)
+C(=O)NCC c3ccccc3
+C(=O)N(C [C@@H]2C)[C@H](C)C
+C(=O)C [C@H](N)C
+C(=O) C(=O)
+=C(I )C(=O)NC2=O
+=C(C =CC(=O)N
+=C(C =C
+5)c6 occc6
+4CCOCC 4)n2
+4CCC 3
+3C c4ccccc4C3
+3=CC=C NC3=O)
+3)C(=O)NCC c4ccccc4
+3 )c5ccc(cc5
+2CCN(CC2)c3ccccn3 )cc1
+2C (O)
+2)c3ncnc4 c3nc5CCCCC
+2)C(=O) OCC
+2) [C@H]1
+1CSC [C@H]1
+1=O) C(=O)
+1)c2ccc(N c3ncc4
+1)c2ccc( c(F)c2
+.C c6ccc(cc6
+)ncc1 [N+](=O)[O-]
+)n2CC =C
+)n2 n1
+)n (Cc4ccccc4
+)ccc3 4)
+)cc4)n cnc23
+)cc( Br)c1
+)c5ccccc5 C(F)(F)F
+)c4cc5OCOc5 cc24
+)c4 c(Cl)cccc4Cl
+)c3ccc( s
+)NC(=O)[C@H](N C2=O)
+)N(C c4ccccc4)C(=O)
+)N 7
+)C2=O)cc1 F
+)C(=O)O 2
+)C(=O)N[C@H](C c3ccccc3
+)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CCCCN)C(=O)N CC(=O)N[C@@H](CCCNC(=N)N
+)C(=O)N[C@@H](CC 3CCCCC3)
+)C(=O)NC 5CC5
+)C(=O)N )cc1
+)C c12
+(O)C2=C( OC(=CC2=O)
+(CCN3 C)
+(C(=O)O) C(=O)O)
+%28 cnc%29
+%26 cnc%27
+# CCO
diff --git a/resources/vocabs/ESPF/protein_codes_uniprot.txt b/resources/vocabs/ESPF/protein_codes_uniprot.txt
new file mode 100644
index 0000000000000000000000000000000000000000..07fe8e4b250c0bc795a3000978a735f9acd44c67
--- /dev/null
+++ b/resources/vocabs/ESPF/protein_codes_uniprot.txt
@@ -0,0 +1,10121 @@
+#version: 0.2
+L L
+A A
+A L
+V L
+G L
+E L
+S L
+G G
+S S
+E E
+T L
+D L
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+I L
+A V
+K L
+A G
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+K K
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+P L
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+V E
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+I K
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+V P
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+I P
+F E
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+Q G
+D S
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+Y G
+T P
+K R
+K N
+K D
+V F
+A Y
+P P
+K T
+N N
+A M
+R D
+V Q
+N E
+I I
+F D
+H G
+Y E
+N S
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+P D
+Q S
+Y S
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+K P
+I F
+T R
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+K I
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+C G
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+N F
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+H R
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+K M
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+I C
+P Y
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+S Y
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+GL G
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+C K
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+NN NN
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+I SG
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+I VL
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+I IG
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+T PE
+I VS
+I ID
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+V FL
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diff --git a/resources/vocabs/ESPF/subword_units_map_chembl.csv b/resources/vocabs/ESPF/subword_units_map_chembl.csv
new file mode 100644
index 0000000000000000000000000000000000000000..bd6fb11a26d59c47024dd8419221cb9f8c5d59f4
--- /dev/null
+++ b/resources/vocabs/ESPF/subword_units_map_chembl.csv
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diff --git a/resources/vocabs/ESPF/subword_units_map_uniprot.csv b/resources/vocabs/ESPF/subword_units_map_uniprot.csv
new file mode 100644
index 0000000000000000000000000000000000000000..3020c4af47022d5f45d2ce58b81f746d2a4692a7
--- /dev/null
+++ b/resources/vocabs/ESPF/subword_units_map_uniprot.csv
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+16250,16250,NLKF,513
+16251,16251,NKVL,513
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+16254,16254,MAML,513
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+16259,16259,IKVP,513
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+16269,16269,DIPE,513
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+16304,16304,TLIR,511
+16305,16305,SFSS,511
+16306,16306,SEKR,511
+16307,16307,SEAQ,511
+16308,16308,REVT,511
+16309,16309,QVNG,511
+16310,16310,QLVI,511
+16311,16311,QVIS,511
+16312,16312,PIAA,511
+16313,16313,PAGG,511
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+16329,16329,EEKP,511
+16330,16330,DSAA,511
+16331,16331,DEPS,511
+16332,16332,VNAS,510
+16333,16333,VKNN,510
+16334,16334,VAFE,510
+16335,16335,TTTL,510
+16336,16336,TSAI,510
+16337,16337,TPVQ,510
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+16342,16342,QEAG,510
+16343,16343,NLGD,510
+16344,16344,NFHL,510
+16345,16345,MSFS,510
+16346,16346,IPYL,510
+16347,16347,INRN,510
+16348,16348,IFDD,510
+16349,16349,IDTR,510
+16350,16350,IILD,510
+16351,16351,GGSGG,510
+16352,16352,GGGTLGHPWG,510
+16353,16353,GGIP,510
+16354,16354,FDNF,510
+16355,16355,FDLP,510
+16356,16356,FARG,510
+16357,16357,ELYS,510
+16358,16358,AYEK,510
+16359,16359,AVRK,510
+16360,16360,AVDR,510
+16361,16361,AMAK,510
+16362,16362,AAFF,510
+16363,16363,YGAT,509
+16364,16364,WLSG,509
+16365,16365,WLGL,509
+16366,16366,WWN,509
+16367,16367,VRAP,509
+16368,16368,VNAH,509
+16369,16369,VGTKL,509
+16370,16370,TTAP,509
+16371,16371,TPTE,509
+16372,16372,TNSL,509
+16373,16373,RGRT,509
+16374,16374,REIQ,509
+16375,16375,PLDLFEE,509
+16376,16376,PLQP,509
+16377,16377,PLHS,509
+16378,16378,KTYS,509
+16379,16379,KPAE,509
+16380,16380,KIQD,509
+16381,16381,KRRS,509
+16382,16382,IFFL,509
+16383,16383,IQLS,509
+16384,16384,HLNN,509
+16385,16385,DFAS,509
+16386,16386,CKW,509
+16387,16387,AYSP,509
+16388,16388,AVYS,509
+16389,16389,ARRN,509
+16390,16390,AQQR,509
+16391,16391,ANKR,509
+16392,16392,AGET,509
+16393,16393,AGAGG,509
+16394,16394,AFSR,509
+16395,16395,AFIG,509
+16396,16396,AARI,509
+16397,16397,YISG,508
+16398,16398,YEVSRSL,508
+16399,16399,VPAL,508
+16400,16400,VLFSPD,508
+16401,16401,TSVP,508
+16402,16402,TSFL,508
+16403,16403,TPPG,508
+16404,16404,TERS,508
+16405,16405,SVQE,508
+16406,16406,STVS,508
+16407,16407,SSRF,508
+16408,16408,SSNP,508
+16409,16409,RGDL,508
+16410,16410,RFLL,508
+16411,16411,QRTG,508
+16412,16412,QLHE,508
+16413,16413,QHRG,508
+16414,16414,QFSG,508
+16415,16415,PTPG,508
+16416,16416,PMFR,508
+16417,16417,PASS,508
+16418,16418,NGVF,508
+16419,16419,NFSII,508
+16420,16420,KTHG,508
+16421,16421,IYFG,508
+16422,16422,IDER,508
+16423,16423,IDDP,508
+16424,16424,HLGNRR,508
+16425,16425,HGSL,508
+16426,16426,HVSL,508
+16427,16427,GGDE,508
+16428,16428,DVPG,508
+16429,16429,DGIS,508
+16430,16430,ASEP,508
+16431,16431,AQNL,508
+16432,16432,AQAD,508
+16433,16433,AHRF,508
+16434,16434,YDYL,507
+16435,16435,WLLE,507
+16436,16436,VVQE,507
+16437,16437,VRTR,507
+16438,16438,VNDN,507
+16439,16439,VGDT,507
+16440,16440,VFES,507
+16441,16441,TTVE,507
+16442,16442,TNKYAEGYPG,507
+16443,16443,TIFG,507
+16444,16444,TDED,507
+16445,16445,RTVS,507
+16446,16446,RSLRVL,507
+16447,16447,PARL,507
+16448,16448,NNRS,507
+16449,16449,NGIT,507
+16450,16450,MEKF,507
+16451,16451,MIDG,507
+16452,16452,KTVP,507
+16453,16453,KRFF,507
+16454,16454,KIES,507
+16455,16455,ISNF,507
+16456,16456,IPRL,507
+16457,16457,IDAN,507
+16458,16458,HGIE,507
+16459,16459,FNPD,507
+16460,16460,FETE,507
+16461,16461,ARDYL,507
+16462,16462,AFSI,507
+16463,16463,AFEQ,507
+16464,16464,YIAE,506
+16465,16465,YESG,506
+16466,16466,WLEG,506
+16467,16467,VMTD,506
+16468,16468,VMAL,506
+16469,16469,VEVF,506
+16470,16470,TRVYTTTP,506
+16471,16471,TQRE,506
+16472,16472,TQQL,506
+16473,16473,SLRQL,506
+16474,16474,SLPR,506
+16475,16475,SLFY,506
+16476,16476,SIKE,506
+16477,16477,RETD,506
+16478,16478,QDAI,506
+16479,16479,PEKE,506
+16480,16480,PEIE,506
+16481,16481,NREL,506
+16482,16482,NKPS,506
+16483,16483,NIQE,506
+16484,16484,MAES,506
+16485,16485,KTIN,506
+16486,16486,KLIH,506
+16487,16487,KAHG,506
+16488,16488,IKTF,506
+16489,16489,IIQL,506
+16490,16490,IFKG,506
+16491,16491,IFLE,506
+16492,16492,HSLL,506
+16493,16493,HKIGE,506
+16494,16494,FSKP,506
+16495,16495,FDES,506
+16496,16496,FDLL,506
+16497,16497,DSIL,506
+16498,16498,DIGL,506
+16499,16499,DGRTL,506
+16500,16500,ARRI,506
+16501,16501,AKRD,506
+16502,16502,ADVK,506
+16503,16503,AADDGVMPQT,506
+16504,16504,YIKE,505
+16505,16505,YVDG,505
+16506,16506,VVRP,505
+16507,16507,TKPS,505
+16508,16508,THEG,505
+16509,16509,TGMG,505
+16510,16510,SRSL,505
+16511,16511,SEVT,505
+16512,16512,RGAT,505
+16513,16513,QSVN,505
+16514,16514,QPFMRWRD,505
+16515,16515,PVAL,505
+16516,16516,PTVE,505
+16517,16517,PFVT,505
+16518,16518,NPND,505
+16519,16519,NLTDGLD,505
+16520,16520,MLEQ,505
+16521,16521,KNSP,505
+16522,16522,KIQL,505
+16523,16523,ISRQR,505
+16524,16524,IKAY,505
+16525,16525,IVKD,505
+16526,16526,FVAT,505
+16527,16527,FKDEVR,505
+16528,16528,FIDNIF,505
+16529,16529,DVDL,505
+16530,16530,AGNE,505
+16531,16531,YLIR,504
+16532,16532,VRTP,504
+16533,16533,VDYG,504
+16534,16534,TNAT,504
+16535,16535,TKRG,504
+16536,16536,TIQL,504
+16537,16537,TDVP,504
+16538,16538,TDIP,504
+16539,16539,RTIG,504
+16540,16540,RKPS,504
+16541,16541,PTVI,504
+16542,16542,PATG,504
+16543,16543,NLRN,504
+16544,16544,NEVV,504
+16545,16545,NDTS,504
+16546,16546,MSTP,504
+16547,16547,MDHL,504
+16548,16548,LLHTS,504
+16549,16549,KSVI,504
+16550,16550,KLRP,504
+16551,16551,IRDS,504
+16552,16552,HLDS,504
+16553,16553,HIID,504
+16554,16554,HMM,504
+16555,16555,FLRF,504
+16556,16556,FDRE,504
+16557,16557,FIEG,504
+16558,16558,DEPE,504
+16559,16559,DNY,504
+16560,16560,CQV,504
+16561,16561,CAGL,504
+16562,16562,AMAD,504
+16563,16563,AKTR,504
+16564,16564,AFFG,504
+16565,16565,YSAP,503
+16566,16566,WVLL,503
+16567,16567,VRMG,503
+16568,16568,VNSF,503
+16569,16569,VGFL,503
+16570,16570,VFRD,503
+16571,16571,TSIVG,503
+16572,16572,TIAY,503
+16573,16573,TFLL,503
+16574,16574,TEAP,503
+16575,16575,TDTL,503
+16576,16576,SVAN,503
+16577,16577,SLHP,503
+16578,16578,RSSE,503
+16579,16579,QIES,503
+16580,16580,PTYG,503
+16581,16581,MNDL,503
+16582,16582,MIDE,503
+16583,16583,KPTS,503
+16584,16584,KGRN,503
+16585,16585,KDFN,503
+16586,16586,KAYE,503
+16587,16587,IDSP,503
+16588,16588,HLPG,503
+16589,16589,HGIQVE,503
+16590,16590,FGQN,503
+16591,16591,FDRD,503
+16592,16592,FAQG,503
+16593,16593,ELVH,503
+16594,16594,EDVL,503
+16595,16595,DIRL,503
+16596,16596,DGQG,503
+16597,16597,AMKE,503
+16598,16598,ALQH,503
+16599,16599,YDLS,502
+16600,16600,VSNN,502
+16601,16601,VLKY,502
+16602,16602,VGKTE,502
+16603,16603,TRDD,502
+16604,16604,RIAH,502
+16605,16605,RGTVVTG,502
+16606,16606,QAIS,502
+16607,16607,PTASG,502
+16608,16608,MNKK,502
+16609,16609,MLAM,502
+16610,16610,MAGS,502
+16611,16611,KNAK,502
+16612,16612,KKVD,502
+16613,16613,KKSD,502
+16614,16614,KITS,502
+16615,16615,KHLG,502
+16616,16616,ISKQ,502
+16617,16617,HLIN,502
+16618,16618,HWR,502
+16619,16619,GLHG,502
+16620,16620,FSFE,502
+16621,16621,ERAK,502
+16622,16622,DTAK,502
+16623,16623,DLPAPS,502
+16624,16624,AVDF,502
+16625,16625,ADEV,502
+16626,16626,YSRR,501
+16627,16627,WWR,501
+16628,16628,VIQE,501
+16629,16629,VGEI,501
+16630,16630,VDNG,501
+16631,16631,TQRL,501
+16632,16632,TLKP,501
+16633,16633,TEES,501
+16634,16634,TAQE,501
+16635,16635,SGAF,501
+16636,16636,RYSG,501
+16637,16637,RLYG,501
+16638,16638,RKVK,501
+16639,16639,QQAT,501
+16640,16640,QGAS,501
+16641,16641,QAML,501
+16642,16642,PTKK,501
+16643,16643,PRAR,501
+16644,16644,PEVD,501
+16645,16645,NTVK,501
+16646,16646,NPRE,501
+16647,16647,NLRT,501
+16648,16648,NLIF,501
+16649,16649,NESG,501
+16650,16650,NDFY,501
+16651,16651,MVAI,501
+16652,16652,MLRN,501
+16653,16653,MKAQ,501
+16654,16654,MNLS,501
+16655,16655,KYPD,501
+16656,16656,KTDS,501
+16657,16657,KSAS,501
+16658,16658,KLVP,501
+16659,16659,HGKP,501
+16660,16660,HEAF,501
+16661,16661,FIRG,501
+16662,16662,EEIN,501
+16663,16663,AQAK,501
+16664,16664,ANST,501
+16665,16665,ADFP,501
+16666,16666,YLKH,500
+16667,16667,TTVP,500
+16668,16668,TSNP,500
+16669,16669,TSIP,500
+16670,16670,SERS,500
+16671,16671,RRVE,500
+16672,16672,QQQH,500
+16673,16673,PDVS,500
+16674,16674,NWI,500
+16675,16675,MYVCG,500
+16676,16676,KTPS,500
+16677,16677,KSDS,500
+16678,16678,ISRF,500
+16679,16679,IKIE,500
+16680,16680,HLVN,500
+16681,16681,HGHS,500
+16682,16682,HDIP,500
+16683,16683,FSTG,500
+16684,16684,FKNG,500
+16685,16685,ERRL,500
+16686,16686,DPSS,500
+16687,16687,CPSG,500
+16688,16688,CLQG,500
+16689,16689,AWSP,500
+16690,16690,ARGD,500
+16691,16691,AKPS,500
+16692,16692,AKDD,500
diff --git a/resources/vocabs/drug_vqa/combinedVoc-wholeFour.voc b/resources/vocabs/drug_vqa/combinedVoc-wholeFour.voc
new file mode 100644
index 0000000000000000000000000000000000000000..66f63fd3b892ce30337bf9552d3a3315b6ec7dc1
--- /dev/null
+++ b/resources/vocabs/drug_vqa/combinedVoc-wholeFour.voc
@@ -0,0 +1,247 @@
+[PAD]
+[102Ru]
+[80Se]
+[N-]
+[33SH2]
+[Ag+]
+Cl
+[Fr]
+[C@@H-]
+[SiH2]
+[Sr+2]
+[P@@H]
+[CH2]
+[10B]
+[SH]
+[Gd]
+[Ru+2]
+[81Br-]
+[Ar]
+[85Sr+2]
+[29Si]
+[Cu+2]
+[CH3-]
+[56Ni]
+[Na+]
+[Au+]
+[Co+3]
+[Se-]
+[NH-]
+[113In]
+)
+[Ir]
+[NH+]
+[109Cd]
+[Br-]
+[43K]
+[15NH2]
+[Zn+2]
+[P@H]
+[Ce]
+[As+5]
+[Pt+4]
+n
+6
+[Ca+2]
+[121Sn]
+4
+[nH+]
+Br
+[F]
+[Pd]
+3
+[Br]
+\
+#
+[H-]
+[16OH2]
+[O-2]
+[nH]
+[Ni+2]
+[CH]
+I
+[Ho]
+[C@H-]
+[209Pb]
+p
+F
+[18F]
+[183W]
+[34SH2]
+[Co+2]
+[CH2-]
+[Sb]
+[Be+2]
+[n+]
+[13C]
+[Fe+2]
+[B]
+[178W]
+[67Ga+3]
+[3H]
+[63Cu]
+[79Br-]
+[n-]
+s
+[C+]
+[205Pb]
+[H+]
+[O-]
+(
+[H]
+[OH2+]
+[Mn]
+[Se]
+[78Se]
+[s+]
+[NH2-]
+[Fe]
+[C@@]
+[OH-]
+[S@]
+5
+[173Ta]
+[11CH4]
+[Cl-]
+[128IH]
+[Mo]
+[As+]
+[B-]
+[129IH]
+[Ta+5]
+[68Ge]
+C
+[77BrH]
+/
+[Hg+]
+[NH4+]
+[P@@+]
+[CH-]
+[84BrH]
+[43Ca]
+[S-2]
+[11CH3]
+[Fe+3]
+[Ca]
+[125Sb]
+[Sb-]
+[P@]
+[210Tl]
+[PH+]
+[K]
+[SH+]
+o
+[Si+4]
+[89Sr+2]
+[I]
+[15NH3]
+O
+[14CH2]
+[33P]
+c
+=
+[C-]
+[127IH]
+[141Ce]
+[75Se]
+8
+[11B]
+[P@@]
+B
+.
+N
+[P@+]
+[Hg]
+[252Cf]
+[Pb+2]
+[O+]
+[56Fe]
+[13CH]
+[45Ca+2]
+[Zr]
+[45K]
+[C@H]
+[In+3]
+[S@@]
+[Si]
+[99Tc]
+[Hg+2]
+[o+]
+[Ba+2]
+[N@+]
+1
+9
+[AlH4-]
+[44Ca]
+[N+]
+[Cu]
+[9Li]
+[Tl+]
+[Co]
+[Re]
+[OH3+]
+[Mn+2]
+[Ru+]
+[Cl]
+[Li+]
+[V]
+7
+[115Cd]
+[Mg+2]
+[P+]
+[Pd+2]
+[I-]
+[Cr+3]
+[Zn]
+[C@]
+[110Ag]
+[214Pb]
+2
+[60Fe]
+S
+[NH2+]
+[P@@H+]
+[W]
+[Al+3]
+[F-]
+[OH]
+[125IH]
+[N@@+]
+[C]
+[13NH3]
+[Bi]
+[218Po]
+[S-]
+[As]
+[SiH3]
+[23Na]
+[Ge]
+[197Au]
+[32P]
+[12CH4]
+[C@@H]
+[17NH3]
+[NH3+]
+[132IH]
+[99Mo]
+[207Pb]
+[7Li]
+[2H]
+[Al]
+[OH+]
+[Zr+4]
+[S+]
+[125I]
+-
+[Ag]
+[Sb+3]
+[3He]
+[90Sr+2]
+[K+]
+[Y+3]
+[Ti]
+[188W]
+[Rb+]
+[Pt+2]
+[14C]
+P
\ No newline at end of file
diff --git a/resources/vocabs/drug_vqa/sequence.voc b/resources/vocabs/drug_vqa/sequence.voc
new file mode 100644
index 0000000000000000000000000000000000000000..1bef043584d9b8c9cfa53c82624494b2ae181bf2
--- /dev/null
+++ b/resources/vocabs/drug_vqa/sequence.voc
@@ -0,0 +1,21 @@
+[PAD]
+Y
+Q
+V
+P
+L
+T
+N
+R
+G
+A
+M
+S
+C
+H
+F
+D
+E
+I
+K
+W
\ No newline at end of file
diff --git a/resources/vocabs/smiles.txt b/resources/vocabs/smiles.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6a7cad14fa33ceb18fb9fba55d10239f1fc5260b
--- /dev/null
+++ b/resources/vocabs/smiles.txt
@@ -0,0 +1,591 @@
+[PAD]
+[unused1]
+[unused2]
+[unused3]
+[unused4]
+[unused5]
+[unused6]
+[unused7]
+[unused8]
+[unused9]
+[unused10]
+[UNK]
+[CLS]
+[SEP]
+[MASK]
+c
+C
+(
+)
+O
+1
+2
+=
+N
+.
+n
+3
+F
+Cl
+>>
+~
+-
+4
+[C@H]
+S
+[C@@H]
+[O-]
+Br
+#
+/
+[nH]
+[N+]
+s
+5
+o
+P
+[Na+]
+[Si]
+I
+[Na]
+[Pd]
+[K+]
+[K]
+[P]
+B
+[C@]
+[C@@]
+[Cl-]
+6
+[OH-]
+\
+[N-]
+[Li]
+[H]
+[2H]
+[NH4+]
+[c-]
+[P-]
+[Cs+]
+[Li+]
+[Cs]
+[NaH]
+[H-]
+[O+]
+[BH4-]
+[Cu]
+7
+[Mg]
+[Fe+2]
+[n+]
+[Sn]
+[BH-]
+[Pd+2]
+[CH]
+[I-]
+[Br-]
+[C-]
+[Zn]
+[B-]
+[F-]
+[Al]
+[P+]
+[BH3-]
+[Fe]
+[C]
+[AlH4]
+[Ni]
+[SiH]
+8
+[Cu+2]
+[Mn]
+[AlH]
+[nH+]
+[AlH4-]
+[O-2]
+[Cr]
+[Mg+2]
+[NH3+]
+[S@]
+[Pt]
+[Al+3]
+[S@@]
+[S-]
+[Ti]
+[Zn+2]
+[PH]
+[NH2+]
+[Ru]
+[Ag+]
+[S+]
+[I+3]
+[NH+]
+[Ca+2]
+[Ag]
+9
+[Os]
+[Se]
+[SiH2]
+[Ca]
+[Ti+4]
+[Ac]
+[Cu+]
+[S]
+[Rh]
+[Cl+3]
+[cH-]
+[Zn+]
+[O]
+[Cl+]
+[SH]
+[H+]
+[Pd+]
+[se]
+[PH+]
+[I]
+[Pt+2]
+[C+]
+[Mg+]
+[Hg]
+[W]
+[SnH]
+[SiH3]
+[Fe+3]
+[NH]
+[Mo]
+[CH2+]
+%10
+[CH2-]
+[CH2]
+[n-]
+[Ce+4]
+[NH-]
+[Co]
+[I+]
+[PH2]
+[Pt+4]
+[Ce]
+[B]
+[Sn+2]
+[Ba+2]
+%11
+[Fe-3]
+[18F]
+[SH-]
+[Pb+2]
+[Os-2]
+[Zr+4]
+[N]
+[Ir]
+[Bi]
+[Ni+2]
+[P@]
+[Co+2]
+[s+]
+[As]
+[P+3]
+[Hg+2]
+[Yb+3]
+[CH-]
+[Zr+2]
+[Mn+2]
+[CH+]
+[In]
+[KH]
+[Ce+3]
+[Zr]
+[AlH2-]
+[OH2+]
+[Ti+3]
+[Rh+2]
+[Sb]
+[S-2]
+%12
+[P@@]
+[Si@H]
+[Mn+4]
+p
+[Ba]
+[NH2-]
+[Ge]
+[Pb+4]
+[Cr+3]
+[Au]
+[LiH]
+[Sc+3]
+[o+]
+[Rh-3]
+%13
+[Br]
+[Sb-]
+[S@+]
+[I+2]
+[Ar]
+[V]
+[Cu-]
+[Al-]
+[Te]
+[13c]
+[13C]
+[Cl]
+[PH4+]
+[SiH4]
+[te]
+[CH3-]
+[S@@+]
+[Rh+3]
+[SH+]
+[Bi+3]
+[Br+2]
+[La]
+[La+3]
+[Pt-2]
+[N@@]
+[PH3+]
+[N@]
+[Si+4]
+[Sr+2]
+[Al+]
+[Pb]
+[SeH]
+[Si-]
+[V+5]
+[Y+3]
+[Re]
+[Ru+]
+[Sm]
+*
+[3H]
+[NH2]
+[Ag-]
+[13CH3]
+[OH+]
+[Ru+3]
+[OH]
+[Gd+3]
+[13CH2]
+[In+3]
+[Si@@]
+[Si@]
+[Ti+2]
+[Sn+]
+[Cl+2]
+[AlH-]
+[Pd-2]
+[SnH3]
+[B+3]
+[Cu-2]
+[Nd+3]
+[Pb+3]
+[13cH]
+[Fe-4]
+[Ga]
+[Sn+4]
+[Hg+]
+[11CH3]
+[Hf]
+[Pr]
+[Y]
+[S+2]
+[Cd]
+[Cr+6]
+[Zr+3]
+[Rh+]
+[CH3]
+[N-3]
+[Hf+2]
+[Th]
+[Sb+3]
+%14
+[Cr+2]
+[Ru+2]
+[Hf+4]
+[14C]
+[Ta]
+[Tl+]
+[B+]
+[Os+4]
+[PdH2]
+[Pd-]
+[Cd+2]
+[Co+3]
+[S+4]
+[Nb+5]
+[123I]
+[c+]
+[Rb+]
+[V+2]
+[CH3+]
+[Ag+2]
+[cH+]
+[Mn+3]
+[Se-]
+[As-]
+[Eu+3]
+[SH2]
+[Sm+3]
+[IH+]
+%15
+[OH3+]
+[PH3]
+[IH2+]
+[SH2+]
+[Ir+3]
+[AlH3]
+[Sc]
+[Yb]
+[15NH2]
+[Lu]
+[sH+]
+[Gd]
+[18F-]
+[SH3+]
+[SnH4]
+[TeH]
+[Si@@H]
+[Ga+3]
+[CaH2]
+[Tl]
+[Ta+5]
+[GeH]
+[Br+]
+[Sr]
+[Tl+3]
+[Sm+2]
+[PH5]
+%16
+[N@@+]
+[Au+3]
+[C-4]
+[Nd]
+[Ti+]
+[IH]
+[N@+]
+[125I]
+[Eu]
+[Sn+3]
+[Nb]
+[Er+3]
+[123I-]
+[14c]
+%17
+[SnH2]
+[YH]
+[Sb+5]
+[Pr+3]
+[Ir+]
+[N+3]
+[AlH2]
+[19F]
+%18
+[Tb]
+[14CH]
+[Mo+4]
+[Si+]
+[BH]
+[Be]
+[Rb]
+[pH]
+%19
+%20
+[Xe]
+[Ir-]
+[Be+2]
+[C+4]
+[RuH2]
+[15NH]
+[U+2]
+[Au-]
+%21
+%22
+[Au+]
+[15n]
+[Al+2]
+[Tb+3]
+[15N]
+[V+3]
+[W+6]
+[14CH3]
+[Cr+4]
+[ClH+]
+b
+[Ti+6]
+[Nd+]
+[Zr+]
+[PH2+]
+[Fm]
+[N@H+]
+[RuH]
+[Dy+3]
+%23
+[Hf+3]
+[W+4]
+[11C]
+[13CH]
+[Er]
+[124I]
+[LaH]
+[F]
+[siH]
+[Ga+]
+[Cm]
+[GeH3]
+[IH-]
+[U+6]
+[SeH+]
+[32P]
+[SeH-]
+[Pt-]
+[Ir+2]
+[se+]
+[U]
+[F+]
+[BH2]
+[As+]
+[Cf]
+[ClH2+]
+[Ni+]
+[TeH3]
+[SbH2]
+[Ag+3]
+%24
+[18O]
+[PH4]
+[Os+2]
+[Na-]
+[Sb+2]
+[V+4]
+[Ho+3]
+[68Ga]
+[PH-]
+[Bi+2]
+[Ce+2]
+[Pd+3]
+[99Tc]
+[13C@@H]
+[Fe+6]
+[c]
+[GeH2]
+[10B]
+[Cu+3]
+[Mo+2]
+[Cr+]
+[Pd+4]
+[Dy]
+[AsH]
+[Ba+]
+[SeH2]
+[In+]
+[TeH2]
+[BrH+]
+[14cH]
+[W+]
+[13C@H]
+[AsH2]
+[In+2]
+[N+2]
+[N@@H+]
+[SbH]
+[60Co]
+[AsH4+]
+[AsH3]
+[18OH]
+[Ru-2]
+[Na-2]
+[CuH2]
+[31P]
+[Ti+5]
+[35S]
+[P@@H]
+[ArH]
+[Co+]
+[Zr-2]
+[BH2-]
+[131I]
+[SH5]
+[VH]
+[B+2]
+[Yb+2]
+[14C@H]
+[211At]
+[NH3+2]
+[IrH]
+[IrH2]
+[Rh-]
+[Cr-]
+[Sb+]
+[Ni+3]
+[TaH3]
+[Tl+2]
+[64Cu]
+[Tc]
+[Cd+]
+[1H]
+[15nH]
+[AlH2+]
+[FH+2]
+[BiH3]
+[Ru-]
+[Mo+6]
+[AsH+]
+[BaH2]
+[BaH]
+[Fe+4]
+[229Th]
+[Th+4]
+[As+3]
+[NH+3]
+[P@H]
+[Li-]
+[7NaH]
+[Bi+]
+[PtH+2]
+[p-]
+[Re+5]
+[NiH]
+[Ni-]
+[Xe+]
+[Ca+]
+[11c]
+[Rh+4]
+[AcH]
+[HeH]
+[Sc+2]
+[Mn+]
+[UH]
+[14CH2]
+[SiH4+]
+[18OH2]
+[Ac-]
+[Re+4]
+[118Sn]
+[153Sm]
+[P+2]
+[9CH]
+[9CH3]
+[Y-]
+[NiH2]
+[Si+2]
+[Mn+6]
+[ZrH2]
+[C-2]
+[Bi+5]
+[24NaH]
+[Fr]
+[15CH]
+[Se+]
+[At]
+[P-3]
+[124I-]
+[CuH2-]
+[Nb+4]
+[Nb+3]
+[MgH]
+[Ir+4]
+[67Ga+3]
+[67Ga]
+[13N]
+[15OH2]
+[2NH]
+[Ho]
+[Cn]
\ No newline at end of file