update

model_cards/article.md

@@ -1,76 +1,12 @@
-# Model documentation & parameters
+# Model documentation 
 
-**Algorithm Version**: Which model version to use.
 
-**Property goals**: One or multiple properties that will be optimized.
+**SMILES**: 
 
-**Protein target**: An AAS of a protein target used for conditioning. Leave blank unless you use `affinity` as a `property goal`.
 
-**Decoding temperature**: The temperature parameter in the SMILES/SELFIES decoder. Higher values lead to more explorative choices, smaller values culminate in mode collapse.
+ell lines in rows and genes in columns
 
-**Maximal sequence length**: The maximal number of SMILES tokens in the generated molecule.
 
-**Number of samples**: How many samples should be generated (between 1 and 50).
-
-**Limit**: Hypercube limits in the latent space.
-
-**Number of steps**: Number of steps for a GP optmization round. The longer the slower. Has to be at least `Number of initial points`.
-
-**Number of initial points**: Number of initial points evaluated. The longer the slower.
-
-**Number of optimization rounds**: Maximum number of optimization rounds.
-
-**Sampling variance**: Variance of the Gaussian noise applied during sampling from the optimal point.
-
-**Samples for evaluation**: Number of samples averaged for each minimization function evaluation. 
-
-**Max. sampling steps**: Maximum number of sampling steps in an optmization round.
-
-**Seed**: The random seed used for initialization.
-
-
-
-# Model card -- PaccMannGP
-
-**Model Details**: [PaccMann<sup>GP</sup>](https://github.com/PaccMann/paccmann_gp) is a language-based Variational Autoencoder that is coupled with a GaussianProcess for controlled sampling. This model systematically explores the latent space of a trained molecular VAE.
-
-**Developers**: Jannis Born, Matteo Manica and colleagues from IBM Research.
-
-**Distributors**: Original authors' code wrapped and distributed by GT4SD Team (2023) from IBM Research.
-
-**Model date**: Published in 2022.
-
-**Model version**: A molecular VAE trained on 1.5M molecules from ChEMBL. 
-
-**Model type**: A language-based molecular generative model that can be explored with Gaussian Processes to generate molecules with desired properties.
-
-**Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**: 
-Described in the [original paper](https://pubs.acs.org/doi/10.1021/acs.jcim.1c00889).
-
-**Paper or other resource for more information**: 
-[Active Site Sequence Representations of Human Kinases Outperform Full Sequence Representations for Affinity Prediction and Inhibitor Generation: 3D Effects in a 1D Model (2022; *Journal of Chemical Information & Modeling*)](https://pubs.acs.org/doi/10.1021/acs.jcim.1c00889).
-
-**License**: MIT
-
-**Where to send questions or comments about the model**: Open an issue on [GT4SD repository](https://github.com/GT4SD/gt4sd-core).
-
-**Intended Use. Use cases that were envisioned during development**: Chemical research, in particular drug discovery.
-
-**Primary intended uses/users**: Researchers and computational chemists using the model for model comparison or research exploration purposes.
-
-**Out-of-scope use cases**: Production-level inference, producing molecules with harmful properties.
-
-**Factors**: Not applicable.
-
-**Metrics**: High reward on generating molecules with desired properties.
-
-**Datasets**: ChEMBL.
-
-**Ethical Considerations**: Unclear, please consult with original authors in case of questions.
-
-**Caveats and Recommendations**: Unclear, please consult with original authors in case of questions.
-
-Model card prototype inspired by [Mitchell et al. (2019)](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)
 
 ## Citation
 If you use this webservice, please cite:

model_cards/description.md

@@ -1,3 +1,3 @@
 <img align="right" src="https://repository-images.githubusercontent.com/219031433/3729c600-fcdc-11e9-9cdf-60c4a2b41700" alt="logo" width="120" >
 
-PaccMann is a 
+PaccMann is a webservice for anticancer compound sensitivity prediction. For details on usage, please see the [PaccMann paper](https://academic.oup.com/nar/article/48/W1/W502/5836770) in *Nucleic Acid Research*.