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--- |
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title: MHNfs |
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emoji: 🔬 |
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short_description: Activity prediction for low-data scenarios |
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colorFrom: gray |
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colorTo: gray |
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sdk: streamlit |
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sdk_version: 1.29.0 |
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app_file: app.py |
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pinned: true |
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--- |
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# Activity Predictions with MHNfs for low-data scenarios |
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## ⚙️ Under the hood |
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<div style="text-align: justify"> |
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The predictive model (MHNfs) used in this application was specifically designed and |
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trained for low-data scenarios. The model predicts whether a molecule is active or |
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inactive. The predicted activity value is a continuous value between 0 and 1, and, |
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similar to a probability, the higher/lower the value, the more confident the model |
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is that the molecule is active/inactive.<br> |
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<br> |
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The model was trained on the FS-Mol dataset which |
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includes 5120 tasks (roughly 5000 tasks were used for training, rest for evaluation). |
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The training tasks are listed here: |
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<a href="https://github.com/microsoft/FS-Mol/tree/main/datasets/targets" |
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target="_blank">https://github.com/microsoft/FS-Mol/tree/main/datasets/targets</a>. |
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</div> |
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## 🎯 About few-shot learning and the model MHNfs |
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<div style="text-align: justify"> |
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<b>Few-shot learning</b> is a machine learning sub-field which aims to provide |
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predictive models for scenarios in which only little data is known/available.<br> |
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<br> |
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<b>MHNfs</b> is a few-shot learning model which is specifically designed for drug |
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discovery applications. It is built to use the input prompts in a way such that |
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the provided available knowledge, i.e. the known active and inactive molecules, |
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functions as context to predict the activity of the new requested molecules. |
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Precisely, the provided active and inactive molecules are associated with a |
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large set of general molecules - called context molecules - to enrich the |
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provided information and to remove spurious correlations arising from the |
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decoration of molecules. This is analogous to a Large Language Model which would |
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not only use the provided information in the current prompt as context but would |
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also have access to way more information, e.g., a prompting history. |
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</div> |
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## 💻 Run the prediction pipeline locally for larger screening chunks |
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### Get started: |
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```bash |
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# Copied from hugging face |
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# Make sure you have git-lfs installed (https://git-lfs.com) |
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git lfs install |
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# Clone repo |
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git clone https://huggingface.co/spaces/tschouis/mhnfs |
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# Alternatively, if you want to clone without large files |
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GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/spaces/tschouis/mhnfs |
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``` |
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### Install requirements |
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```bash |
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pip install -r requirements.txt |
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``` |
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Notably, this command was tested inside a conda environment with python 3.7. |
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### Run the prediction pipeline: |
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For your screening, load the model, i.e. the **Activity Predictor** into your python file or notebook and simply run it: |
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```python |
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from src.prediction_pipeline load ActivityPredictor |
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# Define inputs |
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query_smiles = ["C1CCCCC1", "C1CCCCC1", "C1CCCCC1", "C1CCCCC1"] # Replace with your data |
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support_actives_smiles = ["C1CCCCC1", "C1CCCCC1"] # Replace with your data |
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support_inactives_smiles = ["C1CCCCC1", "C1CCCCC1"] # Replace with your data |
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# Make predictions |
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predictions = predictor.predict(query_smiles, support_actives_smiles support_inactives_smiles) |
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``` |
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* Provide molecules in SMILES notation. |
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* Make sure that the inputs to the Activity Predictor are either comma separated lists, or flattened numpy arrays, or pandas DataFrames. In the latter case, there should be a "smiles" column (both upper and lower case "SMILES" are accepted). All other columns are ignored. |
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### Run the app locally with streamlib: |
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```bash |
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# Navigate into root directory of this project |
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cd .../whatever_your_dir_name_is/ # Replace with your path |
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# Run streamlit app |
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python -m streamlit run |
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``` |
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## 📚 Cite us |
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``` |
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@inproceedings{ |
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schimunek2023contextenriched, |
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title={Context-enriched molecule representations improve few-shot drug discovery}, |
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author={Johannes Schimunek and Philipp Seidl and Lukas Friedrich and Daniel Kuhn and Friedrich Rippmann and Sepp Hochreiter and Günter Klambauer}, |
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booktitle={The Eleventh International Conference on Learning Representations}, |
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year={2023}, |
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url={https://openreview.net/forum?id=XrMWUuEevr} |
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} |
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``` |
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