molML/chemical-language-processing-for-bioactivity-prediction
The official codebase for the paper "A Hitchhiker's Guide to Deep Chemical Language Processing for Bioactivity Prediction"
This project helps chemists and drug discovery scientists predict if a small molecule will bind to a target protein, a critical step in drug development. You provide a dataset of molecules, represented as SMILES or SELFIES strings, along with their known binding activity. The project then trains a model that can predict the bioactivity (binding or inhibition) of new molecules, outputting scores or classifications.
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Use this if you need to quickly train and evaluate deep learning models for predicting small molecule bioactivity, experimenting with different molecule representations or model architectures.
Not ideal if you prefer a graphical user interface or a solution that doesn't require writing Python code.
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Mar 26, 2025
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