ChatterjeeAyan/AI-Bind
Interpretable AI pipeline improving binding predictions for novel protein targets and ligands
This project helps pharmaceutical researchers and computational chemists rapidly screen potential drug candidates and understand how novel proteins and ligands might bind. It takes simple chemical features like a protein's amino acid sequence and a ligand's SMILES string, then predicts the probability of them binding. The output helps prioritize pairs for further validation or guide expensive auto-docking simulations.
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Use this if you need to quickly identify potential protein-ligand interactions for never-before-seen molecules without relying on existing 3D structural data.
Not ideal if you are solely interested in predicting binding for molecules that have already been studied and are present in existing training datasets.
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Jupyter Notebook
License
MIT
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Last pushed
May 06, 2024
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