gcorso/DiffDock
Implementation of DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
DiffDock helps drug discovery scientists and computational chemists predict how a small molecule (ligand) binds to a protein, which is crucial for drug design. You input a protein structure (or sequence) and a ligand (as a SMILES string or file), and it generates the likely 3D binding poses of the ligand within the protein's active site. This is used by researchers in pharmaceutical and biotech fields to screen potential drug candidates.
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Use this if you need to accurately predict the 3D binding pose of drug-like molecules to protein targets, especially when exploring new drug candidates.
Not ideal if your primary goal is to predict the exact binding affinity or strength of interaction between the ligand and protein, as DiffDock provides pose predictions and a confidence score rather than direct affinity.
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Python
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MIT
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May 02, 2025
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