gcorso/DiffDock

Health v2 · maintenance only

Adoption not part of the score

1569 stars · 359 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

DiffDock is a deep learning implementation of a diffusion generative model for molecular docking, predicting how small molecule ligands bind to protein structures. It frames docking as generative modeling over the non-Euclidean manifold of ligand poses (translations, rotations, torsions) and includes the improved DiffDock-L model.

Use cases

• predict how a small molecule binds to a protein structure
• run molecular docking for drug design
• dock ligands to computationally folded proteins without a known pocket
• generate ligand binding poses with confidence estimates
• replicate state-of-the-art docking benchmarks on PDBBind
• try docking predictions through a web interface

When to choose

• you need fast, accurate deep-learning-based docking instead of traditional search-based tools
• your protein structure is computationally predicted and lacks a known binding pocket
• you want confidence estimates alongside predicted poses
• you are doing ML research on equivariant diffusion models for molecules

When to avoid

• you need physics-based scoring or exhaustive conformational search guarantees
• you lack GPU resources or a Python/ML environment
• you require certified binding free-energy calculations rather than pose prediction

Facets

library · maturity active

machine-learning deep-learning simulation bioinformatics machine-learning healthcare artificial-intelligence python molecular-docking diffusion-models drug-design equivariance score-based-models computational-chemistry ligand-binding deep-learning linux docker gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="gcorso/DiffDock")