Diffdock

This is a diffusion-based deep learning model for predicting how small molecules bind to proteins in 3D, which matters if you're doing drug discovery or chemical biology work. You give it a protein structure (PDB or sequence via ESMFold) and a ligand (SMILES or structure file), and it generates ranked binding poses with confidence scores. The key thing to understand is it predicts pose geometry and model certainty, not binding affinity, so you'll need to pair it with scoring functions like GNINA for energy estimates. It handles batch virtual screening well, especially if you pre-compute protein embeddings for large libraries. GPU recommended since it's 10-100x faster than CPU, and the first run takes a few minutes to build lookup tables.

Install to Claude Code

npx -y skills add k-dense-ai/scientific-agent-skills --skill diffdock --agent claude-code