DiffDock Molecular Docking

关于

DiffDock is a specialized Claude Code skill for computational chemistry and drug discovery that leverages diffusion-based deep learning to model protein-ligand interactions. It enables researchers to predict high-accuracy 3D binding poses from either protein structures (PDB) or sequences (ESMFold) alongside chemical SMILES strings. Beyond simple pose prediction, the skill provides confidence scoring to assess reliability and supports high-throughput virtual screening of compound libraries, bridging the gap between raw chemical data and structural insights for lead optimization.

主要功能

• 3,718 GitHub stars
• Batch processing for large-scale virtual screening
• Automated confidence scoring for prediction reliability
• High-accuracy 3D ligand binding pose prediction
• Support for protein structures (PDB) and sequences (ESMFold)
• Integration with ligand formats including SMILES, SDF, and MOL2

使用场景

• Virtual screening of chemical libraries against protein targets
• Structure-based drug discovery and lead optimization
• Binding site prediction and molecular interaction analysis