DiffDock Molecular Docking

About

DiffDock is a specialized scientific skill for Claude Code designed to accelerate structure-based drug discovery and computational biology. It leverages diffusion models to predict the most probable 3D orientations (poses) of small molecule ligands when bound to target proteins, providing both structural coordinates and reliability confidence scores. This skill enables researchers to automate complex bioinformatics workflows, ranging from single protein-ligand docking to large-scale virtual screening and ensemble docking, significantly reducing the manual effort required to model molecular interactions.

Key Features

• Batch processing capabilities for virtual screening campaigns
• Support for PDB files, amino acid sequences, and SMILES strings
• 2,188 GitHub stars
• Automated confidence scoring to assess prediction reliability
• High-accuracy 3D ligand binding pose prediction
• Seamless integration with ESMFold for sequence-to-structure workflows

Use Cases

• Predicting the binding orientation of novel drug candidates to target proteins
• Conducting virtual screenings of compound libraries to identify lead molecules
• Analyzing molecular interactions for structure-based drug design and optimization