diffdock

Topics in the source

• DiffDock: Molecular Docking with Diffusion Models
• Overview
• When to Use This Skill
• Installation and Environment Setup
• Check Environment Status
• Installation Options
• Core Workflows
• Workflow 1: Single Protein-Ligand Docking

DiffDock is a diffusion-based deep learning tool for molecular docking that predicts 3D binding poses of small molecule ligands to protein targets. It represents the state-of-the-art in computational docking, crucial for structure-based drug discovery and chemical biology. Core Capabilities: Predict ligand binding poses with high accuracy using deep learning Support protein structures (PDB files) or sequences (via ESMFold) Process single complexes or batch virtual screening campaigns Generate confidence scores to assess prediction reliability Handle diverse ligand inputs (SMILES, SDF, MOL2) Key Distinction: DiffDock predicts binding poses (3D structure) and confidence (prediction certainty), NOT binding affinity (ΔG, Kd). Always combine with scoring functions (GNINA, MM/GBSA) for affinity assessment. This skill should be used when: "Dock this ligand to a protein" or "predict binding pose" "Run molecular docking" or "perform protein-ligand docking" "Virtual screening" or "screen compound library" "Where does this molecule bind?" or "predict binding site" Structure-based drug design or lead optimization tasks Tasks involving PDB files + SMILES strings or ligand structures Batch docki

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