diffdocklisted

# DiffDock: Molecular Docking with Diffusion Models ## Overview 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. ## When to Use This Skill 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 docking of multiple protein-ligand pairs ## Installation and Environment Setup ### Check Environment Status Before proceeding with DiffDock tasks, verify the environ