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: