diffdock

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

Related Skills

• molecular-docking: Full end-to-end pipeline including target prep, pocket detection, AutoDock Vina, scoring, and interaction analysis. Use when you need the complete workflow, not just DiffDock.
• denovo-design: For generating new molecules (not docking). Use diffdock afterwards to dock generated molecules.

Running DiffDock on Modal (Recommended for openscience)

Use Modal for on-demand GPU access without local GPU setup.

Prerequisites

# Verify Modal credentials (auto-injected by openscience)
[ -n "$MODAL_TOKEN_ID" ] && echo "MODAL_TOKEN_ID set" || echo "NOT SET"
[ -n "$MODAL_TOKEN_SECRET" ] && echo "MODAL_TOKEN_SECRET set" || echo "NOT SET"