Diffusion-based molecular docking

# 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