DiffDock: Molecular Docking with Diffusion Models

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Last updated: April 10, 2026

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name: diffdock description: "Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction."

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 (

DiffDock: Molecular Docking with Diffusion Models

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Manual Installation

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name: diffdock description: "Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction."

DiffDock: Molecular Docking with Diffusion Models

Overview

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