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diffdock

Predict 3D binding poses of small molecule ligands to protein targets using diffusion models.

2|Updated Jun 4, 2026
Search Tags:#bioinformatics#drug discovery#chemistry#docking#diffusion#proteomics
One-click install
npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill diffdock-lord1egypt
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: diffdock
Source: https://github.com/Lord1Egypt/scientific-agent-toolkit/tree/main/scientific-skills/diffdock
Command: npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill diffdock-lord1egypt

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires torch, torch-geometric, rdkit, biopython, pytorch-lightning, pyyaml, fair-esm, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This skill addresses the computational bottleneck in drug discovery by providing an automated, high-accuracy method to predict how small molecules bind to protein targets.

Core Features & Use Cases

  • Molecular Docking: Predicts 3D binding poses for protein-ligand complexes using state-of-the-art diffusion models.
  • Virtual Screening: Enables high-throughput screening of compound libraries against specific protein targets.
  • Use Case: Researchers can use this to identify potential lead compounds for a specific protein receptor by docking thousands of candidates and ranking them by confidence score.

Quick Start

Use the diffdock skill to predict the binding pose for the protein in receptor.pdb and the ligand defined by the SMILES string CC(=O)Oc1ccccc1C(=O)O.

Frequently Asked Questions about diffdock

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I predict protein-ligand binding poses using diffusion models?▼

Virtual screening with diffusion models supports high-throughput docking of large compound libraries against a specific protein target. You rank thousands of ligand candidates by their predicted confidence scores to identify lead compounds.

What input formats do I need for molecular docking predictions?▼

Molecular docking predictions require a protein structure file in PDB format and a small molecule ligand defined by a SMILES string. These inputs feed into the diffusion model for 3D complex generation.

Do I need PyTorch and RDKit to run diffusion-based docking?▼

Diffusion-based docking uses deep learning to model the generative process of ligand binding, contrasting with traditional physics-based scoring. This approach predicts 3D poses by progressively denoising molecular configurations.

Can I use ESM protein sequence folding for large-scale virtual screening?▼

ESM is utilized for protein sequence folding to prepare receptor structures for virtual screening. This enables large-scale campaigns to dock thousands of candidates against specific protein targets and rank them by confidence.

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