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diffdock

Predict protein-ligand binding poses and confidence scores with diffusion models.

Updated Mar 10, 2026
Search Tags:#virtual screening#drug discovery#molecular docking#diffusion models#cheminformatics#protein-ligand interaction
One-click install
npx skills add https://github.com/felixboehm/biochem-allergy --skill diffdock-felixboehm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: diffdock
Source: https://github.com/felixboehm/biochem-allergy/tree/main/.claude/skills/diffdock
Command: npx skills add https://github.com/felixboehm/biochem-allergy --skill diffdock-felixboehm

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates the prediction of how small molecules bind to protein targets, a critical step in drug discovery and chemical biology.

Core Features & Use Cases

  • Molecular Docking: Predicts 3D binding poses of ligands to proteins using diffusion models.
  • Virtual Screening: Screens large compound libraries against a target protein to identify potential drug candidates.
  • Use Case: A medicinal chemist needs to understand where a new drug candidate might bind to a target protein. They can use this Skill to generate plausible binding poses and assess their likelihood.

Quick Start

Use the diffdock skill to dock the ligand with SMILES 'CC(=O)Oc1ccccc1C(=O)O' to the protein defined in 'protein.pdb'.

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?▼

Predict protein-ligand binding poses by providing a target protein PDB file and a ligand SMILES string to generate 3D binding poses and confidence scores using diffusion models.

Can I use a protein sequence instead of a PDB file for molecular docking?▼

Yes, you can perform molecular docking using a protein sequence by generating the required 3D structure via ESMFold, allowing structure-based drug design without a pre-existing PDB file.

What ligand formats are supported for virtual screening and molecular docking?▼

Molecular docking and virtual screening support small molecule ligands provided as SMILES strings, SDF files, or MOL2 files to predict binding poses.

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

Yes, diffusion-based molecular docking requires PyTorch, PyTorch Geometric, RDKit, PyTorch Lightning, and ESM to operate and predict protein-ligand interactions.

What is the best way to screen compound libraries against a target protein?▼

Screen compound libraries against a target protein by using diffusion-based deep learning models to predict 3D binding poses and confidence scores, facilitating structure-based virtual screening.

How does structure-based drug design identify small molecule binding sites?▼

Structure-based drug design identifies small molecule binding sites by predicting plausible 3D binding poses of ligands to proteins and assessing their likelihood with confidence scores.

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