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diffdock

Predict 3D binding poses and confidence scores for small-molecule ligands against protein targets using DiffDock software.

13|3|Updated Jun 10, 2026
Search Tags:#molecular docking#confidence scoring#protein-ligand interaction#structure-based drug design#binding pose prediction
One-click install
npx skills add https://github.com/tassiovale/claude-code-kit --skill diffdock-tassiovale
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: diffdock
Source: https://github.com/tassiovale/claude-code-kit/tree/main/skills/diffdock
Command: npx skills add https://github.com/tassiovale/claude-code-kit --skill diffdock-tassiovale

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires diffdock, python:3.9, rdkit, pytorch, pyg, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill predicts binding poses of small molecule ligands to protein targets and provides confidence scores for assessment.

Core Features & Use Cases

  • Protein-Ligand Pose Prediction: Predicts 3D binding poses of small molecule ligands to protein targets.
  • Confidence Score Generation: Generates confidence scores to assess prediction reliability.
  • Use Case: For structure-based drug design, where understanding the 3D pose of a ligand in a protein is crucial.

Quick Start

Use the diffdock skill to predict the binding pose of a ligand with SMILES string "CC(=O)Oc1ccccc1C(=O)O" against a protein PDB file "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 3D ligand binding poses for protein targets?▼

To predict protein-ligand binding poses, provide a protein PDB file and ligand SMILES string. The Skill calculates 3D binding poses and generates confidence scores to evaluate prediction reliability.

What is confidence scoring in molecular docking and how is it used?▼

Confidence scoring in molecular docking estimates the reliability of predicted ligand binding poses. The Skill outputs confidence scores to help researchers evaluate protein-ligand interaction prediction quality for structure-based drug design.

Do I need PyTorch and RDKit to run DiffDock for binding pose prediction?▼

Yes, running DiffDock requires a Python 3.9 environment with PyTorch, PyG, and RDKit installed. Optional CUDA GPU acceleration is supported to significantly speed up protein-ligand binding pose prediction.

Can I use SMILES strings and PDB files for structure-based drug design?▼

Yes, use ligand SMILES strings and protein PDB files as inputs to predict 3D binding poses. This workflow directly supports structure-based drug design and molecular biology research by generating binding pose predictions.

What is the best way to assess protein-ligand interaction prediction reliability?▼

Assess protein-ligand interaction prediction reliability by reviewing the generated confidence scores. These scores evaluate the accuracy of predicted 3D binding poses for small molecule ligands bound to specific protein targets.

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