bio-diffdock

Name: bio-diffdock
Availability: InStock
Author: biomaps-infra

Predict molecular docking poses for protein-ligand interactions using diffusion models.

Updated Mar 13, 2026

Search Tags:#bioinformatics #virtual screening #drug discovery #molecular docking #cheminformatics #protein-ligand interaction

Ready to run

npx skills add https://github.com/biomaps-infra/blender-opencode --skill bio-diffdock

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bio-diffdock
Source: https://github.com/biomaps-infra/blender-opencode/tree/main/.opencode/skills/bio-diffdock
Command: npx skills add https://github.com/biomaps-infra/blender-opencode --skill bio-diffdock

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the prediction of how small molecules (ligands) bind to proteins, a crucial step in drug discovery and understanding biological interactions.

Core Features & Use Cases

Predict Binding Poses: Determine the 3D structure of ligand-protein interactions.
Virtual Screening: Screen large libraries of compounds to identify potential drug candidates.
Structure-Based Design: Aid in optimizing lead compounds by predicting their binding modes.
Use Case: You have a target protein structure and a library of 1,000 potential drug molecules. Use this Skill to predict the binding pose of each molecule to the protein, identifying the most promising candidates for further study.

Quick Start

Use the bio-diffdock skill to predict the binding pose of the ligand 'CC(=O)Oc1ccccc1C(=O)O' to the protein structure 'target_protein.pdb'.

Frequently Asked Questions about bio-diffdock

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

FAQPage Schema

How do I predict protein-ligand binding poses for structure-based drug design?▼

Predict protein-ligand binding poses by using diffusion models to calculate the 3D molecular docking interactions. This approach automates structure-based drug design and virtual screening by processing PDB files or protein sequences to generate accurate ligand poses and confidence scores.

Can I run virtual screening for a large library of compounds using PDB files?▼

Yes, you can run virtual screening by inputting a target protein structure as a PDB file alongside a library of potential drug molecules. The system processes these inputs to predict the binding pose of each molecule, identifying the most promising candidates for further study.

Do I need PyTorch with CUDA and RDKit to predict molecular docking poses?▼

Yes, predicting molecular docking poses requires Python, PyTorch with CUDA, RDKit, and ESM. These dependencies are necessary to run the diffusion models for accurate pose generation and to calculate binding confidence scores.

What is the best way to screen drug candidates when I only have a protein sequence?▼

When you only have a protein sequence, you can still predict molecular docking poses for drug candidate screening. The system utilizes ESM to process protein sequences directly, enabling binding site prediction and ligand interaction analysis without a complete PDB structure.

Does diffusion model-based molecular docking support cheminformatics workflows?▼

Diffusion model-based molecular docking supports cheminformatics workflows by predicting 3D protein-ligand binding poses and calculating confidence scores. It integrates with cheminformatics processes like virtual screening and lead compound optimization to identify potential drug candidates.