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autodock-vina-docking

Perform molecular docking with AutoDock Vina to predict protein-ligand binding poses and affinities.

298|27|Updated Feb 18, 2026
Search Tags:#virtual screening#drug discovery#molecular docking#cheminformatics#computational chemistry#protein-ligand interaction
One-click install
npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill autodock-vina-docking
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: autodock-vina-docking
Source: https://github.com/jaechang-hits/SciAgent-Skills/tree/main/skills/structural-biology-drug-discovery/autodock-vina-docking
Command: npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill autodock-vina-docking

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires vina, meeko, rdkit-pypi, prody, py3Dmol, numpy, pandas, subprocess, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the complex process of molecular docking, enabling researchers to predict how small molecules (ligands) bind to proteins and estimate their binding strength, accelerating drug discovery.

Core Features & Use Cases

  • Receptor & Ligand Preparation: Converts PDB and SMILES/SDF into formats suitable for docking.
  • Binding Site Definition: Identifies or allows manual specification of the docking search space.
  • Docking Execution: Runs AutoDock Vina to generate multiple binding poses and their energies.
  • Result Analysis: Extracts binding energies, allows RMSD calculation for validation, and visualizes results.
  • Batch Screening: Enables high-throughput virtual screening of compound libraries.
  • Use Case: A medicinal chemist wants to screen a library of 1000 compounds against a newly identified cancer target protein. This Skill can automate the docking of all compounds, rank them by predicted binding affinity, and identify the top potential drug candidates for further experimental testing.

Quick Start

Use the autodock-vina-docking skill to prepare the receptor from '1HPV.pdb', prepare the ligand from SMILES 'CC(C)(C)NC(=O)[C@@H]1CN(CCc2ccccc2)C[C@H]1O', and run docking with 10 poses.

Frequently Asked Questions about autodock-vina-docking

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

FAQPage Schema
How do I run molecular docking using a PDB receptor and SMILES ligand?▼

Molecular docking with a PDB receptor and SMILES ligand requires converting both structures into PDBQT format. This workflow automates 3D coordinate generation and format conversion via RDKit and Meeko, then executes AutoDock Vina to predict binding poses.

Can I do virtual screening for a compound library using AutoDock Vina?▼

Virtual screening of compound libraries using AutoDock Vina is fully supported through batch processing. The workflow automates ligand preparation, executes docking simulations across the compound set, and ranks candidates by predicted binding affinity.

What is needed to prepare protein-ligand complexes for molecular docking?▼

Preparing protein-ligand complexes for molecular docking requires PDB files for receptors and SMILES or SDF formats for ligands. The process converts these inputs into PDBQT files using RDKit and Meeko for 3D coordinate generation and format compatibility.

How do I validate docking results using RMSD and binding energies?▼

Validating docking results using RMSD and binding energies involves extracting the predicted affinity values and calculating root mean square deviation against reference poses. The workflow includes recipes for re-docking validation to ensure pose prediction accuracy.

Does this docking workflow support flexible receptor docking?▼

Flexible receptor docking is supported within the AutoDock Vina execution workflow. It allows specified receptor side chains to be treated as flexible during the simulation, alongside standard rigid receptor docking and scoring-only evaluations.

Can I visualize docked protein-ligand complexes directly after the simulation?▼

Visualizing docked protein-ligand complexes is handled directly using py3Dmol. The analysis step automatically renders the docked poses and receptor structures, allowing immediate inspection of the predicted binding modes.

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