AutoDock Vina Molecular Docking

AutoDock Vina Molecular Docking

Overview

AutoDock Vina is one of the fastest and most widely used open-source molecular docking engines for predicting protein–ligand binding modes and affinities. This skill covers the full Python-based pipeline: receptor preparation from PDB, ligand preparation from SMILES/SDF via Meeko and RDKit, search box definition, docking execution, pose analysis, and batch virtual screening for hit identification.

When to Use

• Predicting binding poses of small molecules to a protein target
• Estimating relative binding affinities (kcal/mol) for ligand ranking
• Virtual screening of compound libraries against a target receptor
• Validating docking protocols by re-docking co-crystallized ligands
• Preparing docking inputs from SMILES strings without intermediate files
• Comparing binding modes of analogs in a structure-activity relationship study
• Generating starting poses for molecular dynamics simulations
• Use DiffDock instead for blind docking when the binding site is unknown; use GNINA as an alternative with CNN scoring

Prerequisites

• Python packages: vina, meeko, rdkit, prody (for PDB fetch), py3Dmol (for visualization)
• External tools: ADFR Suite (provides prepare_receptor for PDBQT conversion) — download from https://ccsb.scripps.edu/adfr/downloads/
• Data requirements: Protein structure (PDB file or PDB ID), ligand(s) as SMILES, SDF, or MOL2
• Environment: Python 3.8+, Linux or macOS recommended

pip install vina meeko rdkit-pypi prody py3Dmol
# ADFR Suite must be installed separately for prepare_receptor

Workflow

Step 1: Fetch and Prepare the Receptor

Download the protein structure, remove water/heteroatoms, and convert to PDBQT format.

import prody
import subprocess
from pathlib import Path