Automate end-to-end molecular docking simulations using AutoDock VINA, SMILES strings, and protein PDB structures.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install molecular-docking-autodock
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install molecular-docking-autodock using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Molecular Docking Automation (AutoDock VINA)?
This molecular docking skill provides a comprehensive automation pipeline for protein-ligand binding predictions. By utilizing Openclaw Skills, researchers and developers can bypass the tedious manual processing of structural files. The skill handles everything from receptor cleaning to ligand 3D conformation generation, ensuring a seamless transition from raw chemical data to docked complex structures.
At its core, the skill integrates the industry-standard AutoDock VINA engine. It supports flexible input methods, including standard PDB files for proteins and SMILES expressions for small molecules. The inclusion of intelligent pocket identification allows the agent to interpret natural language descriptions or residue-based coordinates to define the binding site, making high-throughput screening more accessible within an AI-driven environment.
Molecular Docking Automation (AutoDock VINA) Use Cases
- Predicting the binding mode and affinity between a target protein and a potential drug candidate.
- Screening a library of small molecule SMILES for high-scoring ligands against a specific binding pocket.
- Generating 3D complex structures (Protein + Ligand) for further molecular dynamics or structural analysis.
- Automating the identification of binding sites using residues or natural language descriptions via p2rank.
How Molecular Docking Automation (AutoDock VINA) Works
- Input Validation: The skill verifies the integrity of the PDB protein file and the chemical validity of the provided SMILES string.
- Binding Pocket Resolution: It parses the target area based on either coordinate ranges or text-based descriptions, using p2rank for automated pocket prediction if necessary.
- Protein Preprocessing: It automatically removes water molecules and non-standard residues, adds hydrogens, calculates charges, and converts the file to PDBQT format.
- Ligand Preprocessing: It generates the optimal 3D conformation from the SMILES input, calculates Gasteiger charges, and prepares the ligand PDBQT file.
- VINA Docking Execution: The skill runs the docking simulation with specified exhaustiveness and pose limits.
- Result Synthesis: It exports the top-scoring complex structure and a detailed affinity scoring table for analysis.
Molecular Docking Automation (AutoDock VINA) Setup
Ensure the required scientific environment is configured by installing the following dependencies:
conda install -c conda-forge autodock-vina pymol-open-source rdkit openbabel
pip install meeko biopython
For automated pocket prediction, install p2rank:
wget https://github.com/rdk/p2rank/releases/download/2.4/p2rank_2.4.tar.gz
tar -xzf p2rank_2.4.tar.gz && export PATH=$PATH:$(pwd)/p2rank_2.4
Molecular Docking Automation (AutoDock VINA) Data Schema & Taxonomy
The skill organizes docking outputs within a structured directory for easy retrieval and analysis:
| File Type | Description |
|---|---|
*.pdbqt |
Intermediate prepared files for both protein and ligand. |
complex_topN.pdb |
The final docked complex structure (protein + ligand). |
scores.csv |
A table containing binding affinity scores (kcal/mol) for all poses. |
visualization.png |
(Optional) Generated images of the docking result. |
Molecular Docking Automation (AutoDock VINA) Advanced Features
- Natural language binding site identification using p2rank integration for zero-coordinate pocket targeting.
- Customizable exhaustiveness settings to balance simulation speed and docking accuracy.
- Multi-pose output support allowing for the analysis of up to 9 distinct binding构象.
- Fully automated PDB cleaning and hydrogenation for high-consistency receptor preparation using Openclaw Skills.
SKILL.md
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