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drug-ligand-prep

Prepare small-molecule ligands for docking by enumerating states, generating conformers, and exporting SDF and PDBQT.

144|21|Updated Jan 8, 2026
Search Tags:#drug discovery#rdkit#autodock vina#conformer generation#ligand preparation#pdbqt export#protonation state enumeration
One-click install
npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill drug-ligand-prep
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: drug-ligand-prep
Source: https://github.com/learningmatter-mit/AtomisticSkills/tree/main/.agents/skills/drug-ligand-prep
Command: npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill drug-ligand-prep

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires rdkit, meeko, dimorphite_dl, and includes scripts (resource) components.

What problem does it solve?

This Skill removes the repetitive, error-prone work of turning raw ligand inputs (SMILES/SDF) into docking-ready 3D conformations and structure files, so researchers can move faster to screening and analysis.

Core Features & Use Cases

  • Optional state enumeration: Enumerates protonation states (and optionally tautomers) to produce chemically relevant ligand variants.
  • Reproducible 3D conformer generation: Generates multiple conformers using RDKit ETKDG via MCP.
  • Docking-ready exports: Produces an optimized SDF and an AutoDock-Vina PDBQT suitable for downstream docking workflows.
  • MMFF94/UFF minimization: Minimizes conformers with MMFF94 (falling back to UFF) to select a lowest-energy structure.

Use case: You have a list of candidate ligands as SMILES and want a batch of docking-ready PDBQT files for a small-molecule screening campaign, including physiologically plausible protonation states.

Quick Start

Prepare docking-ready ligand states and PDBQT from a SMILES file by running the ligand preparation script on your inputs with enumeration enabled and an output directory for the generated SDF and PDBQT files.

Frequently Asked Questions about drug-ligand-prep

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

FAQPage Schema
How do I convert SMILES to AutoDock Vina PDBQT files for virtual screening?▼

You convert SMILES to AutoDock Vina PDBQT files by generating 3D conformers via RDKit and delegating final PDBQT generation to a conversion tool. This workflow exports optimized SDF and PDBQT files suitable for virtual screening campaigns.

What is ligand preparation and protonation state enumeration in drug discovery?▼

Ligand preparation normalizes raw SMILES or SDF inputs into 3D conformations, while protonation state enumeration generates physiologically plausible ligand variants. This ensures chemically relevant structures for accurate downstream docking and virtual screening.

Does RDKit conformer generation with MMFF94 minimization work for large ligand batches?▼

RDKit conformer generation with MMFF94 minimization supports batch conversion from SMILES or SDF inputs for virtual screening. It embeds multiple conformers using ETKDG, minimizes them with MMFF94 falling back to UFF, and selects the lowest-energy structure.

Can I generate tautomers and protonation states before exporting PDBQT for docking?▼

Yes, you can optionally enumerate tautomers and protonation states before exporting PDBQT files for docking. This generates chemically relevant ligand variants from SMILES inputs, ensuring final AutoDock Vina PDBQT outputs reflect physiologically plausible structures.

Why does my ligand preparation workflow fail to export valid PDBQT files?▼

PDBQT export failures during ligand preparation often occur when 3D conformer embedding or MMFF94 minimization fails on invalid SMILES inputs. The workflow requires a deterministic preparation environment and delegates final PDBQT generation to a specific conversion tool.

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