Drug Ligand Prep
Prepare small-molecule ligands for docking and analysis via optional state enumeration, 3D conformer generation, MMFF/UFF minimization, and export to SDF + AutoDock PDBQT.
Install
$ agentstack add skill-learningmatter-mit-atomisticskills-drug-ligand-prep ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
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Ligand Preparation
Goal
To prepare small-molecule ligands for molecular docking and downstream analysis by: 1) optionally enumerating relevant ligand ionization states and tautomers, 2) generating 3D conformers with RDKit ETKDG (via MCP), 3) minimizing with MMFF94/UFF (via MCP), 4) exporting a docking-ready PDBQT (AutoDock-Vina) and an optimized SDF (via MCP).
This skill combines script-based state enumeration with MCP-based 3D generation to ensure reproducibility.
Instructions
1. Enumerate States (Optional Batch Processing)
Use the script to process SMILES/SDF files and enumerate protonation/tautomer states. This outputs 2D SDFs.
# Env: drugdisc-agent
python .agents/skills/drug-ligand-prep/scripts/prepare_ligand.py \
--smiles_file ligands.smi \
--enumerate_protomers \
--output_dir ligand_states/2. Generate 3D Conformer and PDBQT (using MCP)
Use the mcp_drugdisc_convert_to_pdbqt tool to generate the final 3D docking input.
From a single SMILES:
mcp_drugdisc_convert_to_pdbqt(
input_data="CC(=O)Oc1ccccc1C(=O)O",
input_type="smiles",
output_path="aspirin.pdbqt",
num_confs=50
)From an SDF (e.g. output of Step 1):
mcp_drugdisc_convert_to_pdbqt(
input_data="ligand_states/ligand_001.sdf",
input_type="sdf",
output_path="ligand_001.pdbqt",
num_confs=20
)Examples
Prepare Ibuprofen
- Enumerate inputs (if needed):
``bash python .agents/skills/drug-ligand-prep/scripts/prepare_ligand.py \ --smiles "CC(C)Cc1ccc(cc1)[C@@H](C)C(=O)O" \ --name ibuprofen \ --output_dir prep_stages/ ``
- Generate PDBQT:
``bash mcp_drugdisc_convert_to_pdbqt( input_data="prep_stages/ibuprofen.sdf", input_type="sdf", output_path="prep_stages/ibuprofen.pdbqt", num_confs=50 ) ``
Constraints
- Environment: Requires
drugdisc-agent. - 3D/PDBQT: Delegated to
mcp_drugdisc_convert_to_pdbqt(Meeko/RDKit). - State Enumeration: The script handles batch enumeration of protonation/tautomer states, but 3D generation is done by the MCP tool.
Author: Matthew Cox Contact: GitHub @mcox3406
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: learningmatter-mit
- Source: learningmatter-mit/AtomisticSkills
- License: MIT
- Homepage: https://arxiv.org/abs/2605.24002
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.