Ligand Preparation

Goal

To prepare small-molecule ligands for molecular docking and downstream analysis by:

optionally enumerating relevant ligand ionization states and tautomers,
generating 3D conformers with RDKit ETKDG (via MCP),
minimizing with MMFF94/UFF (via MCP),
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):

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:

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