What is drug-ligand-prep?

A small-molecule preparation tool that creates 3D structures suitable for molecular docking, which predicts how a molecule may fit into a protein pocket. It can also generate alternative protonation and tautomer forms, optimize their geometry, and export SDF and PDBQT files.

Use it to prepare one or many ligands, enumerate relevant chemical states, generate 3D conformers, minimize their geometry, and create AutoDock Vina-ready files.

Raw molecule names or SMILES strings may not contain the 3D shape and chemical state needed by docking software. This prepares those inputs in a repeatable way.

drug-ligand-prep — Skill for Claude Code and Codex by learningmatter-mit: A small-molecule preparation tool that creates 3D structures suita

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods

npx agentmods add skills/learningmatter-mit/atomisticskills/drug-ligand-prep

Any agent

npx skills add learningmatter-mit/AtomisticSkills --skill drug-ligand-prep

Clone the repo

git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/drug-ligand-prep.svg)](https://agentmods.dev/skills/learningmatter-mit/atomisticskills/drug-ligand-prep)

Your own site

<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/drug-ligand-prep"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/drug-ligand-prep.svg" alt="Measured on agentmods" height="20"></a>

Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 726 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5	$0.00048	$0.00726
Opus 5	$0.00024	$0.00363
Sonnet 5	$0.00010	$0.00145
Haiku 4.5	$0.00005	$0.00073

Measured 5d ago against content hash f0c7bc734f06, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

drug-ligand-prep scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/prepare_ligand.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/drug-ligand-prep/SKILL.md · 87 lines

How it starts

The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 87 lines

Files

What ships with it

10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

5d ago First seen · 87 lines · 48 tokens per session scan A f0c7bc734f06

Subscribe to this mod's changes

drug-ligand-prep is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (160 stars, last pushed 2d ago), licensed MIT. It adds 48 tokens to every session and 726 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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