Agent skill

Drug Ligand Prep

by learningmatter-mit in learningmatter-mit/AtomisticSkills

Prepare small-molecule ligands for docking and analysis via optional state enumeration, 3D conformer generation, MMFF/UFF minimization, and export to SDF + AutoDock PDBQT.

MITAuto-check passedResearch & Science

Install Drug Ligand Prep

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-ligand-prep -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-ligand-prep --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/drug-ligand-prep .claude/skills/drug-ligand-prep && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "drug-ligand-prep" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-ligand-prep into .claude/skills/drug-ligand-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-ligand-prep", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-ligand-prep

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

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

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-ligand-prep --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/drug-ligand-prep .agents/skills/drug-ligand-prep && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "drug-ligand-prep" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-ligand-prep into .agents/skills/drug-ligand-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-ligand-prep", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

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

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-ligand-prep --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/drug-ligand-prep .cursor/skills/drug-ligand-prep && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "drug-ligand-prep" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-ligand-prep into .cursor/skills/drug-ligand-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-ligand-prep", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/learningmatter-mit/AtomisticSkills.git --path skills/drug-ligand-prep

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-ligand-prep -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-ligand-prep --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/drug-ligand-prep .gemini/skills/drug-ligand-prep && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "drug-ligand-prep" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-ligand-prep into .gemini/skills/drug-ligand-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-ligand-prep", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-ligand-prep

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-ligand-prep -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/drug-ligand-prep .github/skills/drug-ligand-prep && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "drug-ligand-prep" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-ligand-prep into .github/skills/drug-ligand-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-ligand-prep", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

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

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-ligand-prep --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/drug-ligand-prep .opencode/skills/drug-ligand-prep && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "drug-ligand-prep" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-ligand-prep into .opencode/skills/drug-ligand-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-ligand-prep", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: drug-ligand-prep
GitHub stars: 176
Token cost: ~772 tokens
SKILL.md length: 222 words
Files: 11 (incl. scripts)
Skills in repo: 129
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Prepare small-molecule ligands for docking and analysis via optional state enumeration, 3D conformer generation, MMFF/UFF minimization, and export to SDF + AutoDock PDBQT.

Works in 2 steps: Enumerate States (Optional Batch… → Generate 3D Conformer and PDBQT (using…
Tasks that involve Drug discovery and cheminformatics
SKILL.md covers Goal, Instructions, Examples and Constraints
Runs Python scripts from its folder

What it does

Drug Ligand Prep is an agent skill from learningmatter-mit/AtomisticSkills. Prepare small-molecule ligands for docking and analysis via optional state enumeration, 3D conformer generation, MMFF/UFF minimization, and export to SDF + AutoDock PDBQT.

Its SKILL.md is about 770 tokens, which your agent loads only when the skill is triggered. The skill folder holds 17 other files, including scripts (for example `examples/common_drugs/README.md`, `examples/common_drugs/output/preparation_summary.json` and `scripts/prepare_ligand.py`).

It sits in Research & Science, covering Drug discovery and cheminformatics. It works with Model Context Protocol. The repository describes itself as: Integrating AtomisticSkills into Agentic IDEs (Cursor, Claude Code, Codex, Google Antigravity, Hermes Agent, etc). The licence is MIT.

When your agent uses it

Tasks that involve Drug discovery and cheminformatics

Example prompts

“/drug-ligand-prep”

Requirements

Python 3

Workflow steps

2 steps, taken from the step headings in SKILL.md.

Enumerate States (Optional Batch Processing)
Generate 3D Conformer and PDBQT (using MCP)

What it can do on your machine

Read from SKILL.md and the folder at commit 6257444. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
- github.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Drug Ligand Prep loads about 772 tokens when it runs. Until then it costs about 47 tokens; SKILL.md has 222 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~47
When it runs · the whole SKILL.md, loaded when a task matches: ~772

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from learningmatter-mit/AtomisticSkills at commit 6257444, republished under its MIT licence (© learningmatter-mit). 222 words, ~772 tokens.

Download SKILL.mdSave it as .claude/skills/drug-ligand-prep/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.

name: drug-ligand-prep
description: Prepare small-molecule ligands for docking and analysis via optional state enumeration, 3D conformer generation, MMFF/UFF minimization, and export to SDF + AutoDock PDBQT.
metadata.category: drug-discovery
metadata.venv: cpu

Ligand Preparation

<!-- mcp-tools-note -->

[!NOTE] Steps written server.tool are MCP tool calls: drugdisc.convert_to_pdbqt is the convert_to_pdbqt tool of the drugdisc server (mcp__drugdisc__convert_to_pdbqt, or mcp__plugin_atomistic-skills_drugdisc__convert_to_pdbqt when installed as a plugin). Without a connected server, run the same tools from the shell. Tools named in one command share a process, so a model loaded by load_model stays loaded:
bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python -m src.mcp_server.cli drugdisc convert_to_pdbqt key=value

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.

bash

${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/prepare_ligand.py \
  --smiles_file ligands.smi \
  --enumerate_protomers \
  --output_dir ligand_states/

2. Generate 3D Conformer and PDBQT (using MCP)

Use the drugdisc.convert_to_pdbqt tool to generate the final 3D docking input.

From a single SMILES:

bash

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):

bash

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

${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/prepare_ligand.py \
  --smiles "CC(C)Cc1ccc(cc1)[C@@H](C)C(=O)O" \
  --name ibuprofen \
  --output_dir prep_stages/

Generate PDBQT:

bash

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 cpu.
3D/PDBQT: Delegated to 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

Files

SKILL.md and 10 other files (scripts) in skills/drug-ligand-prep of learningmatter-mit/AtomisticSkills.

SKILL.md
examples/common_drugs/README.md
examples/common_drugs/compounds.smi
examples/common_drugs/output/aspirin/aspirin.pdbqt
examples/common_drugs/output/aspirin/aspirin.sdf
examples/common_drugs/output/caffeine/caffeine.pdbqt
examples/common_drugs/output/caffeine/caffeine.sdf
examples/common_drugs/output/ibuprofen/ibuprofen.pdbqt
examples/common_drugs/output/ibuprofen/ibuprofen.sdf
examples/common_drugs/output/preparation_summary.json
scripts/prepare_ligand.py

Open the folder on GitHubat commit 6257444

Compare with similar skills

Drug Ligand Prep next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Drug Ligand Prep compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Drug Ligand Prep this skilllearningmatter-mit/AtomisticSkills	176	—	~772	Automated safety check: Pass	MIT	yesterday
Hcls Build Agentaws-samples/amazon-bedrock-agents-healthcare-lifesciences	274	—	~885	Automated safety check: Pass	MIT-0	7 days ago
Tooluniverseynulihao/AgentSkillOS	617	3 repos	~2.5k	Automated safety check: Pass	None	7 mo ago
Hcls Get Startedaws-samples/amazon-bedrock-agents-healthcare-lifesciences	274	—	~607	Automated safety check: Pass	MIT-0	7 days ago
Patsnap Chemistry Small Moleculepatsnap/mcp	112	—	~643	Automated safety check: Pass	Apache-2.0	1 mo ago
Chemgraphargonne-lcf/ChemGraph	162	—	~2.7k	Automated safety check: Pass	Apache-2.0	yesterday

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Questions about Drug Ligand Prep

What does Drug Ligand Prep do?

Prepare small-molecule ligands for docking and analysis via optional state enumeration, 3D conformer generation, MMFF/UFF minimization, and export to SDF + AutoDock PDBQT. Drug Ligand Prep is an agent skill from learningmatter-mit/AtomisticSkills. Prepare small-molecule ligands for docking and analysis via optional state enumeration, 3D conformer generation, MMFF/UFF minimization, and export to SDF + AutoDock PDBQT.

When should I use Drug Ligand Prep?

Drug Ligand Prep fits situations like: tasks that involve Drug discovery and cheminformatics.

How do I install Drug Ligand Prep in Claude Code?

Run `npx skills add learningmatter-mit/AtomisticSkills --skill drug-ligand-prep -a claude-code`. Or copy the skill folder (skills/drug-ligand-prep in learningmatter-mit/AtomisticSkills) into .claude/skills/drug-ligand-prep in your project. Claude Code loads it when a task matches its description.

How do I install Drug Ligand Prep in Codex?

Run `npx skills add learningmatter-mit/AtomisticSkills --skill drug-ligand-prep -a codex`. Or copy the skill folder (skills/drug-ligand-prep in learningmatter-mit/AtomisticSkills) into .agents/skills/drug-ligand-prep in your project. Codex loads it when a task matches its description.

Can I use Drug Ligand Prep in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add learningmatter-mit/AtomisticSkills --skill drug-ligand-prep -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/drug-ligand-prep, .gemini/skills/drug-ligand-prep, .github/skills/drug-ligand-prep and .opencode/skills/drug-ligand-prep in your project.

What does Drug Ligand Prep need to run?

Going by SKILL.md and its folder, Drug Ligand Prep needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Drug Ligand Prep access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Drug Ligand Prep safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Drug Ligand Prep use?

Drug Ligand Prep is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Drug Ligand Prep use?

About 772 tokens (SKILL.md is roughly 3.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Drug Ligand Prep?

Skills that share tags, products or a category with Drug Ligand Prep: Hcls Build Agent (aws-samples/amazon-bedrock-agents-healthcare-lifesciences, 274 stars), Tooluniverse (ynulihao/AgentSkillOS, 617 stars), Hcls Get Started (aws-samples/amazon-bedrock-agents-healthcare-lifesciences, 274 stars) and Patsnap Chemistry Small Molecule (patsnap/mcp, 112 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Drug Ligand Prep?

learningmatter-mit (a GitHub organization) maintains it in learningmatter-mit/AtomisticSkills, which has 176 GitHub stars. The repository holds 129 skills in this directory. The repository was last updated on October 7, 2026.

Source: learningmatter-mit/AtomisticSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Drug Ligand Prep

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Ligand Preparation

Goal

Instructions

1. Enumerate States (Optional Batch Processing)

2. Generate 3D Conformer and PDBQT (using MCP)

Examples

Prepare Ibuprofen

Constraints

Files

Compare with similar skills

Similar skills

More from learningmatter-mit/AtomisticSkills

Related

Questions about Drug Ligand Prep