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Agent skill

Drug Protein Ligand Md

by learningmatter-mit in learningmatter-mit/AtomisticSkills

Run a protein-ligand MD simulation in OpenMM with energy minimization, restrained equilibration, and production NPT, producing trajectory and checkpoint files for downstream analysis.

MITAuto-check passedResearch & Science

Install Drug Protein Ligand Md

skills CLI
$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-protein-ligand-md -a claude-code

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

GitHub CLI
$ gh skill install learningmatter-mit/AtomisticSkills drug-protein-ligand-md --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-protein-ligand-md .claude/skills/drug-protein-ligand-md && 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/

Facts

Skill name
drug-protein-ligand-md
GitHub stars
176
Token cost
~1.4k tokens
SKILL.md length
467 words
Files
4 (incl. scripts)
Skills in repo
129
Repo updated
First seen
Licence
MIT

At a glance

Run a protein-ligand MD simulation in OpenMM with energy minimization, restrained equilibration, and production NPT, producing trajectory and checkpoint files for downstream analysis.

  • Works in 5 steps: Prepare inputs → Run the simulation → Output files → …
  • Research & Science work in your project
  • SKILL.md covers Goal, Instructions, Examples and Constraints, plus 1 more section
  • Runs Python scripts from its folder

What it does

Drug Protein Ligand Md is an agent skill from learningmatter-mit/AtomisticSkills. Run a protein-ligand MD simulation in OpenMM with energy minimization, restrained equilibration, and production NPT, producing trajectory and checkpoint files for downstream analysis.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts (for example `examples/hiv1-protease/README.md`, `examples/hiv1-protease/run/md_provenance.json` and `scripts/run_md.py`).

It sits in Research & Science. 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

  • Research & Science work in your project

Example prompts

  • “/drug-protein-ligand-md”

Requirements

  • Python 3

Workflow steps

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

  1. Prepare inputs
  2. Run the simulation
  3. Output files
  4. Running replicates
  5. Quick validation checks

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):

    • doi.org
    • 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 Protein Ligand Md loads about 1.4k tokens when it runs. Until then it costs about 52 tokens; SKILL.md has 467 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~52
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

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). 467 words, ~1,448 tokens.

Download SKILL.mdSave it as .claude/skills/drug-protein-ligand-md/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
drug-protein-ligand-md
description
Run a protein-ligand MD simulation in OpenMM with energy minimization, restrained equilibration, and production NPT, producing trajectory and checkpoint files for downstream analysis.
metadata.category
drug-discovery
metadata.venv
cpu

drug-protein-ligand-md

Goal

To run a complete protein-ligand molecular dynamics simulation using OpenMM, starting from a system bundle produced by drug-complex-system-builder. The workflow includes:

  1. Energy minimization
  2. NVT equilibration with positional restraints on heavy atoms
  3. NPT equilibration with restraints gradually released
  4. NPT production run

The output is a DCD trajectory + final state checkpoint suitable for drug-trajectory-analysis.

Instructions

1. Prepare inputs

Required from drug-complex-system-builder:

  • system.xml: serialized OpenMM System
  • complex_solvated.pdb: solvated complex PDB (used as topology reference)
2. Run the simulation
bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu+openmm python ${CLAUDE_SKILL_DIR}/scripts/run_md.py \
  --system_xml md/system/system.xml \
  --input_pdb md/system/complex_solvated.pdb \
  --temperature 300 \
  --pressure 1.0 \
  --timestep 4.0 \
  --minimize_steps 5000 \
  --equil_nvt_steps 25000 \
  --equil_npt_steps 50000 \
  --production_steps 2500000 \
  --restraint_k 50.0 \
  --reporting_interval 5000 \
  --checkpoint_interval 25000 \
  --output_dir md/run/

Key parameters:

  • --temperature: simulation temperature in Kelvin (default: 300).
  • --pressure: target pressure in atm (default: 1.0).
  • --timestep: integration timestep in fs (default: 4.0). 4 fs is safe with hydrogen mass repartitioning (HMR) from the system builder; use 2 fs without HMR.
  • --minimize_steps: max minimization steps (default: 5000). Set to 0 to skip.
  • --equil_nvt_steps: NVT equilibration steps with restraints on protein/ligand heavy atoms (default: 25000 = 100 ps at 4 fs).
  • --equil_npt_steps: NPT equilibration steps with restraints released (default: 50000 = 200 ps).
  • --production_steps: production NPT steps (default: 2500000 = 10 ns at 4 fs).
  • --restraint_k: restraint force constant for equilibration in kJ/mol/nm^2 (default: 50.0).
  • --reporting_interval: write trajectory frame every N steps (default: 5000 = 20 ps).
  • --checkpoint_interval: write checkpoint every N steps (default: 25000).
3. Output files

The script produces:

  • md/run/minimized.pdb: structure after energy minimization
  • md/run/nvt_equilibration.log: energy/temperature log during NVT equilibration
  • md/run/npt_equilibration.log: energy/temperature/density log during NPT equilibration
  • md/run/production.dcd: production trajectory (DCD format)
  • md/run/production.log: production energy/temperature/density log
  • md/run/final_state.xml: serialized simulation state for restarts
  • md/run/md_provenance.json: all simulation parameters and timing
4. Running replicates

For statistical confidence, run multiple independent replicates with different random seeds:

bash
for i in 1 2 3; do
  ${CLAUDE_SKILL_DIR}/../../venv/run cpu+openmm python ${CLAUDE_SKILL_DIR}/scripts/run_md.py \
    --system_xml md/system/system.xml \
    --input_pdb md/system/complex_solvated.pdb \
    --production_steps 2500000 \
    --seed $((42 + i)) \
    --output_dir md/rep_${i}/
done
5. Quick validation checks

After the run, verify:

  • Temperature fluctuates around the target (check production.log)
  • Density is stable around 1.0 g/cm^3 for aqueous systems
  • Total energy does not drift monotonically
  • The ligand remains in the binding pocket (use drug-trajectory-analysis)
Show full SKILL.md (169 more words)Show less

Examples

Example: 10 ns production MD of TYK2 complex
bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu+openmm python ${CLAUDE_SKILL_DIR}/scripts/run_md.py \
  --system_xml tyk2/md/system/system.xml \
  --input_pdb tyk2/md/system/complex_solvated.pdb \
  --temperature 300 \
  --timestep 4.0 \
  --production_steps 2500000 \
  --output_dir tyk2/md/run/
Example: short 1 ns refinement for pose assessment
bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu+openmm python ${CLAUDE_SKILL_DIR}/scripts/run_md.py \
  --system_xml md/system/system.xml \
  --input_pdb md/system/complex_solvated.pdb \
  --production_steps 250000 \
  --reporting_interval 2500 \
  --output_dir md/short_refine/

Constraints

  • Environment: Requires cpu+openmm.
  • GPU acceleration: The script auto-detects CUDA GPUs. Without a GPU, simulations will run on CPU (significantly slower; consider reducing production_steps for testing).
  • Timestep: 4 fs requires hydrogen mass repartitioning (HMR) in the system. The drug-complex-system-builder applies HMR by default. If using a system without HMR, set --timestep 2.0.
  • Trajectory size: DCD files grow ~1 MB per 1000 frames for a typical 50k-atom system. A 10 ns run at 20 ps intervals produces ~500 frames (~500 MB).
  • Restarts: use --restart_from with a saved state XML to continue a simulation.

References

  • Eastman, P.; Galvelis, R.; Pelaez, R. P.; et al. OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials. J. Phys. Chem. B 2024, 128(1), 109-116. https://doi.org/10.1021/acs.jpcb.3c06662
  • Hopkins, C. W.; Le Grand, S.; Walker, R. C.; Roitberg, A. E. Long-Time-Step Molecular Dynamics through Hydrogen Mass Repartitioning. J. Chem. Theory Comput. 2015, 11, 1864-1874. https://doi.org/10.1021/ct5010406

Author: Matthew Cox Contact: GitHub @mcox3406

© learningmatter-mit, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts) in skills/drug-protein-ligand-md of learningmatter-mit/AtomisticSkills.

  • SKILL.md
  • examples/hiv1-protease/README.md
  • examples/hiv1-protease/run/md_provenance.json
  • scripts/run_md.py

Open the folder on GitHubat commit 6257444

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Categories

Questions about Drug Protein Ligand Md

What does Drug Protein Ligand Md do?

Run a protein-ligand MD simulation in OpenMM with energy minimization, restrained equilibration, and production NPT, producing trajectory and checkpoint files for downstream analysis. Drug Protein Ligand Md is an agent skill from learningmatter-mit/AtomisticSkills. Run a protein-ligand MD simulation in OpenMM with energy minimization, restrained equilibration, and production NPT, producing trajectory and checkpoint files for downstream analysis.

When should I use Drug Protein Ligand Md?

Drug Protein Ligand Md fits situations like: research & Science work in your project.

How do I install Drug Protein Ligand Md in Claude Code?

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

How do I install Drug Protein Ligand Md in Codex?

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

Can I use Drug Protein Ligand Md 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-protein-ligand-md -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-protein-ligand-md, .gemini/skills/drug-protein-ligand-md, .github/skills/drug-protein-ligand-md and .opencode/skills/drug-protein-ligand-md in your project.

What does Drug Protein Ligand Md need to run?

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

Does Drug Protein Ligand Md access the network?

SKILL.md names 2 domains. As links in the text: doi.org and github.com. This is read from the text; nothing was executed.

Is Drug Protein Ligand Md 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 Protein Ligand Md use?

Drug Protein Ligand Md 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 Protein Ligand Md use?

About 1.4k tokens (SKILL.md is roughly 5.8k 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 Protein Ligand Md?

Skills that share tags, products or a category with Drug Protein Ligand Md: Hypothesis Generation (spacering-net/codeg, 3.9k stars), GitHub Deep Research (bytedance/deer-flow, 84k stars), Nature Paper Card (Yuan1z0825/nature-skills, 47k stars) and Content Research Writer (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Drug Protein Ligand Md?

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.