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Agent skill

Drug Trajectory Analysis

by learningmatter-mit in learningmatter-mit/AtomisticSkills

Analyze a protein-ligand MD trajectory to compute ligand RMSD, pocket RMSF, hydrogen bonds, contact occupancy, and protein-ligand interaction fingerprints over time.

MITAuto-check passed

Install Drug Trajectory Analysis

skills CLI
$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-trajectory-analysis -a claude-code

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

GitHub CLI
$ gh skill install learningmatter-mit/AtomisticSkills drug-trajectory-analysis --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-trajectory-analysis .claude/skills/drug-trajectory-analysis && 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-trajectory-analysis
GitHub stars
175
Token cost
~1.2k tokens
SKILL.md length
459 words
Files
15 (incl. scripts)
Skills in repo
129
Repo updated
First seen
Licence
MIT

At a glance

Analyze a protein-ligand MD trajectory to compute ligand RMSD, pocket RMSF, hydrogen bonds, contact occupancy, and protein-ligand interaction fingerprints over time.

  • Works in 5 steps: Prepare inputs → Run trajectory analysis → Output files → …
  • SKILL.md covers Goal, Instructions, Examples and Constraints, plus 1 more section
  • Runs Python scripts from its folder

What it does

Drug Trajectory Analysis is an agent skill from learningmatter-mit/AtomisticSkills. Analyze a protein-ligand MD trajectory to compute ligand RMSD, pocket RMSF, hydrogen bonds, contact occupancy, and protein-ligand interaction fingerprints over time.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 19 other files, including scripts (for example `examples/hiv1-protease/README.md`, `examples/hiv1-protease/analysis/analysis_summary.json` and `scripts/analyze_trajectory.py`).

The repository describes itself as: Integrating AtomisticSkills into Agentic IDEs (Cursor, Claude Code, Codex, Google Antigravity, Hermes Agent, etc). The licence is MIT.

Example prompts

  • “/drug-trajectory-analysis”

Requirements

  • Python 3

Workflow steps

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

  1. Prepare inputs
  2. Run trajectory analysis
  3. Output files
  4. Interpret results
  5. Quick single-metric check

What it can do on your machine

Read from SKILL.md and the folder at commit 7f2d86d. 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 Trajectory Analysis loads about 1.2k tokens when it runs. Until then it costs about 48 tokens; SKILL.md has 459 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~48
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

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 7f2d86d, republished under its MIT licence (© learningmatter-mit). 459 words, ~1,249 tokens.

Download SKILL.mdSave it as .claude/skills/drug-trajectory-analysis/SKILL.md (or your agent's skills folder). This skill also uses 14 other files; get the full folder from GitHub.
name
drug-trajectory-analysis
description
Analyze a protein-ligand MD trajectory to compute ligand RMSD, pocket RMSF, hydrogen bonds, contact occupancy, and protein-ligand interaction fingerprints over time.
metadata.category
drug-discovery
metadata.venv
cpu

drug-trajectory-analysis

Goal

To extract quantitative binding-mode descriptors from a protein-ligand MD trajectory, producing:

  • Ligand heavy-atom RMSD (pose stability)
  • Ligand center-of-mass drift
  • Binding-pocket residue RMSF (pocket flexibility)
  • Hydrogen bond persistence
  • Key contact occupancy
  • Protein-ligand interaction fingerprints (IFPs) over time

These outputs feed directly into go/no-go decisions about pose validity and can be used to compare refinement trajectories across compounds.

Instructions

1. Prepare inputs

Required:

2. Run trajectory analysis
bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/analyze_trajectory.py \
  --topology md/system/complex_solvated.pdb \
  --trajectory md/run/production.dcd \
  --ligand_resname UNL \
  --pocket_cutoff 5.0 \
  --output_dir md/analysis/

Key parameters:

  • --ligand_resname: residue name of the ligand in the topology (default: UNL). Check the solvated PDB if unsure.
  • --pocket_cutoff: distance cutoff in Angstroms for defining pocket residues around the ligand in the first frame (default: 5.0).
  • --skip_frames: skip the first N frames as equilibration (default: 0).
  • --snapshots: render PyMOL binding pocket snapshots at 4 timepoints (requires pymol-open-source).
3. Output files

The script produces:

  • md/analysis/ligand_rmsd.csv: per-frame ligand heavy-atom RMSD (Angstroms)
  • md/analysis/ligand_com.csv: per-frame ligand COM relative to protein backbone COM
  • md/analysis/pocket_rmsf.csv: per-residue RMSF of pocket residues (Angstroms)
  • md/analysis/hbonds.csv: hydrogen bond donor-acceptor pairs and occupancy fractions
  • md/analysis/contacts.csv: residue-level contact occupancy fractions
  • md/analysis/interaction_fingerprints.csv: per-frame binary IFP matrix (requires ProLIF)
  • md/analysis/analysis_summary.json: summary statistics
  • md/analysis/plots/: directory with PNG plots (RMSD time series, COM drift, RMSF bar chart, contact occupancy, PyMOL binding pocket snapshots)
4. Interpret results

Key indicators of a stable binding pose:

  • Ligand RMSD: should plateau below 2-3 A for a stable pose. Persistent drift above 3 A suggests the ligand is leaving the pocket or adopting an alternative binding mode.
  • COM drift: large monotonic drift indicates ligand unbinding.
  • Pocket RMSF: identifies flexible vs. rigid pocket regions. High RMSF (>2 A) at key contact residues may indicate induced fit.
  • H-bond persistence: critical hydrogen bonds should have >50% occupancy for a well-resolved interaction.
  • IFP consistency: stable binding modes show consistent fingerprint patterns across the trajectory.
Show full SKILL.md (157 more words)Show less
5. Quick single-metric check

For a fast assessment, check only ligand RMSD:

bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/analyze_trajectory.py \
  --topology md/system/complex_solvated.pdb \
  --trajectory md/run/production.dcd \
  --ligand_resname UNL \
  --rmsd_only \
  --output_dir md/analysis/

Examples

Example: full analysis of TYK2 inhibitor trajectory
bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/scripts/analyze_trajectory.py \
  --topology tyk2/md/system/complex_solvated.pdb \
  --trajectory tyk2/md/run/production.dcd \
  --ligand_resname UNL \
  --pocket_cutoff 5.0 \
  --skip_frames 10 \
  --output_dir tyk2/md/analysis/

Constraints

  • Environment: Requires cpu (includes MDAnalysis and ProLIF).
  • Trajectory format: DCD is the default from the MD skill. PDB trajectories and XTC are also supported by MDAnalysis.
  • Ligand residue name: must match the name used in the topology PDB. OpenMM often assigns UNL to non-standard residues.
  • ProLIF requirement: interaction fingerprints require ProLIF. If ProLIF is not installed, the script skips IFP computation and logs a warning.
  • Memory: large trajectories (>10k frames) may require significant memory for IFP computation. Use --skip_frames or --stride to downsample.

References

  • Michaud-Agrawal, N.; Denning, E. J.; Woolf, T. B.; Beckstein, O. MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations. J. Comput. Chem. 2011, 32, 2319-2327. https://doi.org/10.1002/jcc.21787
  • Bouysset, C.; Fiorucci, S. ProLIF: a Library to Encode Molecular Interactions as Fingerprints. J. Cheminform. 2021, 13, 72. https://doi.org/10.1186/s13321-021-00548-6

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 14 other files (scripts) in skills/drug-trajectory-analysis of learningmatter-mit/AtomisticSkills.

  • SKILL.md
  • examples/hiv1-protease/README.md
  • examples/hiv1-protease/analysis/analysis_summary.json
  • examples/hiv1-protease/analysis/contacts.csv
  • examples/hiv1-protease/analysis/hbonds.csv
  • examples/hiv1-protease/analysis/interaction_fingerprints.csv
  • examples/hiv1-protease/analysis/ligand_com.csv
  • examples/hiv1-protease/analysis/ligand_rmsd.csv
  • examples/hiv1-protease/analysis/plots/binding_snapshots.png
  • examples/hiv1-protease/analysis/plots/contacts.png
  • examples/hiv1-protease/analysis/plots/ligand_com.png
  • examples/hiv1-protease/analysis/plots/ligand_rmsd.png
  • examples/hiv1-protease/analysis/plots/pocket_rmsf.png
  • examples/hiv1-protease/analysis/pocket_rmsf.csv
  • scripts/analyze_trajectory.py

Open the folder on GitHubat commit 7f2d86d

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Questions about Drug Trajectory Analysis

What does Drug Trajectory Analysis do?

Analyze a protein-ligand MD trajectory to compute ligand RMSD, pocket RMSF, hydrogen bonds, contact occupancy, and protein-ligand interaction fingerprints over time. Drug Trajectory Analysis is an agent skill from learningmatter-mit/AtomisticSkills. Analyze a protein-ligand MD trajectory to compute ligand RMSD, pocket RMSF, hydrogen bonds, contact occupancy, and protein-ligand interaction fingerprints over time.

How do I install Drug Trajectory Analysis in Claude Code?

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

How do I install Drug Trajectory Analysis in Codex?

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

Can I use Drug Trajectory Analysis 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-trajectory-analysis -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-trajectory-analysis, .gemini/skills/drug-trajectory-analysis, .github/skills/drug-trajectory-analysis and .opencode/skills/drug-trajectory-analysis in your project.

What does Drug Trajectory Analysis need to run?

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

Does Drug Trajectory Analysis 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 Trajectory Analysis 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 Trajectory Analysis use?

Drug Trajectory Analysis 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 Trajectory Analysis use?

About 1.2k tokens (SKILL.md is roughly 5k 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 Trajectory Analysis?

Skills that share tags, products or a category with Drug Trajectory Analysis: Ito Compute (affaan-m/ECC, 274k stars), Senior Computer Vision (davila7/claude-code-templates, 32k stars), Senior Computer Vision (alirezarezvani/claude-skills, 28k stars) and GCP Compute (sickn33/agentic-awesome-skills, 47k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Drug Trajectory Analysis?

learningmatter-mit (a GitHub organization) maintains it in learningmatter-mit/AtomisticSkills, which has 175 GitHub stars. The repository holds 129 skills in this directory. The repository was last updated on October 6, 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.