What is drug-mmpbsa-gbsa?

A scientific analysis skill that estimates how strongly a drug-like molecule may bind to a protein using molecular-dynamics simulation data. MM-GBSA and MM-PBSA are calculation methods that compare the simulated energy of the complete protein–molecule system with its separate parts.

Use it to rescore compounds from a trajectory, compare binding estimates, and choose between a faster GB calculation or a PB calculation with energy breakdowns.

It provides an additional way to rank docked molecules after simulation, alongside docking scores and structural stability, without claiming an exact experimental binding value.

drug-mmpbsa-gbsa — Skill for Claude Code and Codex by learningmatter-m

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.

Any agent

npx skills add learningmatter-mit/AtomisticSkills --skill drug-mmpbsa-gbsa

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-mmpbsa-gbsa/github.svg)](https://agentmods.dev/skills/learningmatter-mit/atomisticskills/drug-mmpbsa-gbsa)

Your own site

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

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/drug-mmpbsa-gbsa"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/drug-mmpbsa-gbsa.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 86 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 4,967 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector warn 7 Sept 2026

SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

medium analysis-evasion · line 1
Suspicious Unicode normalization or mixed-script content

Fix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.

How audits are shown

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 5.1	$0.00086	$0.04967
Opus 5	$0.00043	$0.02483
Sonnet 5	$0.00017	$0.00993
Haiku 4.5	$0.00009	$0.00497

Measured 10d ago against content hash 07d9896d340a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

drug-mmpbsa-gbsa 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 10d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/compute_mmgbsa.py, scripts/compute_mmpbsa.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-mmpbsa-gbsa/SKILL.md · 233 lines

How it starts

The opening of the file, as written. The whole thing — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.

drug-mmpbsa-gbsa (MM-GBSA / MM-PBSA)

Goal

To estimate relative binding free energies from MD trajectories using the single-trajectory MM-GBSA / MM-PBSA approach. For each trajectory frame, the method strips explicit solvent, evaluates potential energies of the complex, receptor, and ligand subsystems in implicit solvent, and computes:

dG = E_complex - E_receptor - E_ligand

The entropy term (-TdS) is omitted, which is standard practice when the goal is relative ranking rather than absolute binding affinity. MM-GBSA / MM-PBSA is most useful for re-ranking docked poses after MD refinement, providing an orthogonal signal to docking scores and geometric stability metrics.

Choosing a backend

Path	Script	When to use	Extras
OpenMM GBn2 (fast)	`compute_mmgbsa.py`	Throughput rescoring of HTVS hits; everything stays inside OpenMM with the same force field as the MD	No extra dependencies; ~1-5 minutes per compound on CPU
AmberTools MMPBSA.py	`compute_mmpbsa.py`	When you need PB (not just GB), per-method decomposition (ELE, VDW, EGB / EPB, ESURF), or a setup that matches what reviewers expect from the MM-PBSA literature	Adds `MMPBSA.py`, `cpptraj`, and `parmed` to the dependency surface (already in `drugmd-agent`); ~1-3 minutes for GB, ~5-30 minutes for PB depending on system size and frame count

Both paths give comparable GB rankings for typical drug-protein systems, but the absolute dG numbers will differ across backends because they use different GB models, radius sets, and surface-area treatments. Don't compare numbers across the two scripts.

Instructions

1. Basic usage (protein + ligand, short HTVS-style MD)

For the 1-5 ns production runs typical in the HTVS workflow:

# Env: drugmd-agent
python .agents/skills/drug-mmpbsa-gbsa/scripts/compute_mmgbsa.py \
  --topology md/system/complex_solvated.pdb \
  --trajectory md/run/production.dcd \
  --ligand_sdf md/ligand.sdf \
  --ligand_resname UNL \
  --skip_ns 0.5 \
  --stride 5 \
  --output_dir md/mmgbsa/

Read the full file on GitHub · 233 lines

Files

What ships with it

25 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.

10d ago First seen · 233 lines · 86 tokens per session scan A 07d9896d340a

Subscribe to this mod's changes

drug-mmpbsa-gbsa is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (163 stars, last pushed 6d ago), licensed MIT. It adds 86 tokens to every session and 4,967 once invoked, about $0.0004 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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drug-mmpbsa-gbsa