agentmods.dev

drug-docking-analysis

Analysis of virtual drug-screening docking results, which estimate how small molecules may fit a target protein. It calculates score distributions, ligand-efficiency measures, and enrichment statistics when labeled compounds are available.

In plain words
What is it for?
Analyzing ranked docking CSV files, plotting score and efficiency results, measuring ROC AUC and enrichment, and exporting results for later review.
Why use it?
It shows whether docking scores and rankings are useful for selecting compounds, while making clear that it does not verify whether the predicted poses are physically realistic.

Skill for Claude CodeCodex

154 8d ago A scan 32 tokens original MIT
View the source file learningmatter-mit/AtomisticSkills
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-docking-analysis
Any agent
npx skills add learningmatter-mit/AtomisticSkills --skill drug-docking-analysis
Clone the repo
git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills

Made for: Claude Code, Codex.

Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,051 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.

ModelPer sessionOnce invoked
Fable 5 $0.00032 $0.02051
Opus 5 $0.00016 $0.01026
Sonnet 5 $0.00006 $0.00410
Haiku 4.5 $0.00003 $0.00205

Measured 3d ago against content hash dc7139a1f051, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

drug-docking-analysis 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 3d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/analyze_docking.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-docking-analysis/SKILL.md · 121 lines

How it starts

The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.

drug-docking-analysis

Goal

To analyze virtual screening docking results by computing score distributions, ligand efficiency metrics, and (when labeled actives/inactives are available) enrichment statistics. This skill sits between drug-docking-vina and downstream refinement stages, providing quantitative assessment of docking campaign quality.

Important: this skill does not assess pose quality. A compound can receive an excellent Vina score with a physically implausible pose (internal clashes, strained torsions, mis-assigned bond orders). Always pair this analysis with drug-pose-validation (PoseBusters) before acting on the top-ranked compounds.

Outputs:

  • Score distribution KDE plot
  • Score vs. molecular weight scatter (visualizes Vina's size/lipophilicity bias)
  • Ligand efficiency (LE, BEI, SEI) distributions
  • ROC curve with AUC (when labels available)
  • Enrichment factor bar chart at 1%, 2%, 5%, 10%, 20% (when labels available)
  • Enriched results CSV with per-compound efficiency metrics

Instructions

0. Prerequisites: produce a docking_ranked.csv

This skill expects a ranked CSV produced by drug-docking-vina's collect_results.py. If you are starting from raw drug-docking-vina JSON output, run the collect step first:

# Env: drugdisc-agent
python .agents/skills/drug-docking-vina/scripts/collect_results.py \
  --results docking/results/docking_results.json \
  --library_csv library/library_master.csv \
  --output_dir docking/analysis/

The collect step joins the docking scores with the library CSV to pull SMILES, labels, and (when present) parent_compound_id / microstate_id columns. See drug-docking-vina SKILL.md step 5 for details.

1. Basic analysis (no labels)

When you have docking results but no active/inactive labels:

# Env: drugdisc-agent
python .agents/skills/drug-docking-analysis/scripts/analyze_docking.py \
  --docking_csv docking/docking_ranked.csv \
  --output_dir docking/analysis/

Read the full file on GitHub · 121 lines

Files

What ships with it

46 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.

  1. 3d ago First seen · 121 lines · 32 tokens per session scan A dc7139a1f051

Subscribe to this mod's changes

drug-docking-analysis is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (154 stars, last pushed 8d ago), licensed MIT. It adds 32 tokens to every session and 2,051 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.

Analytics and metricsScientificdrug-discovery
Related

Other skills, from other repositories

datamol

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters…

synthetic-sciences/openscience · 67 tokens

deepchem

Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first…

synthetic-sciences/openscience · 78 tokens

binding-affinity

Hybrid ML + physics binding affinity prediction. Empirical scoring, MM/GBSA rescoring, multi-method consensus, and batch virtual screening for protein-ligand complexes.

synthetic-sciences/openscience · 38 tokens

drug-design

End-to-end drug discovery pipeline orchestration. Deterministic Python script that auto-chains structure prediction, pocket detection, de novo design, docking, scoring, and ADMET filtering into reproducible workflows.

synthetic-sciences/openscience · 44 tokens

molecular-optimization

Iterative lead optimization with analyze-reason-generate-verify-evaluate loop. Paper-backed (MT-Mol, DrugR, MultiMol).

synthetic-sciences/openscience · 34 tokens

admet-reasoning

Interpretable ADMET analysis with mechanistic reasoning. Maps liabilities to structural causes and biological pathways. Based on CoTox (Park 2025) and DrugR (Liu 2026).

synthetic-sciences/openscience · 47 tokens