drug-discovery

drug-discovery is a skill for Claude Code, Codex from MilkyWay008/Hermes-OTG. It costs 84 tokens per session (2,804 once invoked), scanned A, a copy of drug-discovery, MIT.

A research assistant for discovering and evaluating drug compounds during pharmaceutical development. It searches bioactivity records, measures common drug-likeness properties, checks drug interactions, and helps interpret ADMET data, which describes how a drug is absorbed, distributed, metabolized, excreted, and tolerated.

In plain words
What is it for?
Use it to search ChEMBL for compounds and targets, assess lead candidates, review drug–drug interactions, interpret ADMET profiles, and guide lead optimization.
Why use it?
It brings several early-stage medicinal-chemistry checks into one workflow instead of requiring separate lookups and calculations.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Good fit Use it to search ChEMBL for compounds and targets, assess lead candidates, review drug–drug interactions, interpret ADMET profiles, and guide lead optimization.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/milkyway008/hermes-otg/drug-discovery
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 MilkyWay008/Hermes-OTG --skill drug-discovery
Clone the repo
git clone --depth 1 https://github.com/MilkyWay008/Hermes-OTG

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.

agentmods badge for drug-discovery

README.md
[![agentmods](https://agentmods.dev/badge/skills/milkyway008/hermes-otg/drug-discovery/github.svg)](https://agentmods.dev/skills/milkyway008/hermes-otg/drug-discovery)
Your own site
<a href="https://agentmods.dev/skills/milkyway008/hermes-otg/drug-discovery"><img src="https://agentmods.dev/badge/skills/milkyway008/hermes-otg/drug-discovery/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.

agentmods 80×15 button for drug-discovery

Your own site · 80×15
<a href="https://agentmods.dev/skills/milkyway008/hermes-otg/drug-discovery"><img src="https://agentmods.dev/badge/skills/milkyway008/hermes-otg/drug-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 84 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,804 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin 84% copy Near-identical to another mod 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 5.1 $0.00084 $0.02804
Opus 5 $0.00042 $0.01402
Sonnet 5 $0.00017 $0.00561
Haiku 4.5 $0.00008 $0.00280

Measured 9d ago against content hash b7eb32a28367, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

drug-discovery scanned grade A with 1 finding 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 9d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/chembl_target.py, scripts/ro5_screen.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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

commands: [curl, python3]
Origin

This is a copy

84% identical to drug-discovery — 36 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

data/skills/research/drug-discovery/SKILL.md · 228 lines

How it starts

The opening of the file, as written. The whole thing — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Drug Discovery & Pharmaceutical Research

You are an expert pharmaceutical scientist and medicinal chemist with deep knowledge of drug discovery, cheminformatics, and clinical pharmacology. Use this skill for all pharma/chemistry research tasks.

Core Workflows

1 — Bioactive Compound Search (ChEMBL)

Search ChEMBL (the world's largest open bioactivity database) for compounds by target, activity, or molecule name. No API key required.

# Search compounds by target name (e.g. "EGFR", "COX-2", "ACE")
TARGET="$1"
ENCODED=$(python3 -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$TARGET")
curl -s "https://www.ebi.ac.uk/chembl/api/data/target/search?q=${ENCODED}&format=json" \
  | python3 -c "
import json,sys
data=json.load(sys.stdin)
targets=data.get('targets',[])[:5]
for t in targets:
    print(f\"ChEMBL ID : {t.get('target_chembl_id')}\")
    print(f\"Name      : {t.get('pref_name')}\")
    print(f\"Type      : {t.get('target_type')}\")
    print()
"
# Get bioactivity data for a ChEMBL target ID
TARGET_ID="$1"   # e.g. CHEMBL203
curl -s "https://www.ebi.ac.uk/chembl/api/data/activity?target_chembl_id=${TARGET_ID}&pchembl_value__gte=6&limit=10&format=json" \
  | python3 -c "
import json,sys
data=json.load(sys.stdin)
acts=data.get('activities',[])
print(f'Found {len(acts)} activities (pChEMBL >= 6):')
for a in acts:
    print(f\"  Molecule: {a.get('molecule_chembl_id')}  |  {a.get('standard_type')}: {a.get('standard_value')} {a.get('standard_units')}  |  pChEMBL: {a.get('pchembl_value')}\")
"
# Look up a specific molecule by ChEMBL ID
MOL_ID="$1"   # e.g. CHEMBL25 (aspirin)
curl -s "https://www.ebi.ac.uk/chembl/api/data/molecule/${MOL_ID}?format=json" \
  | python3 -c "
import json,sys
m=json.load(sys.stdin)
props=m.get('molecule_properties',{}) or {}
print(f\"Name       : {m.get('pref_name','N/A')}\")
print(f\"SMILES     : {m.get('molecule_structures',{}).get('canonical_smiles','N/A') if m.get('molecule_structures') else 'N/A'}\")
print(f\"MW         : {props.get('full_mwt','N/A')} Da\")
print(f\"LogP       : {props.get('alogp','N/A')}\")
print(f\"HBD        : {props.get('hbd','N/A')}\")
print(f\"HBA        : {props.get('hba','N/A')}\")
print(f\"TPSA       : {props.get('psa','N/A')} Ų\")
print(f\"Ro5 violations: {props.get('num_ro5_violations','N/A')}\")
print(f\"QED        : {props.get('qed_weighted','N/A')}\")
"

Read the full file on GitHub · 228 lines

Files

What ships with it

3 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. 9d ago First seen · 228 lines · 84 tokens per session scan A b7eb32a28367

Subscribe to this mod's changes

drug-discovery is a skill published in the GitHub repository MilkyWay008/Hermes-OTG (15 stars, last pushed 28d ago), licensed MIT. It adds 84 tokens to every session and 2,804 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 84% identical to drug-discovery, differing in 36 lines, and is treated as a copy.

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