PharmKG Query Skill

SkillDev tools

Query the PharmKG knowledge graph (180k entities, 39 relation types, >1M triples). Use whenever the user asks about biomedical relationships among genes, drugs/chemicals, and diseases, e.g. drug, gene interactions, drug, disease associations, gene, disease links, or drug, drug relationships derived from literature and curated databases.

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the PharmKG Query Skill skill

What this skill tells your AI

The instructions your AI receives, as published by qsong-github/drugclaw in skills/drug_knowledgebase/pharmkg/SKILL.md and read by ahel’s review.

Search PharmKG triples by entity name. Matching is case-insensitive; exact match is tried first via a prebuilt index, with substring fallback.

Input Example	Matches On
`aspirin`	exact on Entity1_name or Entity2_name
`BRCA1`	exact / substring on entity names
`Alzheimer Disease`	substring on entity names

Entity & Relation Types

Entities (~188 k): Drug/Chemical (DrugBank, ChEMBL), Gene/Protein (Entrez, UniProt), Disease (DO, MeSH).

Relations (39 types): chemical–gene (inhibition, activation, binding …), chemical–disease (treatment, marker, risk factor), gene–disease (association, marker), chemical–chemical (similarity, interaction), and others.

API

Function	Input	Returns
`load_pharmkg(path)`	CSV path	`list[dict]` of triples
`_build_index(triples)`	triple list	`dict[str, list]` (entity→triples)
`search(triples, entity, index=)`	entity string	`list[dict]`
`search_batch(triples, entities, index=)`	list of strings	`dict[str, list]`
`summarize(hits, entity)`	hits + label	compact text
`to_json(hits)`	hits	JSON string

Usage

See if __name__ == "__main__" block in 55_PharmKG.py for runnable examples covering: single entity search, batch search, summarize, and JSON output.

Data

Source: raw_PharmKG-180k.csv (comma-separated)
Columns: Entity1_name, relationship_type, Entity2_name
Scale: ~188 k entities, 39 relation types, >1 M triples
Path: DATA_PATH variable in 55_PharmKG.py
Paper: Zheng et al., Briefings in Bioinformatics 22(4), 2021. DOI: 10.1093/bib/bbaa344

Signals

GitHub stars: 116
Forks: 3
Last commit: Aug 2026

Advanced

Item type: skill
Key: pharmkg-query
Source: github.com/qsong-github/drugclaw

github.com/qsong-github/drugclaw