Pharmkg

Skill BioTender-max/awesome-bio-agent-skills/skills/drugclaw/pharmkg

A curated collection of AI agent skills for biomedical research, covering genomics, proteomics, single-cell analysis, clinical AI, and protein design.

Install

npx -y skills add BioTender-max/awesome-bio-agent-skills --skill pharmkg

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.

What its author says it does

Copied from the file, not written here

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.

SKILL.md

2.0 KB, as published. Nobody here has run it

PharmKG Query Skill

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