Drkg

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

Query the DRKG (Drug Repurposing Knowledge Graph). Use whenever the user asks about drug–gene, drug–disease, gene–disease, or other biomedical entity relationships in a knowledge-graph context, drug repurposing candidates, COVID-19 drug repurposing, or wants to explore neighbours of any biomedical entity (compound, gene, disease, pathway, side effect, etc.) in DRKG.From its SKILL.md

Install

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

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SKILL.md

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DRKG Query Skill

Search the Drug Repurposing Knowledge Graph (97 238 entities, 5 874 261 triplets, 107 relation types) by any entity. Auto-detects entity type by input pattern and returns all KG neighbours.

Entity Format

Entities in DRKG are typed strings: Type::ID.

Entity Type	Example	Count
Compound	`Compound::DB00945`	24 313
Gene	`Gene::1956`	39 220
Disease	`Disease::DOID:162`	5 103
Anatomy	`Anatomy::UBERON:0001474`	400
Biological Process	`Biological Process::GO:0006915`	11 381
Cellular Component	`Cellular Component::GO:0005634`	1 391
Molecular Function	`Molecular Function::GO:0005515`	2 884
Pathway	`Pathway::PC7_8078`	1 822
Pharmacologic Class	`Pharmacologic Class::N0000175605`	345
Side Effect	`Side Effect::C0000737`	5 701
Symptom	`Symptom::D009325`	415
Atc	`Atc::N02BE01`	4 048
Tax	`Tax::9606`	215

Relations are typed strings: Source::RelType::HeadType:TailType, e.g. DRUGBANK::target::Compound:Gene, Hetionet::CtD::Compound:Disease.

Input Auto-Detection

Input Pattern	Detected As	Match Logic
`Compound::DB00945`	full DRKG entity	exact match
`DB\d{5,}`	DrugBank ID	prepend `Compound::`
`DOID:\d+` / `MESH:D\d+`	Disease ID	prepend `Disease::`
`GO:\d+`	GO term	try BP / MF / CC
pure digits (`1956`)	Entrez Gene ID	prepend `Gene::`
anything else	free text	case-insensitive substring across all entities

API

Function	Input	Returns
`search(query, limit=200)`	single entity string	dict with `resolved`, `as_head`, `as_tail`
`search_batch(queries, limit=200)`	list of entity strings	dict[query → result]
`get_sources(entity)`	resolved DRKG entity	source attribution string
`get_relation_info(relation)`	relation string	glossary dict
`entity_types()`	—	list of 13 entity type names
`summarize(result)`	search result dict	compact LLM-readable text
`to_json(result)`	search result dict	JSON-serialisable dict

Usage

See if __name__ == "__main__" block in 25_DRKG.py for runnable examples: single-entity search (full ID, bare ID, free text), batch search, relation glossary lookup, and JSON output.

Data Sources

DRKG integrates six databases plus COVID-19 literature:

Source	Triplets	Coverage
DrugBank	1 424 790	drug–drug, drug–gene, drug–disease, ATC
Hetionet	2 250 197	gene–gene, anatomy, pathways, side effects
GNBR	335 369	gene–gene, compound–gene, disease–gene
STRING	1 496 708	protein–protein interactions
IntAct	256 151	protein–protein interactions
DGIdb	26 290	drug–gene interactions
Bibliography	84 756	COVID-19 related

Data Files

Located at DATA_DIR in 25_DRKG.py:

File	Description
`drkg.tsv`	5 874 261 triplets (head, relation, tail)
`relation_glossary.tsv`	relation type glossary with source info
`entity2src.tsv`	entity → original data-source mapping

Citation

Ioannidis et al. "DRKG - Drug Repurposing Knowledge Graph for Covid-19", 2020.
https://github.com/gnn4dr/DRKG

What ships with it: 5 files

16.8 KB alongside SKILL.md, 4 of them executable

drkg_skill.pyruns4.6 KB
example.pyruns10.0 KB
__init__.pyruns122 B
README.md1.3 KB
retrieve.pyruns787 B