How do I install the Drkg Query skill?

Run npx skillmds@latest add gabrielmoreira/drkg-query in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Drkg Query safe to use?

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Which AI agents work with Drkg Query?

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Is Drkg Query free to use?

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Who published Drkg Query?

gabrielmoreira (@gabrielmoreira) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Drkg Query by gabrielmoreira — Agent Skill

Name: Drkg Query
Rating: 4.5 (4 reviews)
Author: gabrielmoreira

Drkg Query

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.

gabrielmoreira Updated Sep 9, 2026 17 repo stars

File contents

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

---name: drkg-querydescription: 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.--- # 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 ```

Frequently asked questions