OREGANO Query Skill
SkillDev toolsQuery the OREGANO knowledge graph for computational drug repurposing. Use whenever the user asks about drug, target, disease, gene, pathway relationships, compound cross-references, drug repurposing hypotheses, or wants to explore neighbors of any biomedical entity in a knowledge graph that includes natural compounds.
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 OREGANO Query Skill skill
What this skill tells your AI
The instructions your AI receives, as published by qsong-github/drugclaw in skills/drug_repurposing/oregano/SKILL.md and read by ahel’s review.
Search the OREGANO knowledge graph (88,937 nodes, 824,231 links) by any entity. Auto-resolves input to OREGANO node IDs via cross-reference tables.
| Input Pattern | Detected As | Match Logic |
|---|---|---|
OREGANO internal ID (e.g. 1234) | OREGANO node ID | exact in triplet index |
DB00331 / DrugBank ID | external xref | exact in COMPOUND.tsv |
| UniProt / KEGG / MeSH / UMLS ID | external xref | exact across all metadata TSVs |
metformin, BRCA1, free text | entity name | substring on name columns |
API
| Function | Input | Returns |
|---|---|---|
search(query) | single entity string | dict: {query, resolved_ids, metadata, triplets} |
search_batch(queries) | list of entity strings | dict[str, search_result] |
summarize(result) | search result dict | compact LLM-readable text |
to_json(result) | search result dict | JSON-serializable dict |
get_stats() | — | graph-level counts (triplets, nodes, predicates, entity types) |
Graph Schema
11 node types: Compound (90,868), Gene (35,794), Target (22,096), Disease (18,333), Phenotype (11,605), Side Effect (6,060), Indication (2,714), Pathway (2,129), Effect (171), Activity (78).
19 relation types (predicates): e.g. has_target, has_indication, has_side_effect, interacts_with, involved_in_pathway, associated_with, has_phenotype, etc. Run get_stats() to list all predicates with counts.
Usage
See if __name__ == "__main__" block in 21_OREGANO.py for runnable examples covering: free-text drug name search, DrugBank ID lookup, batch search, JSON pipeline output, and graph statistics.
Data
- Source: Zenodo DOI 10.5281/zenodo.10103842 (CC-BY 4.0)
- Version: v2.1 (published 2023-11-10)
- Core file:
OREGANO_V2.1.tsv— tab-delimited triplets (Subject, Predicate, Object) - Metadata files:
COMPOUND.tsv,TARGET.tsv,GENES.tsv,DISEASES.tsv,PHENOTYPES.tsv,PATHWAYS.tsv,INDICATION.tsv,SIDE_EFFECT.tsv,ACTIVITY.tsv,EFFECT.tsv - Path:
DATA_DIRvariable in21_OREGANO.py
Citation
Boudin, M., Diallo, G., Drancé, M. & Mougin, F. The OREGANO knowledge graph for computational drug repurposing. Sci Data 10, 871 (2023). https://doi.org/10.1038/s41597-023-02757-0
Signals
- GitHub stars
- 116
- Forks
- 3
- Last commit
- Aug 2026
Advanced
- Item type
- skill
- Key
oregano-query- Source
- github.com/qsong-github/drugclaw
github.com/qsong-github/drugclaw
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