68 · KEGG Drug
SkillProductivity🦀 Agentic RAG for drug intelligence · 57 skills · 15 task categories · DTI · ADR · DDI · PGx · Repurposing · Powered by LangGraph
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Then ask your AI: use the 68 · KEGG Drug skill
What this skill tells your AI
The instructions your AI receives, as published by qsong-github/drugclaw in skills/ddi/kegg_drug/SKILL.md and read by ahel’s review.
Approved drugs — structures, targets, pathways & drug-drug interactions Category: Drug-centric | Type: DB | Subcategory: DDI API:
https://rest.kegg.jp(free, no key required for academic use)
| Resource | URL |
|---|---|
| Homepage | https://www.genome.jp/kegg/ |
| API docs | https://www.kegg.jp/kegg/docs/keggapi.html |
| Paper | https://academic.oup.com/nar/article/38/suppl_1/D355/3112250 |
What it provides
- Drug metadata: name, formula, molecular weight, efficacy, class
- Targets: gene/protein targets for each approved drug
- Interactions (DDI): drug-drug interaction annotations
- Pathways: linked KEGG pathway IDs
Quick start
from 68_KEGG_Drug import query
# Single entity
results = query("aspirin")
# Multiple entities
results = query(["aspirin", "metformin", "imatinib"])
# By KEGG Drug ID
results = query("D00109")
# Specific fields only
results = query("warfarin", fields="targets")
results = query("warfarin", fields="interactions")
query() interface
query(entities, fields="all") -> list[dict]
| Parameter | Type | Description |
|---|---|---|
entities | str | list[str] | Drug name(s) or KEGG Drug ID(s) (e.g. "D00109") |
fields | str | "all" — full entry; "targets" — targets only; "interactions" — DDI only |
Return structure (fields="all")
[
{
"drug_id": "dr:D00109",
"query": "aspirin",
"name": "Aspirin (JP18/USP/INN); ...",
"formula": "C9H8O4",
"mol_weight": "180.0423",
"targets": ["PTGS1 ...", "PTGS2 ..."],
"interactions": ["Warfarin [precaution] ...", ...],
"pathways": ["map07112 ...", ...],
"classes": ["Analgesic ...", ...]
}
]
If a name cannot be resolved, the entry contains {"query": "xxx", "error": "No match found"}.
Lower-level functions
| Function | Input | Output | Description |
|---|---|---|---|
search(query, limit=10) | drug name/keyword | list[{id, name}] | Keyword search |
get_entry(drug_id) | KEGG Drug ID | dict | Full parsed entry |
get_targets(drug_id) | KEGG Drug ID | list[str] | Target lines |
get_interactions(drug_id) | KEGG Drug ID | list[str] | DDI lines |
Notes
- KEGG REST API is free for academic use; commercial use requires a license.
- Rate limit: no official cap, but keep requests reasonable (~1 req/sec).
- Drug IDs look like
D00109ordr:D00109; both formats accepted. - Not all drugs have interaction or target annotations — empty list means no data.
Signals
- GitHub stars
- 116
- Forks
- 3
- Last commit
- Aug 2026
ahel review
K1binfo
installs-packages (in kegg_drug_skill.py)K1binfo
installs-packages (in README.md)
Automated review, not a security audit. Ruleset v1+k2.
Advanced
- Item type
- skill
- Key
kegg-drug- Source
- github.com/qsong-github/drugclaw
github.com/qsong-github/drugclaw
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