KEGG Query

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name: kegg-query description: > Query KEGG database for drug information, pathway analysis, and disease-drug-target discovery. Use this skill when: (1) Looking up drug information including efficacy, targets, metabolism, and interactions, (2) Analyzing metabolic or signaling pathways to retrieve genes, compounds, and modules, (3) Discovering disease-associated drugs, genes, and pathways for drug repurposing. license: MIT category: knowledge-retrieval tags: [kegg, pathway, drug, disease, target, bioinformatics]

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KEGG Query

Query the KEGG (Kyoto Encyclopedia of Genes and Genomes) database for comprehensive biomedical information.

When to Use

Drug Lookup: Retrieve drug efficacy, targets, metabolism, drug-drug interactions

Pathway Analysis: Get pathway genes, compounds, modules, and related pathways

Disease Discovery: Find disease-associated drugs, genes, and therapeutic targets

Workflow

Use Case 1: Drug Information Lookup

Fetch comprehensive drug information from KEGG DRUG database.

from scripts.kegg_api import kegg_find, kegg_get, parse_drug_entry
Step 1: Search for drug by name
results = kegg_find("drug", "aspirin")
Returns: [("dr:D00109", "Aspirin (JP18/USP); Acetylsalicylic acid; ...")]
Step 2: Get full entry
drug_id = "dr:D00109"  # or just "D00109"
entry = kegg_get(drug_id)
drug_info = parse_drug_entry(entry)

Output includes: Names, formula, efficacy, diseases, targets, pathways, metabolism, DDI.

See examples/drug_lookup.py for complete implementation.

Use Case 2: Pathway Analysis

Analyze KEGG pathways to retrieve genes, compounds, and modules.

from scripts.kegg_api import kegg_get, parse_pathway_entry
Get pathway by ID (e.g., hsa00010 for Glycolysis)
entry = kegg_get("hsa00010")
pathway = parse_pathway_entry(entry)
Access parsed data
print(f"Genes: {len(pathway['genes'])}")      # 50+ genes
print(f"Compounds: {len(pathway['compounds'])}")  # 30+ compounds

Output includes: Description, genes with KO/EC annotations, compounds, modules, related pathways.

See examples/pathway_analysis.py for complete implementation.

Use Case 3: Disease-Drug-Target Discovery

Discover therapeutic targets and drugs for diseases.

from scripts.kegg_api import kegg_find, kegg_get, parse_disease_entry
Step 1: Search for disease
results = kegg_find("disease", "diabetes")
Returns multiple matches including Type 2 diabetes (H00409)
Step 2: Get disease details
entry = kegg_get("ds:H00409")
disease = parse_disease_entry(entry)
Access drugs and targets
print(f"Drugs: {len(disease['drugs'])}")    # 60+ drugs
print(f"Genes: {len(disease['genes'])}")    # 20+ genes

Output includes: Description, category, associated genes, pathways, approved drugs.

See examples/disease_discovery.py for complete implementation.

Expected Outputs

Drug Entry (JSON)

{
  "id": "D00109",
  "names": ["Aspirin", "Acetylsalicylic acid"],
  "formula": "C9H8O4",
  "efficacy": ["Analgesic", "Anti-inflammatory", "Antipyretic", "COX inhibitor"],
  "targets": [
    {"gene": "PTGS1", "uniprot": "P23219", "ko": "K00509"},
    {"gene": "PTGS2", "uniprot": "P35354", "ko": "K11987"}
  ],
  "pathways": ["hsa00590", "hsa04611"],
  "diseases": ["Myocardial infarction", "Unstable angina"]
}

Pathway Entry (JSON)

{
  "id": "hsa00010",
  "name": "Glycolysis / Gluconeogenesis",
  "organism": "Homo sapiens",
  "description": "Glycolysis is the process...",
  "genes": [
    {"id": "10327", "symbol": "AKR1A1", "ko": "K00002", "ec": "1.1.1.2"},
    {"id": "3939", "symbol": "LDHA", "ko": "K00016", "ec": "1.1.1.27"}
  ],
  "compounds": [
    {"id": "C00031", "name": "D-Glucose"},
    {"id": "C00022", "name": "Pyruvate"}
  ],
  "modules": ["hsa_M00001", "hsa_M00002", "hsa_M00003"]
}

Disease Entry (JSON)

{
  "id": "H00409",
  "name": "Type 2 diabetes mellitus",
  "category": "Endocrine and metabolic disease",
  "description": "T2DM is characterized by chronic hyperglycemia...",
  "genes": [
    {"symbol": "CAPN10", "ko": "K08579"},
    {"symbol": "TCF7L2", "ko": "K04491"}
  ],
  "drugs": [
    {"id": "D00944", "name": "Metformin hydrochloride"},
    {"id": "D06404", "name": "Liraglutide"}
  ],
  "pathways": ["hsa04930", "hsa04911"]
}

KEGG API Reference

| Operation | URL Pattern | Description | |-----------|-------------|-------------| | info | /info/{database} | Database statistics | | list | /list/{database} | List all entries | | find | /find/{database}/{query} | Search by keyword | | get | /get/{entry_id} | Retrieve entry | | link | /link/{target}/{source} | Cross-references | | conv | /conv/{target}/{source} | ID conversion |

Key Databases: pathway, compound, drug, disease, genes, enzyme, ko

Entry ID Formats:

Drug: D00009 or dr:D00009

Compound: C00031 or cpd:C00031

Pathway: hsa00010 (organism-specific) or map00010 (reference)

Disease: H00409 or ds:H00409

Gene: hsa:5742 (organism:gene_id)

Error Handling

| Error | Solution | |-------|----------| | Entry not found | Verify ID format (e.g., D00109, not aspirin) | | Multiple matches | Use kegg_find first to get exact ID | | Timeout | Reduce query complexity, retry with delay | | Rate limited | KEGG allows ~10 requests/second; add delays |

Integration with OpenBioMed

from open_biomed.data import Molecule, Protein
from open_biomed.tools.tool_registry import TOOLS
Convert KEGG compound to Molecule
compound_entry = kegg_get("cpd:C00031")  # Glucose
mol_file = kegg_get("C00031", option="mol")  # Get MOL format
molecule = Molecule.from_mol_file(mol_file)
Get protein from KEGG gene
gene_entry = kegg_get("hsa:5742")  # PTGS1
Use UniProt ID to fetch protein
protein_tool = TOOLS["protein_uniprot_request"]
proteins, _ = protein_tool.run(accession="P23219")

References

references/kegg_databases.md - Complete database listing and ID formats

references/kegg_api_operations.md - Detailed API operation reference

KEGG API Documentation: https://www.kegg.jp/kegg/rest/keggapi.html