- name
- kegg-query
- description
- >
- license
- MIT
- category
- knowledge-retrieval
- tags
- [kegg, pathway, drug, disease, target, bioinformatics]
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+ compoundsOutput 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+ genesOutput 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:
D00009ordr:D00009 - Compound:
C00031orcpd:C00031 - Pathway:
hsa00010(organism-specific) ormap00010(reference) - Disease:
H00409ords: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 formatsreferences/kegg_api_operations.md- Detailed API operation reference- KEGG API Documentation: https://www.kegg.jp/kegg/rest/keggapi.html