TTD — Therapeutic Target Database
Source: https://ttd.idrblab.cn/
Paper: https://academic.oup.com/nar/article/52/D1/D1465/7275004
Data dir: resources_metadata/dti/TTD
Data files (4 required)
| File | Content |
|---|---|
P1-01-TTD_target_download.txt | Target info: name, UniProt, gene, type, function, disease indication, pathway |
P2-01-TTD_target_drug.txt | Target ↔ Drug links with clinical status (Approved / Phase I–III / Experimental) |
P1-06-Target_disease.txt | Target ↔ Disease associations |
P1-07-Drug_disease.txt | Drug ↔ Disease associations |
File formats:
P1-01,P2-01— block format: blank-line separated records, each line<ID>\t<KEY>\t<VALUE>P1-06,P1-07— TSV with header row
Query API
query(entities, entity_type="auto", data_dir=DATA_DIR)
Returns a list of dicts, one per queried entity.
| Parameter | Type | Description |
|---|---|---|
entities | str or list[str] | One or more entity names / IDs |
entity_type | "auto" / "target" / "drug" / "disease" | Restrict search; "auto" tries target → drug → disease |
data_dir | str | Path to TTD data directory |
query_json(entities, ...) → str
Same as query() but returns a JSON string. Use for LLM consumption.
Input formats accepted
| Input | Examples |
|---|---|
| Gene / protein name | "EGFR", "TP53", "BCR-ABL" |
| Drug name | "Imatinib", "Gefitinib", "Osimertinib" |
| Disease name | "Lung cancer", "Diabetes mellitus" (partial match supported) |
| TTD Target ID | "TTDTARGET00001" |
| TTD Drug ID | "D0Y4GH" |
Matching is case-insensitive; disease names support partial matching.
Output structure
Target result
json{ "query": "EGFR", "entity_type": "target", "ttd_id": "TTDTARGET00001", "name": "Epidermal growth factor receptor", "uniprot": "P00533", "gene": "EGFR", "target_type": "Successful target", "function": "Receptor tyrosine kinase...", "disease": "Non-small-cell lung cancer [ICD-11: 2C25]", "pathway": "EGFR signaling pathway", "drugs": [ {"drug_id": "D0Y4GH", "drug_name": "Gefitinib", "clinical_status": "Approved"}, {"drug_id": "D08VGC", "drug_name": "Erlotinib", "clinical_status": "Approved"} ] }
Drug result
json{ "query": "Imatinib", "entity_type": "drug", "drug_id": "D0IQX1", "drug_name": "Imatinib", "targets": [ {"ttd_target_id": "TTDTARGET00002", "target_name": "BCR-ABL", "clinical_status": "Approved", "drug_id": "D0IQX1"} ], "diseases": ["Chronic myelogenous leukemia", "Gastrointestinal stromal tumor"] }
Disease result
json{ "query": "Lung cancer", "entity_type": "disease", "disease_name": "non-small-cell lung cancer", "targets": [ {"ttd_target_id": "TTDTARGET00001", "target_name": "EGFR"} ], "drugs": ["Gefitinib", "Osimertinib", "Erlotinib"] }
Not found
json{"query": "XYZ123", "entity_type": "not_found", "message": "No match found in TTD."}
Usage examples
pythonfrom 17_TTD import query, query_json # Single entity results = query("EGFR") # Multiple entities (mixed types — auto-detected) results = query(["EGFR", "Imatinib", "Lung cancer"]) # Restrict to drug search only results = query(["Gefitinib", "Osimertinib"], entity_type="drug") # JSON string output (for LLM) print(query_json("TP53"))
CLI (demo runs with EGFR / Imatinib / Lung cancer if no args):
bashpython 17_TTD.py EGFR Imatinib "Lung cancer" python 17_TTD.py TTDTARGET00001
Notes
entity_type="auto"stops at the first match type per entity (target → drug → disease). Use explicit type to resolve ambiguity.drugsin target results lists all TTD-linked drugs; filterclinical_status == "Approved"for marketed drugs.- Disease partial matching —
"lung cancer"will match"non-small-cell lung cancer". The first candidate is returned; useentity_type="disease"with a more specific name if needed. - Multi-value fields (e.g. multiple pathways for one target) are returned as lists.
