gabrielmoreira 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
{
"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
{
"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
{
"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
{"query": "XYZ123", "entity_type": "not_found", "message": "No match found in TTD."}
Usage examples
from 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):
python 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.