DrugCentral Query Skill

Search local DrugCentral flat files by any entity. Auto-detects query type:

Input Pattern	Detected As	Match Logic
860 (numeric)	DrugCentral ID	exact on ID
50-78-2 (NNN-NN-N)	CAS Number	exact on CAS_RN
BSYNRYMUTXBXSQ or full key	InChIKey (prefix or full)	prefix match on InChIKey
anything else	free text	substring on INN (drug name)

Data

Download from https://drugcentral.org/download:

File	Description	Required
structures.smiles.tsv	SMILES, InChI, InChIKey, ID, INN, CAS_RN	Yes
drug.target.interaction.tsv	Drug-target interaction profiles (gene, action, potency)	Recommended
FDA+EMA+PMDA_Approved.csv	Approval status (ID, drug_name)	Optional

Place files in DATA_DIR (default: resources_metadata/drug_knowledgebase/DrugCentral, or set env DRUGCENTRAL_DIR).

API

Function	Input	Returns
search(entity)	single entity string	dict with structures, targets, approved
search_batch(entities)	list or comma-separated string	dict[str, dict]
summarize(result, entity)	search result dict + label	compact text
to_json(result)	search result dict	JSON string

Key Fields

structures: ID, INN (drug name), CAS_RN, SMILES, InChI, InChIKey

targets (from DTI file): GENE, TARGET_NAME, TARGET_CLASS, ACTION_TYPE, ACT_VALUE, ACT_TYPE, ACT_UNIT, ACCESSION (UniProt), TDL, ORGANISM

approved: id, name, approved (bool)

Usage

from 18_DrugCentral import search, search_batch, summarize, to_json

# Single query — drug name
result = search("aspirin")
print(summarize(result))

# Single query — DrugCentral ID
result = search("860")
print(summarize(result))

# Single query — CAS number
result = search("50-78-2")
print(summarize(result))

# Batch query
results = search_batch(["metformin", "ibuprofen", "50-78-2"])
for entity, res in results.items():
    print(summarize(res, entity))

# JSON export
print(to_json(result))

See if __name__ == "__main__" block in 18_DrugCentral.py for runnable examples covering: drug name, DrugCentral ID, CAS number, InChIKey prefix, batch search, and JSON output.

Source

• DrugCentral: https://drugcentral.org/
• Paper: Avram et al., Nucleic Acids Research 2023, 51(D1):D1276–D1287. DOI: 10.1093/nar/gkac1085
• License: CC BY-NC 4.0 (non-commercial)