For Data Scientists & ML Engineers · Pharmaceuticals
Pharmaceuticals Data for Data Scientists
Pharmaceuticals data for data scientists: 13 datasets on one shelf. Every one delivered as API, files, or warehouse rows.
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API, files, or your warehouse. Daily, weekly, or hourly.
Why pharmaceuticals rewards data-science workflows
This slice over-indexes on the traits a modeling pipeline needs. Quality runs high: the slice averages 8.08 on Datadory's 0-10 rubric versus the 7.81 catalog-wide mean, and six sources score 9 or better. The industry-wide view sits on the pharmaceuticals data hub.
Which pharma datasets rank just below the top eight?
The tail still earns its place: both EMA sources publish machine-readable EU approval tables refreshed overnight or twice daily, DrugBank Open Data ships commercial delivery terms identifiers that join cleanly into knowledge graphs, and the WHO ATC/DDD Index contributes per-substance defined daily doses across 14 anatomical groups.
Which pharmaceuticals APIs should you wire in first?
RxNorm's four REST APIs normalise clinical names to RXCUIs monthly and are the backbone of US medication entity resolution, and ChEMBL covers bioactivity with similarity and substructure search on quarterly releases.
How do you choose between them?
Pick by job. Safety and pharmacovigilance features come from openFDA first. Chemistry and targets go to ChEMBL and PubChem, with DrugBank Open Data as the commercial delivery terms join layer. Medication normalization belongs to RxNorm; interaction graphs belong to KEGG; EU approval and shortage dynamics belong to the two EMA sources. Licensing is the tripwire: everything except Drugs.com permits commercial reuse without negotiation.
This page is the pharmaceuticals slice of our all data-scientists resources hub, which applies the same rubric to every other industry we cover.
Straight answers
Where can I get training data for pharmacovigilance models?
Pair them with RxNorm RXCUI normalization to dedupe drug names before feature engineering, and with EMA medicine tables for European withdrawal and shortage signals.
Can I build drug-interaction prediction models from open data?
Yes. The KEGG REST API exposes seven unauthenticated operations including ddi and link, returning tab-delimited, turtle, n-triples, mol and kgml payloads, while the KEGG DRUG database joins D-numbered entries to targets, enzymes and interaction groups daily.
How fresh is pharmaceuticals data for data scientists?
Five of the 13 sources, PubChem PUG REST, KEGG DRUG, the EMA Download Medicine Data Tables and Data.gov - and EMA's medicines catalogue also publishes JSON twice daily. At the other end, DrugBank Open Data and the Hugging Face corpora are static snapshots, and the WHO ATC/DDD Index refreshes annually across its 14 anatomical groups.
Rows before rollout
Sample rows from any shelf entry — the field dictionary and coverage notes ride along. If the shelf misses what you need, say so; sourcing requests are half our job.
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