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Datasets:
PatSnap
/
drug

Tasks:
Tabular Classification
Modalities:
Text
Formats:
json
Size:
1K - 10K
Tags:
pharmacology
drug-development
nme
first-in-class
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
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metadata 
license: cc-by-4.0
pretty_name: Global Drug Development & Classification
tags:
  - pharmacology
  - drug-development
  - nme
  - first-in-class
task_categories:
  - tabular-classification
size_categories:
  - 1K<n<10K

💊 Dataset Summary

A curated drug intelligence database covering 2,175 therapeutic compounds with multilingual entity resolution. Each record links a drug to its biological targets, disease indications, development status, and approval history.

🚀 Key Features

  • Multilingual Entities: Drug names, aliases, targets, and disease indications are all provided in both Chinese and English.
  • Target & Indication Mapping: Structured target and disease arrays enable drug-target-disease network construction.
  • Development Status: global_highest_dev_status and first_approved_date track the regulatory lifecycle.
  • Drug Typing: drug_type classifies modality (e.g., Fc fusion protein, Biosimilar, small molecule).

💻 Quick Start

from datasets import load_dataset

dataset = load_dataset("your-org/drug-intelligence", split="train")
record = dataset[0]
en_name = next(n["name"] for n in record["drug_name"] if n["lang"] == "EN")
print(en_name)
# Output: 'Aflibercept Biosimilar(Zakłady Farmaceutyczne Polpharma SA)'
print(record["global_highest_dev_status"])
# Output: 'Approved'
print(record["drug_type"])
# Output: ['Fc fusion protein', 'Biosimilar']

🔗 Resources