Datasets:

PatSnap
/

drug-deal

license: cc-by-4.0
task_categories:
  - tabular-classification
  - feature-extraction
language:
  - en
tags:
  - drug-deal
  - biotech-business
  - licensing-deals
  - pharma-m-a
  - market-intelligence
pretty_name: Global Biopharma Licensing & M/A Transactions
size_categories:
  - 1K<n<10K

🤝 Dataset Summary

Pharmaceutical licensing and deal intelligence records covering 1,000 transactions. Each record captures the deal structure, involved organizations, financial terms, and asset details, sourced from news and company disclosures.

🚀 Key Features

Deal Structure: deal_type classifies transaction type (license, acquisition, collaboration, etc.) with multilingual labels.
Financial Terms: deal_value provides structured breakdown of payment types (upfront, milestone) with normalized USD values (~34.5% of records).
Organizational Mapping: principle_org and partner_org identify both sides of each transaction.
Asset Linkage: deal_project links deals to specific drugs or programs by name and type.
Territory Coverage: territory_included specifies geographic rights where disclosed (~33.8% of records).

💻 Quick Start & MCP Integration

from datasets import load_dataset

dataset = load_dataset("your-org/drug-deals", split="train")
record = dataset[0]
print(record["deal_title"])
# Output: 'GSK exercises option on Anacor\'s novel antibiotic for the treatment of gram-negative infections'
print(record["principle_org"])
# Output: ['Anacor Pharmaceuticals']
print(record["deal_value"][0]["value"])
# Output: '15.00'  (million dollars, upfront payment)