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💊 Dataset Summary
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🚀 Key Features
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Regulatory Milestones: Boolean flags for is_nme, is_first_in_class, and is_qidp.
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Connect your AI agents to global drug intelligence using our Drug Discovery MCP Tool on GitHub.
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## 🔗 Resources
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- **Developer Portal**: [open.patsnap.com](https://open.patsnap.com)
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💊 **Dataset Summary**
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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.
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**🚀 Key Features**
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- **Multilingual Entities:** Drug names, aliases, targets, and disease indications are all provided in both Chinese and English.
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- **Target & Indication Mapping:** Structured `target` and `disease` arrays enable drug-target-disease network construction.
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- **Development Status:** `global_highest_dev_status` and `first_approved_date` track the regulatory lifecycle.
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- **Drug Typing:** `drug_type` classifies modality (e.g., Fc fusion protein, Biosimilar, small molecule).
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**💻 Quick Start**
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```python
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from datasets import load_dataset
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dataset = load_dataset("your-org/drug-intelligence", split="train")
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record = dataset[0]
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en_name = next(n["name"] for n in record["drug_name"] if n["lang"] == "EN")
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print(en_name)
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# Output: 'Aflibercept Biosimilar(Zakłady Farmaceutyczne Polpharma SA)'
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print(record["global_highest_dev_status"])
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# Output: 'Approved'
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print(record["drug_type"])
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# Output: ['Fc fusion protein', 'Biosimilar']
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```
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## 🔗 Resources
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- **Developer Portal**: [open.patsnap.com](https://open.patsnap.com)
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