臨床前薬の開発のためのPRINCEマルチエージェント知識エンジン
Carlos Henrique Vieira-Vieira1, Sarang Sanjay Kulkarni2, Adam Zalewski1
1Bayer Research and Development, Pharmaceuticals, Preclinical Development, Berlin, Germany.
Frontiers in artificial intelligence
|September 4, 2025
まとめ
臨床前情報センター (PRINCE) プラットフォームは,人工知能と大型言語モデル (LLM) を使用して,医薬品の安全性データを統合し,医薬品開発のための研究効率と文書生成を強化します.
科学分野:
- 医薬品の研究開発
- 薬剤開発における人工知能
- データの統合と管理
背景:
- 薬剤産業はコストと規制の 増大に伴い 効率的な医薬品開発プロセスを必要としています
- 何十年もの安全性研究データにアクセスし,統合することは大きな課題です.
- 既存のデータ検索方法には 複雑で構造化されていない情報を処理する 精度が欠けていることがよくあります
研究 の 目的:
- クリニック前情報センター (PRINCE) を導入し,クラウドでホストされた新しいデータ統合プラットフォームを導入します.
- PRINCEの進化を詳細に説明します 基本的な検索ツールから人工知能を搭載した 研究アシスタントへと
- 医薬品データ管理における大型言語モデル (LLM) と高度な検索技術の適用を実証する.
主な方法:
- 多エージェントアーキテクチャを利用したPRINCEの開発
- 先進的なデータ検索技術の実装: 検索拡張生成とテキストからSQL.
- ユーザーからのフィードバックを元に繰り返し開発し,精度と説明責任のための人間インザループのアプローチを組み込む.
主要な成果:
- PRINCEは,大量の構造化および非構造化安全研究データを成功裏に統合しています.
- このプラットフォームは複雑な研究質問に答え,規制文書の起草を支援するために進化しました.
- データのアクセシビリティと研究効率の向上は,AI統合によって達成されました.
結論:
- PRINCEプラットフォームは AIが製薬業界でもたらす変革の可能性を示しています
- AI主導のソリューションは,データのアクセシビリティと研究効率を大幅に改善できます.
- 信頼,透明性,説明性,人間の監視を優先することは,規制された産業におけるAIの展開に不可欠です.
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