从数据库到洞察力:用于临床前药物开发的PRINCE多代理知识引擎
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
概括
临床前信息中心 (PRINCE) 平台使用人工智能和大型语言模型 (LLM) 整合药物安全数据,提高药物开发的研究效率和文件生成.
科学领域:
- 制药研究和开发
- 药物发现中的人工智能
- 数据整合和管理
背景情况:
- 制药行业需要高效的药物开发流程,
- 获取和综合数十年安全研究数据是一个重大挑战.
- 现有的数据检索方法往往缺乏处理复杂,非结构化的信息的复杂性.
研究的目的:
- 介绍临床前信息中心 (PRINCE),这是一个新的云托管数据集成平台.
- 详细介绍PRINCE从一个基本的搜索工具到一个由人工智能驱动的研究助理.
- 展示大型语言模型 (LLM) 和高级检索技术在药物数据管理中的应用.
主要方法:
- 使用LLM支持的多代理架构开发PRINCE.
- 实施先进的数据检索技术:检索增强生成和文本到SQL.
- 以用户反为指导的代开发,并采用人为循环的准确性和问责制方法.
主要成果:
- PRINCE成功地整合了大量结构化和非结构化安全研究数据.
- 该平台的发展是为了回答复杂的研究问题,并协助起草监管文件.
- 通过人工智能集成实现了数据可访问性和研究效率的提高.
结论:
- 普林斯平台展示了人工智能在制药领域的变革潜力.
- 人工智能驱动的解决方案可以显著提高数据可访问性和研究效率.
- 优先考虑信任,透明度,可解释性和人类监督对于在受监管的行业中成功部署人工智能至关重要.
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