通过代理人工智能实现现实世界药物发现的民主化.
Jiazhen He1, Helen Lai2, Lakshidaa Saigiridharan1
1Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Pepparedsleden 1, 431 83 Mölndal, Sweden.
Drug discovery today
|January 18, 2026
概括
由大型语言模型 (LLM) 驱动的代理系统现在正在帮助药物发现. 基于LLM的系统ChatInvent已集成到AstraZeneca的分子设计和合成规划管道中.
科学领域:
- 化学中的人工智能.
- 药物发现技术 药物发现技术
- 计算化学计算化学
背景情况:
- 利用大语言模型 (LLM) 的代理系统显示了化学研究的潜力.
- 现有的系统,如CoScientist,Chemcrow和LLM-RDF,在化学信息学和反应开发方面证明了它们的实用性.
- 这些系统在药物发现管道中的现实世界采用仍未得到充分探索.
研究的目的:
- 介绍一个在药物发现中采用代理系统的现实世界的例子.
- 在AstraZeneca详细介绍ChatInvent系统的开发和集成.
- 讨论药物研究中的代理系统的演变,挑战和未来前景.
主要方法:
- 基于大型语言模型开发了一个名为ChatInvent的代理系统.
- 将ChatInvent集成到AstraZeneca的药物发现管道中.
- 从单个代理概念验证到具有图形用户界面的多代理架构的演变.
主要成果:
- 成功地将ChatInvent代理系统集成到一家大型制药公司的发现管道中.
- 在分子设计和合成规划中展示系统的实用性.
- 建立一个可扩展,强大的,可扩展的多代理架构.
结论:
- 代理系统代表了人工智能驱动药物发现的重大进步.
- 该ChatInvent项目提供了一个实用的模型,用于在制药研究中实施基于LLM的工具.
- 持续的发展和解决持续的挑战对于实现化学中代理系统的全部潜力至关重要.
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