快速到药丸:多剂药物发现和临床模拟管道
Ivana Vichentijevikj1, Kostadin Mishev2, Monika Simjanoska Misheva2
1iReason LLC, Skopje 1000, North Macedonia.
Bioinformatics advances
|January 16, 2026
概括
这项研究介绍了Prompt-to-Pill,这是一种用于药物发现和临床试验模拟的AI框架. 它使用大型语言模型 (LLM) 几乎简化了整个药物开发管道.
科学领域:
- 计算化学和药理学计算化学和药理学
- 医学中的人工智能
- 药物的发现和开发.
背景情况:
- 传统的药物发现是漫长而昂贵的.
- 大型语言模型 (LLM) 在各种科学应用中表现有前途.
- 在药物开发管道中整合LLM仍然是一个挑战.
研究的目的:
- 为药物发现和临床试验模拟提供一个全面的模块化AI框架.
- 为了证明基于LLM的虚拟药物开发多代理系统的可行性.
- 用人工智能弥合临床前研究和临床开发之间的差距.
主要方法:
- 开发了一个Prompt-to-Pill架构,使用多代理系统 (MAS) 与专门的法学士.
- 实施了一种工作流,涵盖目标识别,分子生成,ADMET预测,对接和优化.
- 模拟的临床试验阶段,包括患者招募和使用EHR数据预测结果.
主要成果:
- 成功展示了使用二乙酶4 (DPP4) 作为使用案例的端到端AI框架.
- 集成的生成,预测和检索型LLM,以实现无工作流的运行.
- 展示了基于LLM的药剂的潜力,以使药物开发成为可操作的 *in silico*.
结论:
- 快速到药丸框架为人工智能驱动的药物发现和虚拟临床试验提供了一种新的方法.
- 基于LLM的多代理系统可以显著加快和优化药物开发过程.
- 这项工作为未来在制药研究中的AI应用提供了基础.
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