大型语言模型时代的药量计学:未来的愿景
Elena Maria Tosca1, Ludovica Aiello1, Alessandro De Carlo1
1Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di Pavia, 27100 Pavia, Italy.
Pharmaceutics
|October 29, 2025
概括
大型语言模型 (LLM) 显示出作为药量计学中的辅助工具的承诺,用于数据处理和报告等任务. 实现它们的全部潜力需要专门的微调和验证以实现基于模型的药物开发.
科学领域:
- 人工智能在生物医学研究中的应用
- 药理计量和药物开发研究
背景情况:
- 大型语言模型 (LLM) 正在改变科学中的AI应用.
- 药量计学LLM的采用对于基于模型的药物开发 (MIDD) 至关重要,目前是有限的.
- 这项研究探讨了LLM在制药计量工作流程中的整合.
研究的目的:
- 系统地研究LLM在药量计学中的潜在应用.
- 确定LLM在药量测量任务中的当前和未来的角色.
- 概述一个概念框架,用于LLM利用在药量学.
主要方法:
- 在药量计学中LLM应用的综合文献综述.
- 分析相关科学领域的用例.
- 为LLM整合制定一个概念框架.
主要成果:
- 目前在药量计学中LLM的使用很少,主要集中在代码生成上.
- 潜在的应用包括信息合成,数据处理,PK/PD和PBPK/QSP模型支持以及报告生成.
- 讨论了诸如预测建模和数字双胞胎之类的远见应用.
- 挑战包括LLM"幻觉",可重现性问题,以及培训套件中有限的药量数据.
结论:
- 最好将LLM视为辅助工具,而不是取代机械制药计量模型.
- 针对特定领域的微调,提取增强策略和验证对于有效的LLM实施至关重要.
- 结合人类专业知识,传统建模和人工智能的混合方法将推动MIDD的未来创新.
相关概念视频
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