评估ChatGPT和Gemini大型语言模型用于用NONMEMEM进行药量测量
Euibeom Shin1, Yifan Yu1, Robert R Bies1
1Department of Pharmaceutical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, 14214-8033, USA.
Journal of pharmacokinetics and pharmacodynamics
|April 24, 2024
概括
像ChatGPT和Gemini这样的大型语言模型可以通过生成初始的NONMEM代码模板来帮助药量测量研究人员. 然而,由于错误和无法重现,生成的代码需要专家审查和纠正.
科学领域:
- 制药指标 (Pharmacometrics) 是一个指标.
- 临床药理学 临床药理学
- 人工智能在药物开发中的作用
背景情况:
- NONMEM 是一种用于药理动力学/药理动力学 (PK/PD) 建模的标准软件.
- 大型语言模型 (LLM) 显示了自动化编码任务的潜力.
研究的目的:
- 为了评估ChatGPT 4.0和Gemini Ultra 1.0在生成NONMEM代码方面的性能.
- 评估LLM对药理学家和临床药理学家的有用性.
主要方法:
- 要求LLM为PK模型创建NONMEM代码.
- 任务包括课程生成,代码结构概述和代码生成.
- 研究了可复制性和超参数效应.
- 代码由NONMEM专家进行了审查.
主要成果:
- LLM提供了有用的课程结构和代码概述.
- 生成的NONMEM代码包含了需要修改的结构和语法错误.
- 代码输出无法重现,温度设置的影响最小.
- 虽然LLM生成了初始编码模板,但不是无错的可执行代码.
结论:
- 在药量计学中,LLM可以作为非MEM编码任务的起点.
- 专家审查和纠正对于LLM生成的NONMEM代码至关重要.
- 需要进一步开发,以提高LLM输出的准确性和可重复性,以用于NONMEM等专业软件.
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