检索增强语言模型使得可扩展的化学源分类在代谢学工作流程
Prajit Rajkumar1,2, Runbang Tang1,2, Harshada Sapre1,2
1Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, California 92093, United States.
Analytical chemistry
|January 29, 2026
概括
自动化化学分类对暴露学和代谢学至关重要. 新的chemsource工具使用大语言模型 (LLM) 和检索增强生成 (RAG) 来根据暴露分类化学物质,改进数据解释.
科学领域:
- 环境化学环境化学
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
背景情况:
- 解释暴露学和代谢学数据需要可扩展的化学分类.
- 目前的功能性和基于暴露的化学品标签是人工和劳动密集型的.
- 自动化化学分类对于推动这些领域的发展至关重要.
研究的目的:
- 开发和介绍chemsource,一个自动化化学分类的框架.
- 为了完成这一任务,将大型语言模型 (LLM) 与检索增强生成 (RAG) 集成在一起.
- 为了使各种应用程序的用户定义的分类类别.
主要方法:
- chemsource从维基百科或PubMed摘要中获取化学描述.
- 然后它提示LLM根据检索的内容分配用户定义的类别.
- 在五个暴露类别中进行了分类:内源代谢物,食物分子,药物,个人护理产品和工业化学品.
主要成果:
- 与4,953个手工策划的标签进行比较,显示了75%的总体同意.
- 在所有测试的类别中,类别级召回超过了75%.
- 对八个代谢学数据集的应用揭示了各种生物样本和环境样本中不同的暴露模式.
结论:
- chemsource为自动化化学品分类提供了灵活和可定制的解决方案.
- 该框架在代谢学工作流程中展示了实用性,并确定了暴露模式.
- 它以Python包的形式提供,可以在没有编码专业知识的情况下实现各种分类任务.
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