人工智能技术在中药代谢组学研究中的应用

doi:10.19803/j.1672-8629.20250880

摘要/Abstract

摘要： 目的探讨人工智能（AI）技术在中药代谢组学研究中的应用,推动中药复杂体系作用解析智能化。方法结合代谢组学研究流程,系统梳理AI技术在中药组学数据处理、代谢物鉴定、机制解析、质量解析等环节的应用,以具体实例揭示机器学习、深度学习等技术在实际应用中的优势与不足。结果 AI技术通过将中药代谢组学数据高通量、自动化的预处理,进行更智能的数据质量控制和特征筛选;通过与数据库、分子网络等深度融合,并结合质谱数据映射关系,AI技术能实现代谢物智能预测与推断;AI技术能够自动获取高维数据复杂模式并有效评估每个特征对预测的贡献度,通过特征选择、深层关系捕捉、结果可解释等筛选差异代谢物;复杂网络分析、机器学习与知识图谱等智能技术相结合,能够突破中药效应机制的系统解析;植物类中药次生代谢产物与AI技术结合,助力道地性识别、质量优劣评价。AI技术具有提高数据处理效率、提升预测准确性与多维度整合的优势,但也存在数据标准化欠缺、模型可解释性不足、专业领域适配性欠缺等问题。结论 AI技术助力中药代谢组学研究,为中药安全性、有效性及质量可控性研究提供新技术、新方法和新策略。

关键词: 中药, 代谢组学, 生物标志物, 人工智能, 机器学习, 作用机制

Abstract: Objective To explore the applications of artificial intelligence (AI) in research on traditional Chinese medicine (TCM) metabolomics in order to upgrade the intelligent analysis of complex systems of TCM. Methods Following the metabolomics research workflow, this review outlined AI applications in such spheres as data processing, metabolite identification, interpretation of mechanisms, and quality control of TCM. Specific examples were cited to demonstrate the strengths and weaknesses of AI. Results AI enables high-throughput, automated preprocessing of metabolomic data so that quality control and feature screening are improved. Integrated with databases and molecular networking, AI can shed light on mapping relationships from vast data on mass spectrometry for intelligent metabolite predictions. By automatically identifying complex patterns in high-dimensional data and assessing feature contributions, AI facilitates differential metabolite selection via feature selection, deep relationship mining, and interpretability analysis. Network analysis, machine learning, and knowledge graphs can combine to offer insights into the mechanisms of TCM. Furthermore, AI can assist in identification of geographical origins and quality control by analyzing secondary metabolites. While AI enhances data processing efficiency, accuracy of predictions, and multi-dimensional integration, such challenges persist as the lack of data standardization, limited model interpretability, and insufficient domain-specific adaptation. Conclusion AI can empower TCM metabolomics research and contribute to the informatization and intellectualization of the analysis of complex mechanisms of TCM.

Key words: Traditional Chinese Medicine(TCM), Metabolomics, Biomarker, Artificial Intelligence(AI), Machine Learning, Mechanism of Action

中图分类号:

R962
R282

李耀磊, 程显隆, 林永强, 刘静, 魏锋. 人工智能技术在中药代谢组学研究中的应用[J]. 中国药物警戒, 2026, 23(5): 481-486.

LI Yaolei, CHENG Xianlong, LIN Yongqiang, LIU Jing, WEI Feng. Applications of Artificial Intelligence in Research on Traditional Chinese Medicine Metabolomics[J]. Chinese Journal of Pharmacovigilance, 2026, 23(5): 481-486.

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链接本文: https://zgywjj.magtechjournal.com/CN/10.19803/j.1672-8629.20250880

https://zgywjj.magtechjournal.com/CN/Y2026/V23/I5/481

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