针灸和草药（英文）2026，Vol.6Issue(1)：28-41,14.DOI:10.1097/HM9.0000000000000190
从碎片到功能:AI驱动的质谱解析与分子碎片表征策略综述
From fragments to function:a review of AI-driven mass spectrometry and fragment-based strategies
摘要
Abstract
Traditional Chinese medicine(TCM)possesses unique advantages in disease prevention and treatment,yet its inherent complexity and diversity pose tremendous challenges for structural elucidation,mechanism research,and bioactivity characterization.High-resolution mass spectrometry(HRMS)technology demonstrates immense potential in TCM analysis due to its high sensitivity,high resolution,high throughput,and high efficiency.However,its application in TCM research remains constrained by the lack of intelligent analytical methods and unified standardized databases.Therefore,this paper focuses on the integration of artificial intelligence(AI)and mass spectrometry,providing a systematic review of the applications and potential of AI-driven mass spectrometry analysis in structural elucidation,data resource integration,multi-omics mechanism studies,and chemical biology.Furthermore,this article emphasizes that by leveraging AI models to learn the complex mapping from chemical structures to biological functions,fragment-based characterization has emerged as the bridge connecting chemical structures with biological activities.Molecular fragments themselves serve as the core"knowledge units"that carry bioactive information.Future research will focus on establishing high-quality mass spectrometry databases for the complete chemical profiles of TCM and promoting the standardization and open sharing of mass spectrometry databases,thereby advancing the integration of AI and mass spectrometry in TCM analysis and providing new tools for TCM research.关键词
质谱分析/中药/人工智能/结构解析/化学表征Key words
Artificial Intelligence/Fragment-based representation/Mass spectrometry/Structural elucidation/Traditional Chinese medicine引用本文复制引用
马天怡,许赵欣,林玉刚,廖杰,范骁辉..从碎片到功能:AI驱动的质谱解析与分子碎片表征策略综述[J].针灸和草药（英文）,2026,6(1):28-41,14.基金项目
This work was supported by Zhejiang Provincial Natural Science Foundation of China (LD25H280002,J.L.),the National Natural Science Foundation of China (Grant No. U23A20513,X.F.),and the Key Project of Zhejiang Provincial Administration of Traditional Chinese Medicine(Grant No. GZY-KJS-ZJ-2025-071,J.L.),the Starlit South Lake Leading Elite Program (Grant No. 2023A303005,X.F.),and the Fundamental Research Funds for the Central Universities (226-2025-00009,X.F.). （LD25H280002,J.L.）
