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From fragments to function: a review of AI-driven mass spectrometry and fragment-based strategies
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Tianyi Ma1, 2, Zhaoxin Xu1, 2, Yugang Lin3, Jie Liao1, 2, *, Xiaohui Fan1, 2, *
Acupuncture and Herbal Medicine | 2026, 6(1) : 28 - 41
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Acupuncture and Herbal Medicine | 2026, 6(1): 28-41
• Review Article •
From fragments to function: a review of AI-driven mass spectrometry and fragment-based strategies
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Tianyi Ma1, 2, Zhaoxin Xu1, 2, Yugang Lin3, Jie Liao1, 2, *, Xiaohui Fan1, 2, *
Affiliations
  • 1State Key Laboratory of Chinese Medicine Modernization, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
  • 2Zhejiang Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, China
  • 3Department of Pharmacy, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
Published: 2026-03-25 doi: 10.1097/HM9.0000000000000190
Outline
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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.

Artificial Intelligence  /  Fragment-based representation  /  Mass spectrometry  /  Structural elucidation  /  Traditional Chinese medicine
Tianyi Ma, Zhaoxin Xu, Yugang Lin, Jie Liao, Xiaohui Fan. From fragments to function: a review of AI-driven mass spectrometry and fragment-based strategies[J]. Acupuncture and Herbal Medicine, 2026 , 6 (1) : 28 -41 . DOI: 10.1097/HM9.0000000000000190
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Funding
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  • Zhejiang Provincial Natural Science Foundation of China(LD25H280002)
  • National Natural Science Foundation of China(U23A20513)
  • Key Project of Zhejiang Provincial Administration of Traditional Chinese Medicine(GZY-KJS-ZJ-2025-071)
  • Starlit South Lake Leading Elite Program(2023A303005)
  • Fundamental Research Funds for the Central Universities(226-2025-00009)
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Year 2026 volume 6 Issue 1
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Article Info
doi: 10.1097/HM9.0000000000000190
  • Receive Date：2025-11-25
  • Online Date：2026-06-05
  • Published：2026-03-25
Article Data
Affiliations
History
  • Received：2025-11-25
  • Accepted：2026-02-10
Funding
Zhejiang Provincial Natural Science Foundation of China(LD25H280002)
National Natural Science Foundation of China(U23A20513)
Key Project of Zhejiang Provincial Administration of Traditional Chinese Medicine(GZY-KJS-ZJ-2025-071)
Starlit South Lake Leading Elite Program(2023A303005)
Fundamental Research Funds for the Central Universities(226-2025-00009)
Affiliations
    1State Key Laboratory of Chinese Medicine Modernization, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
    2Zhejiang Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, China
    3Department of Pharmacy, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China

Corresponding:

* Corresponding author. Jie Liao, E-mail:
Xiaohui Fan, E-mail: .
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表12种不同金属材料的力学参数
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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