Authors
Zheyue Han, Fuxia Zhao, Haonan Xu, Zhenkai Zhang, Jiatong Wu, Yiqiang Zhang, Juan Pan, Mengmeng Li, Zhichao Hao, Wei Guan, Peng Jiang, Yumeng Luo, Xiaochi Ma, Haixue Kuang, Yan Liu
Published in
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy. Volume 366. Pages 128859. Sep 27, 2026. Epub Sep 27, 2026.
Abstract
Traditional Chinese medicine studies are constrained by complex chemical components and multi-target biological interactions, making conventional analytical methods inefficient for mechanism exploration. As a data-driven technique, machine learning has been widely adopted to tackle diverse complex challenges in TCM research. Nevertheless, most existing reviews merely summarize individual algorithms or scattered application cases, lacking systematic task-based classification and unified operational standards. Based on literature published from 2022 to 2026, this review systematically classifies machine learning applications in TCM into four core research tasks. A task-oriented framework is constructed to match different algorithms with appropriate TCM research scenarios. This paper further establishes a complete standardized workflow covering algorithm selection, model construction and model evaluation to standardize ML applications in TCM research. This review also summarizes the applications of machine learning in TCM quality control, bioactive compound identification, processing analysis, and mechanism elucidation. Furthermore, major bottlenecks in current ML-based TCM studies are analyzed, and feasible optimization strategies are proposed. This review aims to provide clear and practical guidelines for the standardized application of machine learning in future TCM research.
PMID:
42830003
Bibliographic data and abstract were imported from PubMed on 05 Oct 2026.
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