分析测试学报2026，Vol.45Issue(6)：1161-1173,13.DOI:10.12452/j.fxcsxb.26012901
AI驱动下中药化学成分的智能分析与化学空间拓展
AI-driven Intelligent Characterization and Chemical Space Expansion for Traditional Chinese Medicine
摘要
Abstract
The compositional analysis of complex material systems in traditional Chinese medicine(TCM)—including small molecules,proteins,polysaccharides,and higher-order self-assembled structures—has long been constrained by insufficient spectral library coverage and the bottleneck of manual interpretation,thereby limiting the standardization and depth of research into their material basis.Leveraging its strong capability for multi-source data integration and deep representation learn-ing,artificial intelligence(AI)is driving a paradigm shift in TCM constituent analysis from experi-ence-driven approaches to data-driven methodologies.This review systematically surveyed learning paradigms,core architectures,and representative tasks of AI-based TCM constituent analysis,with a particular focus on molecular representation learning methods,spectroscopic data processing strate-gies,and recent advances in complex multidimensional component identification and chemical space expansion.Finally,key challenges and future directions were discussed,including data standard-ization,multimodal integration,and model interpretability.From an interdisciplinary perspective,this review aimed to provide methodological support for the modernization and high-quality develop-ment of traditional Chinese medicine.关键词
人工智能/中药化学成分/深度学习/表征学习/智能分析Key words
artificial intelligence/traditional Chinese medicine(TCM)/deep learning/represen-tation learning/intelligent characterization分类
化学化工引用本文复制引用
卢志鹏,董莹莹,杜柯,单进军,谢彤..AI驱动下中药化学成分的智能分析与化学空间拓展[J].分析测试学报,2026,45(6):1161-1173,13.基金项目
江苏省自然科学基金面上项目(BK20241915) （BK20241915）
江苏省中医药科技计划项目(MS2022002) （MS2022002）
