Acupuncture and Herbal Medicine
|
2026, 6(3): 309-313
AI-powered functional metabolomics: illuminating the dark matter of metabolism and transforming therapeutic discovery in traditional Chinese medicine
Full
- 1Hong Kong Traditional Chinese Medicine Phenome Research Center, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China
- 2Centre for Chinese Herbal Medicine Drug Development Limited, Hong Kong Baptist University, Hong Kong, China
- 3Department of Medicinal Chemistry and Natural Medicine Chemistry, Department of Pharmacognosy, College of Pharmacy, Harbin Medical University, Harbin, China
- 4State-Province Key Laboratory of Biomedicine-Pharmaceutics of China, Harbin Medical University, Harbin, China
- 5Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
- 6Shanghai Center for Systems Biomedicine, State Key Laboratory of Systems Biomedicine (MOST), Shanghai Jiao Tong University, Shanghai, China
About Author:
Jiaxin Zhang, Jingjing Liu, Yuan Tian, and Li Yao contributed equally to this manuscript.
Jiaxin Zhang, Jingjing Liu, Yuan Tian, Li Yao, Zhaoxiang Bian, Aiping Lu, Haitao Lu.
AI-powered functional metabolomics: illuminating the dark matter of metabolism and transforming therapeutic discovery in traditional Chinese medicine[J].
Acupuncture and Herbal Medicine,
2026
, 6
(3)
: 309
-313
.
DOI: 10.1097/HM9.0000000000000202
- Pneumoconiosis Compensation Fund Board (HKSAR) and Guangdong and Hong Kong Universities 1 + 1 + 1 Joint Research Collaboration Scheme(2025A0505000006)
[1]
Patti GJ, Yanes O, Siuzdak G. Innovation: metabolomics: the apogee of the omics trilogy.
Nat Rev Mol Cell Biol 2012;13:263–269.
[2]
Qiu S, Cai Y, Yao H, et al. Small molecule metabolites: discovery of biomarkers and therapeutic targets.
Signal Transduct Target Ther 2023;8:132.
[3]
Wang T, Liu J, Luo X, et al. Functional metabolomics innovates therapeutic discovery of traditional Chinese medicine derived functional compounds.
Pharmacol Ther 2021;224:107824.
[4]
Liu J, Jing W, Wang T, et al. Functional metabolomics revealed the dual-activation of cAMP-AMP axis is a novel therapeutic target of pancreatic cancer.
Pharmacol Res 2023;187:106554.
[5]
Metabolomics: dark matter.
Nature 2008;455:698.
[6]
Liu J, Bao C, Zhang J, et al. Artificial intelligence with mass spectrometry-based multimodal molecular profiling methods for advancing therapeutic discovery of infectious diseases.
Pharmacol Ther 2024;263:108712.
[7]
Chen Z, Wang H, Li C, et al. Large language models in traditional Chinese medicine: a systematic review.
Acupunct Herb Med 2025;5(1):57–67.
[8]
Zhang JH, Chen SL, Chen XM, et al. Traditional Chinese medicine + artificial intelligence: Wuzhen consensus.
Acupunct Herb Med 2025;5(2):134–135.
[9]
Xu R, Zhu J. Unveiling the dark matter of the metabolome: a narrative review of bioinformatics tools for LC-HRMS-based compound annotation.
Talanta 2025;295:128327.
[10]
Han JR, Li S, Li WJ, et al. Mining microbial and metabolic dark matter in extreme environments: a roadmap for harnessing the power of multi-omics data.
Adv Biotechnol (Singap) 2024;2:26.
[11]
Chi J, Shu J, Li M, et al. Artificial intelligence in metabolomics: a current review.
Trends Analyt Chem 2024;178:117852.
[12]
Qiang H, Wang F, Lu W, et al. Language model-guided anticipation and discovery of mammalian metabolites.
Nature 2026;651:211–220.
[13]
Shenouda A, Senthilkumar S, Mourad Y, et al. Artificial intelligence to investigate metabolomics data for precision medicine.
Metabolomics 2026;22:29.
[14]
Sha Y, Meng W, Luo G, et al. MetDIT: transforming and analyzing clinical metabolomics data with convolutional neural networks.
Anal Chem 2024;96:2949–2957.
[15]
Zhou Y, Ni Z, Liu J, et al. Gut microbiota-associated metabolites affected the susceptibility to heart health abnormality in young migrants at high-altitude: gut microbiota and associated metabolites impart heart health in plateau.
Exploration (Beijing) 2025;5:20240332.
[16]
Jing W, Dong S, Xu Y, et al. Gut microbiota-derived tryptophan metabolites regulated by Wuji Wan to attenuate colitis through AhR signaling activation.
Acta Pharm Sin B 2025;15:205–223.
[17]
Di Filippo M, Pescini D, Galuzzi BG, et al. INTEGRATE: model-based multi-omics data integration to characterize multi-level metabolic regulation.
PLoS Comput Biol 2022;18:e1009337.
[18]
Soleymani Babadi F, Razaghi-Moghadam Z, Zare-Mirakabad F, et al. Prediction of metabolite-protein interactions based on integration of machine learning and constraint-based modeling.
Bioinform Adv 2023;3:vbad098.
[19]
Cogne T, Ait Oumelloul M, Saadat A, et al. Structure-based metabolite function prediction using graph neural networks.
Bioinform Adv 2025;5:vbaf174.
[20]
Mardinoglu A, Turkez H, Shong M, et al. Longitudinal big biological data in the AI era.
Mol Syst Biol 2025;21:1147–1165.
[21]
Helmy M, Smith D, Selvarajoo K. Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering.
Metab Eng Commun 2020;11:e00149.
[22]
Lim J, Jung HD, Park SY, et al. Genome-scale knockout simulation and clustering analysis of drug-resistant breast cancer cells reveal drug sensitization targets.
Proc Natl Acad Sci U S A 2025;122:e2425384122.
[23]
Lv H. Mass spectrometry-based metabolomics towards understanding of gene functions with a diversity of biological contexts.
Mass Spectrom Rev 2013;32:118–128.
[24]
Hu L, Liu J, Zhang W, et al. Functional metabolomics decipher biochemical functions and associated mechanisms underlie small-molecule metabolism.
Mass Spectrom Rev 2020;39:417–433.
[25]
Vo DK, Trinh KTL. Emerging biomarkers in metabolomics: advancements in precision health and disease diagnosis.
Int J Mol Sci 2024;25:13190.
[26]
Elguoshy A, Zedan H, Saito S. Machine learning-driven insights in cancer metabolomics: from subtyping to biomarker discovery and prognostic modeling.
Metabolites 2025;15:514.
[27]
Chen Y, Wang B, Zhao Y, et al. Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer.
Nat Commun 2024;15:1657.
[28]
Pun FW, Ozerov IV, Zhavoronkov A. AI-powered therapeutic target discovery.
Trends Pharmacol Sci 2023;44:561–572.
[29]
Li D, Shi S, Yu Z, et al. AI accelerate the identification of druggable targets by 3D structures of proteins and compounds.
npj Precis Oncol 2026;10:133.
[30]
Serrano DR, Luciano FC, Anaya BJ, et al. Artificial intelligence (AI) applications in drug discovery and drug delivery: revolutionizing personalized medicine.
Pharmaceutics 2024;16:1328.
[31]
Johnson KB, Wei WQ, Weeraratne D, et al. Precision medicine, AI, and the future of personalized health care.
Clin Transl Sci 2021;14:86–93.
[32]
Chang L, Liu J, Zhu J, et al. Advancing precision medicine: the transformative role of artificial intelligence in immunogenomics, radiomics, and pathomics for biomarker discovery and immunotherapy optimization.
Cancer Biol Med 2025;22:33–47.
[33]
Hillesheim E, Brennan L. Metabotyping: a tool for identifying subgroups for tailored nutrition advice.
Proc Nutr Soc 2023;82:130–141.
[34]
Metwally AA, Perelman D, Park H, et al. Prediction of metabolic subphenotypes of type 2 diabetes
via continuous glucose monitoring and machine learning.
Nat Biomed Eng 2025;9:1222–1239.
[35]
Ye J, Woods D, Jordan N, et al. The role of artificial intelligence for the application of integrating electronic health records and patient-generated data in clinical decision support.
AMIA Jt Summits Transl Sci Proc 2024;2024:459–467.
[36]
Zheng Y, Ma Y, Xiong Q, et al. The role of artificial intelligence in the development of anticancer therapeutics from natural polyphenols: current advances and future prospects.
Pharmacol Res 2024;208:107381.
[37]
Fu M, Li Y, Liu J, et al. Zhishi Xiebai Guizhi decoction modulates hypoxia and lipid toxicity to alleviate pulmonary vascular remodeling of pulmonary hypertension in rats.
Chin Med 2024;19:173.
[38]
Li Y, Han D, Liu J, et al. A novel role for decadienyl-L-carnitine in pulmonary vascular remodeling and the underlying interventional mechanism of osthole.
Chin Med 2026;21:87.
[39]
Hong L, Teng M, He M, et al. AI-driven multi-scale target analysis of traditional Chinese medicine: from the pharmacological effects of single compounds to the synergistic mechanisms of formulae.
J Adv Res 2026;S2090-1232:00272.
[40]
Ma TY, Xu ZX, Lin YG, et al. From fragments to function: a review of AI-driven mass spectrometry and fragment-based strategies.
Acupunct Herb Med 2026;6(1):28–41.
[41]
Guo R, Luo X, Liu J, et al. Omics strategies decipher therapeutic discoveries of traditional Chinese medicine against different diseases at multiple layers molecular-level.
Pharmacol Res 2020;152:104627.
[42]
Yin F, Li P, Liu C, et al. Spatially resolved multi-omics reveals the renal cortex-metabolic reprogramming of Shenhua tablet for intervention on IgA nephropathy.
Phytomedicine 2025;141:156742.
[43]
Lei C, Chen Z, Ma C, et al. Optimization of potential targets for antidepressant Chinese medicines: AI and multi-omics methods.
Chin Med 2026;21:67.
[44]
Wang T, Hu L, Lu J, et al. Functional metabolomics revealed functional metabolic-characteristics of chronic hepatitis that is significantly differentiated from acute hepatitis in mice.
Pharmacol Res 2022;180:106248.
[45]
Lao Y, Ren H, Ma Z, et al. AI enhancing differential diagnosis of acute chronic obstructive pulmonary disease and acute heart failure.
iScience 2026;29:114197.
[46]
Muthuraj R, Chandrasekaran J. Nature meets machine: the AI renaissance in natural product drug discovery.
Nat Prod Bioprospect 2026;16:37.
[47]
Rozera T, Pasolli E, Segata N, et al. Machine learning and artificial intelligence in the multi-omics approach to gut microbiota.
Gastroenterology 2025;169:487–501.
[48]
Petrick LM, Shomron N. AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications.
Cell Rep Phys Sci 2022;3:100978.
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