Applications of artificial intelligence in the research of molecular mechanisms of traditional Chinese medicine formulas

Abstract

Traditional Chinese medicine formula (TCMF) represents a fundamental component of Chinese medical practice, incorporating medical knowledge and practices from both Han Chinese and various ethnic minorities, while providing comprehensive insights into health and disease. The foundation of TCMF lies in its holistic approach, manifested through herbal compatibility theory, which has emerged from extensive clinical experience and evolved into a highly refined knowledge system. Within this framework, Chinese herbal medicines exhibit intricated characteristics, including multi-component interactions, diverse target sites, and varied biological pathways. These complexities pose significant challenges for understanding their molecular mechanisms. Contemporary advances in artificial intelligence (AI) are reshaping research in traditional Chinese medicine (TCM), offering immense potential to transform our understanding of the molecular mechanisms underlying TCMFs. This review explores the application of AI in uncovering these mechanisms, highlighting its role in compound absorption, distribution, metabolism, and excretion (ADME) prediction, molecular target identification, compound and target synergy recognition, pharmacological mechanisms exploration, and herbal formula optimization. Furthermore, the review discusses the challenges and opportunities in AI-assisted research on TCMF molecular mechanisms, promoting the modernization and globalization of TCM.

Keywords

Artificial intelligence / Traditional Chinese Medicine Formula / Molecular Mechanism / Machine learning / Knowledge graph

References

[1] Soon WW, Hariharan M, Snyder MP. High-throughput sequencing for biology and medicine. Mol Syst Biol. 2013;9:640. https://doi.org/10.1038/msb.2012.61.

[2] Tu YY. The discovery of artemisinin (qinghaosu) and gifts from Chinese medicine. Nat Med. 2011; 17(10):1217-1220. https://doi.org/10.1038/nm.2471.

[3] Wang WY, Xie Y, Zhou H, et al.Contribution of traditional Chinese medicine to the treatment of COVID-19. Phytomedicine. 2021;85:153279. https://doi.org/10.1016/j.phymed.2020.153279.

[4] Huang K, Zhang P, Zhang ZH, et al. Traditional Chinese medicine (TCM) in the treatment of COVID-19 and other viral infections: efficacies and mechanisms. Pharmacol Ther. 2021;225:107843. https://doi.org/10.1016/j.pharmthera.2021.107843.

[5] Zhang BL, Wang YYY. The fundamental study on critical scientific problem of prescriptions. Chin J Nat Med. 2005; 5:258-261. https://lib.cqvip.com/Qikan/Article/Detail?id=20208413.

[6] Zhang WD. Systems biology and modern research on traditional Chinese medicine formulas. Beijing, BJ: China Science Publishing & Media Ltd.; 2017.

[7] Guo DA, Ye M, Wu WY, et al. Systematic analytical methods for complex systems of traditional Chinese medicine and their applications in standards development. Chin Nat Med Res Dev Forum. 2013;53-56.

[8] Galke L, Ram Y, Raviv L. Deep neural networks and humans both benefit from compositional language structure. Nat Commun. 2024; 15(1):10816. https://doi.org/10.1038/s41467-024-55158-1.

[9] Jamali M, Grannan B, Cai J, et al. Semantic encoding during language comprehension at single-cell resolution. Nature. 2024; 631(8021):610-616. https://doi.org/10.1038/s41586-024-07643-2.

[10] Feuerriegel S, Maarouf A, Bär D, et al. Using natural language processing to analyse text data in behavioural science. Nat Rev Psychol. 2025; 4:96-111. https://doi.org/10.1038/s44159-024-00392-z.

[11] Khurana D, Koli A, Khatter K, et al. Natural language processing: state of the art, current trends and challenges. Multimed Tools Appl. 2022; 82:3713-3744. https://doi.org/10.1007/s11042-022-13428-4.

[12] Lewis M, Liu Y, Goyal N, et al. BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension.ACL 2020. 2020:7871-7880. https://doi.org/10.18653/v1/2020.acl-main.703.

[13] Schmidhuber J. Learning to control fast-weight memories: an alternative to dynamic recurrent networks. Neural Comput. 1992; 4(1):131-139. https://doi.org/10.1162/neco.1992.4.1.131.

[14] Pannone A, Raj A, Ravichandran H, et al. Robust chemical analysis with graphene chemosensors and machine learning. Nature. 2024; 634(8034):572-578. https://doi.org/10.1038/s41586-024-08003-w.

[15] Watson JL, Juergens D, Bennett NR, et al. De novo design of protein structure and function with RFdiffusion. Nature. 2023; 620(7976):1089-1100. https://doi.org/10.1038/s41586-023-06415-8.

[16] Graves L, Nagisetty V, Ganesh V.Amnesiac machine learning. Proc AAAI Conf Artif Intell. 2021; 35(13):11516-11524. https://doi.org/10.1609/aaai.v35i13.17371.

[17] Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science. 2015; 349:255-260. https://doi.org/10.1126/science.aaa8415.

[18] Biamonte J, Wittek P, Pancotti N, et al.Quantum machine learning. Nature. 2017; 549:195-202. https://doi.org/10.1038/nature23474.

[19] Zhao X., Wang L., Zhang YF. et al. A review of convolutional neural networks in computer vision. Artif Intell Rev. 2024;57:99. https://doi.org/10.1007/s10462-024-10721-6.

[20] M Laad, R Maurya, N Saiyed. Unveiling the vision:a comprehensive review of computer vision in AI and ML.In: 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems. 2024;1-6. https://doi.org/10.1109/ADICS58448.2024.10533631.

[21] Voulodimos A, Doulamis N, Doulamis A, et al. Deep learning for computer vision: a brief review. Comput Intell Neurosci. 2018;7068349:13. https://doi.org/10.1155/2018/7068349.

[22] Salin E, Farah B, Ayache S, et al. Are vision-language transformers learning multimodal tepresentations? A probing Perspective. Proc Conf AAAI Artif Intell. 2022; 36(10):11248-11257. https://doi.org/10.1609/aaai.v36i10.21375.

[23] Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017; 60(6):84-90. https://doi.org/10.1145/3065386.

[24] Marr D. Visual information processing: the structure and creation of visual representationsPhil. Phil Trans Soc Lond B. 1980; 290:199-218. https://doi.org/10.1098/rstb.1980.0091.

[25] Editorial. Will generative AI transform robotics? Nat Mach Intell. 2024; 6(6):579-579. https://doi.org/10.1038/s42256-024-00862-2.

[26] Kim T, Hong I, Im S, et al. Wing-strain-based flight control of flapping-wing drones through reinforcement learning. Nat Mach Intell. 2024;6:992- 1005. https://doi.org/10.1038/s42256-024-00893-9.

[27] Zhang KT, Chermprayong P, Xiao F, et al. Aerial additive manufacturing with multiple autonomous robots. Nature. 2022; 609(7928):709-717. https://doi.org/10.1038/s41586-022-04988-4.

[28] de Croon GCHE, De Wagter C, Seidl T. Enhancing optical-flow-based control by learning visual appearance cues for flying robots. Nat Mach Intell. 2021; 3(1):33-41. https://doi.org/10.1038/s42256-020-00279-7.

[29] Chen AI, Balter ML, Maguire TJ, et al. Deep learning robotic guidance for autonomous vascular access. Nat Mach Intell. 2020; 2(2):104-115. https://doi.org/10.1038/s42256-020-0148-7.

[30] Paul D, Sanap G, Shenoy S, et al. Artificial intelligence in drug discovery and development. Drug Discov Today. 2021; 26(1):80-93. https://doi.org/10.1016/j.drudis.2020.10.010.

[31] Gupta RR. Application of artificial intelligence and machine learning in drug discovery. Methods Mol Biol. Heidelberg: Humana Press. 2022: 113-124. https://doi.org/10.1007/978-1-0716-1787-8_4.

[32] Sarkar C, Das B, Rawat VS, et al. Artificial intelligence and machine learning technology driven modern drug discovery and development. Int J Mol Sci. 2023; 24(3):2026. https://doi.org/10.3390/ijms24032026.

[33] Rashid MBMA. Artificial intelligence effecting a paradigm shift in drug development. SLAS Technology. 2021; 26(1):3-15. https://doi.org/10.1177/2472630320956931.

[34] Recanatini M, Cabrelle C. Drug research meets network science: where are we? J Med Chem. 2020; 63(16):8653-8666. https://doi.org/10.1021/acs.jmedchem.9b01989.

[35] Zhang P, Wang F, Hu JY, et al. Label propagation prediction of drug-drug interactions based on clinical side effects. Sci Rep. 2015; 5(1):12339. https://doi.org/10.1038/srep12339.

[36] Shalaby KS, Rao SG, Schultz B, et al. SynDRep: a knowledge graph-enhanced tool based on synergistic partner prediction fordrug repurposing. Bioinform Adv. 2024; 5(1):vbaf092. https://doi.org/10.1101/2024.08.13.607713.

[37] Islam MK, Amaya-Ramirez D, Maigret B, et al. Molecular-evaluated and explainable drug repurposing for COVID-19 using ensemble knowledge graph embedding. Sci Rep. 2023; 13(1):1-15. https://doi.org/10.1038/s41598-022-26890-9.

[38] Mohammed MA, Abdulkareem KH, Dinar AM, et al. Rise of deep learning clinical applications and challenges in omics data: a systematic review. Diagnostics. 2023; 13(4):664. https://doi.org/10.3390/diagnostics13040664.

[39] Ii B. The power of deep learning to ligand-based novel drug discovery. Expert Opin Drug Discov. 2020; 15(7): 755-764. https://doi.org/10.1080/17460441.2020.1745183.

[40] Kuenzi BM, Park J, Fong SH, et al. Predicting drug response and synergy using a deep learning model of human cancer cells. Cancer Cell. 2020; 38(5):672-684. e676. https://doi.org/10.1016/j.ccell.2020.09.014.

[41] Exscientia. Exscientia announces first-in-human study for bristol myers squibb in-licensed PKC theta inhibitor, EXS4318. Accessed 4 November 2024. https://investors.exscientia.ai/press-releases/press-release-details/2023/Exscientia-Announces-First-in-Human-Study-for-Bristol-Myers-Squibb-In-Licensed-PKC-Theta-Inhibitor-EXS4318/default.aspx.

[42] Chakraborty S, Chopra H, Akash S, et al. Artificial intelligence (AI) is paving the way for a critical role in drug discovery, drug design, and studying drug-drug interactions - correspondence. Int J Surg. 2023; 109(10):3242. https://doi.org/10.1097/JS9.0000000000000564.

[43] Kuru HI, Tastan O, Cicek AE. MatchMaker: a deep learning framework for drug synergy prediction. IEEE/ACM Trans Comput Biol. 2022; 19(4):2334-2344. https://doi.org/10.1109/TCBB.2021.3086702.

[44] Zhang TY, Zhang LW, Payne PRO, et al. Synergistic drug combination prediction by integrating multiomics data in deep learning models. Translational Bioinformatics for Therapeutic Development. New York. NY: Springer US. 2021: 223-238. https://doi.org/10.1007/978-1-0716-0849-4_12.

[45] Zhou E, Shen Q, Hou Y. Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review. Front Pharmacol. 2024;15:1181183. https://doi.org/10.3389/fphar.2024.1181183.

[46] Ke ZP, Liu MX, Liu J, et al. The application of artificial intelligence in the research and development of traditional Chinese medicine. Int J Drug Discov Pharm. 2024; 2024: 100001. https://doi.org/10.53941/ijddp.2024.100001.

[47] Song ZL, Chen GX, Chen CYC. AI empowering traditional Chinese medicine? Chem Sci. 2024; 15(41):16844-16886. https://doi.org/10.1039/D4SC04107K.

[48] Liu M, Wang X, Zhou L, et al. Study on extraction and recognition of traditional Chinese medicine tongue manifestation: based on deep learning and migration learning. J Tradit Chin Med. 2019; 60(10):835-840. https://doi.org/10.13288/j.11-2166/r.2019.10.007.

[49] Song C, Wang B, Xu JT. A tongue image classification method based on deep transfer learning. Comput Sci Eng. 2021; 43(8):1488-1496. https://doi.org/10.3969/j.issn.1007-130X.2021.08.018.

[50] Lin Y. Facial complexion classification of traditional Chinese medicine based on statistical features and mobileViT. In: IEEE Inf Technol Netw Electr Autom Control Conf. 2021; 7:50-54. https://doi.org/10.1109/itnec60942.2024.10733073.

[51] Li MX, Xu R, Zhang T, et al. A retinal vessel segmentation method based on super-pixel and generative adversarial networks. J Integr Technol. 2020; 9(6):21-28. https://doi.org/10.12146/j.issn.2095-3135.20200707001.

[52] Du FZ, He YJ, Feng XB, et al. Research on TCM syndrome classification Algorithm based on artificial intelligence. Acta Sci Nat Univ Nankaiensis. 2023; 56(2):12-16. https://doi.org/CNKI:SUN:NKDZ.0.2023-02-001.

[53] Zeng ZL. Construction of knowledge map of Yin deficiency syndrome based on ancient books and research on key technologies. Beijing: China Academy of Chinese Medical Sciences. 2023. https://doi.org/10.27658/d.cnki.gzzyy.2022.000035.

[54] Kuang H. Construction of knowledge graph in treatise on febrile diseases and its application in Yang Ming disease. Zhejiang: Zhejiang Chinese Medical University. 2022. https://doi.org/10.27465/d.cnki.gzzyc.2021.000001.

[55] Bender A, Glen RC. Molecular similarity: a key technique in molecular informatics. Org Biomol Chem. 2004; 2(22):3204-3218. https://doi.org/10.1039/b409813g.

[56] Mackenzie PI, Somogyi AA, Miners JO. Advances in drug metabolism and pharmacogenetics research in Australia. Pharmacol Res. 2017; 116:7-19. https://doi.org/10.1016/j.phrs.2016.12.008.

[57] Kirchmair J, Göller AH, Lang D, et al. Predicting drug metabolism: experiment and/or computation? Nat Rev Drug Discovery. 2015; 14(6):387-404. https://doi.org/10.1038/nrd4581.

[58] Sun L, Mi K, Hou Y, et al. Pharmacokinetic and pharmacodynamic drug-drug Interactions: research methods and applications. Metabolites. 2023; 13(8):897. https://doi.org/10.3390/metabo13080897.

[59] Zhang X, Liu T, Fan X, et al. In silico modeling on ADME properties of natural products: classification models for blood-brain barrier permeability, its application to traditional Chinese medicine and in vitro experimental validation. J Mol Graphics Modell. 2017; 75:347-354. https://doi.org/10.1016/j.jmgm.2017.05.021.

[60] Guo FF, Tang X, Zhang W, et al. Exploration of the mechanism of traditional Chinese medicine by AI approach using unsupervised machine learning for cellular functional similarity of compounds in heterogeneous networks, XiaoErFuPi Granules as an example. Pharmacol Res. 2020;160:105077. https://doi.org/10.1016/j.phrs.2020.105077.

[61] Wang P, Li K, Tao Y, et al. TCM-ADMEpred: a novel strategy for poly-pharmacokinetics prediction of traditional Chinese medicine based on single constituent pharmacokinetics, structural similarity, and mathematical modeling. J Ethnopharmacol. 2019; 236:277-287. https://doi.org/10.1016/j.jep.2018.07.008.

[62] Zhang W, Huai Y, Miao Z, et al. Systems pharmacology for investigation of the mechanisms of action of traditional Chinese medicine in drug discovery. Front Pharmacol. 2019;10:743. https://doi.org/10.3389/fphar.2019.00743.

[63] Feinberg EN, Joshi E, Pande VS, et al. Improvement in ADMET prediction with multitask deep featurization. J Med Chem. 2020; 63(16):8835-8848. https://doi.org/10.1021/acs.jmedchem.9b02187.

[64] Duvenaud D, Maclaurin D, Aguilera-Iparraguirre J, et al. Convolutional networks on graphs for learning molecular fingerprints. Adv Neural Inf Process Syst. 2015;2:2224-2232.

[65] Yang K, Swanson K, Jin W, et al. Analyzing learned molecular representations for property prediction. J Chem Inf Model. 2019; 59(8):3370-3388. https://doi.org/10.1021/acs.jcim.9b00237.

[66] Liu K, Sun X, Jia L, et al. Chemi-Net: a molecular graph convolutional network for accurate drug property prediction. Int J Mol Sci. 2019; 20(14):3389. https://doi.org/10.3390/ijms20143389.

[67] Hou TJ, Wang JM, Zhang W, et al. ADME evaluation in drug discovery. 7. Prediction of oral absorption by correlation and classification. J Chem Inf Model. 2007; 47(1):208-18. https://doi.org/10.1021/ci600343x.

[68] Rodríguez-Pérez R, Trunzer M, Schneider N, et al. Multispecies machine learning predictions of in vitro intrinsic clearance with uncertainty quantification analyses. Mol Pharm. 2023;20:383. https://doi.org/10.1021/acs.molpharmaceut.2c00680.

[69] Menon D, Sharma AM, Ani R. Computational approaches for drug discovery: a review of artificial intelligence techniques for ADME/T prediction and virtual screening. Int Conf Comput Commun Netw Technol. 2024; 2024: 1-11. https://doi.org/10.1109/icccnt61001.2024.10724127.

[70] Wishart DS, Knox C, Guo AC, et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006; 34(Database issue): D668-D672. https://doi.org/10.1093/nar/gkj067.

[71] Ru J, Li P, Wang J, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminf. 2014; 6(1): 13. https://doi.org/10.1186/1758-2946-6-13.

[72] Pires DEV, Blundell TL, Ascher DB. pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem. 2015; 58(9):4066-4072. https://doi.org/10.1021/acs.jmedchem.5b00104.

[73] Tao W, Li B, Gao S, et al. CancerHSP: anticancer herbs database of systems pharmacology. Sci Rep. 2015; 5:11481. https://doi.org/10.1038/srep11481.

[74] Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017;7:42717. https://doi.org/10.1038/srep42717.

[75] Xiong GL, Wu ZX, Yi JC, et al. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res. 2021; 49(W1):W5-W14. https://doi.org/10.1093/nar/gkab255.

[76] Wang P, Wang S, Chen H, et al. TCMIP v2.0 powers the identification of chemical constituents available in xinglou chengqi Decoction and the exploration of pharmacological mechanisms acting on stroke complicated with tanre fushi syndrome. Front Pharmacol. 2021;12:598200. https://doi.org/10.3389/fphar.2021.598200.

[77] Fu L, Shi S, Yi J, et al. ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support. Nucleic Acids Res. 2024; 52(W1):W422-W431. https://doi.org/10.1093/nar/gkae236.

[78] Li YL, Lin ZJ, Wang Y, et al. Unraveling the mystery of efficacy in Chinese medicine formula: new approaches and technologies for research on pharmacodynamic substances. Arabian J Chem. 2022; 15(11):104302. https://doi.org/10.1016/j.arabjc.2022.104302.

[79] Luan X, Zhang LJ, Li XQ, et al. Compound-based Chinese medicine formula: from discovery to compatibility mechanism. J Ethnopharmacol. 2020;254:112687. https://doi.org/10.1016/j.jep.2020.112687.

[80] Zhang JH, Zhu Y, Fan XH, et al. Efficacy-oriented compatibility for component-based Chinese medicine. Acta Pharmacol Sin. 2015; 36(6):654-658. https://doi.org/10.1038/aps.2015.8.

[81] Wu Y, Liu XQ, Li GW. Integrated bioinformatics and network pharmacology to identify the therapeutic target and molecular mechanisms of Huangqin Decoction on ulcerative colitis. Sci Rep. 2022; 12(1): 159. https://doi.org/10.1038/s41598-021-03980-8.

[82] Liu Y, Luo D, Xu B. The combination of molecular docking and network pharmacology reveals the molecular mechanism of Danggui Niantong Decoction in treating gout. Medicine. 2022; 101(47):e31535. https://doi.org/10.1097/MD.0000000000031535.

[83] Xue RC, Fang Z, Zhang MX, et al. TCMID: traditional Chinese medicine integrative database for herb molecular mechanism analysis. Nucleic Acids Res. 2012; 41(D1):D1089-D1095. https://doi.org/10.1093/nar/gks1100.

[84] Liu ZY, Guo FF, Wang Y, et al. BATMAN-TCM: a bioinformatics analysis tool for molecular mechanism of traditional Chinese medicine. Sci Rep. 2016; 6(1):21146. https://doi.org/10.1038/srep21146.

[85] Xu HY, Zhang YQ, Liu ZM, et al. ETCM: an encyclopaedia of traditional Chinese medicine. Nucleic Acids Res. 2019; 47(D1):D976-D982. https://doi.org/10.1093/nar/gky987.

[86] Wu Y, Zhang FL, Yang K, et al. SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping. Nucleic Acids Res. 2019; 47(D1):D1110-D1117. https://doi.org/10.1093/nar/gky1021.

[87] Fang SS, Dong L, Liu L, et al. HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine. Nucleic Acids Res. 2020; 49(D1):D1197-D1206. https://doi.org/10.1093/nar/gkaa1063.

[88] Tian SS, Zhang JB, Yuan SL, et al. Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM. Briefings Bioinf. 2023; 24(2):bbad027. https://doi.org/10.1093/bib/bbad027.

[89] Liu XY, Liu JY, Fu BZ, et al. DCABM-TCM: a database of constituents absorbed into the blood and metabolites of traditional Chinese medicine. J Chem Inf Model. 2023; 63(15):4948-4959. https://doi.org/10.1021/acs.jcim.3c00365.

[90] Lv QJ, Chen GX, He HH, et al. TCMBank-the largest TCM database provides deep learning-based Chinese-Western medicine exclusion prediction. Signal Transduction Targeted Ther. 2023; 8(1):127. https://doi.org/10.1038/s41392-023-01339-1.

[91] Yang DQ, Zhu Z, Yao Q, et al. ccTCM: a quantitative component and compound platform for promoting the research of traditional Chinese medicine. Comput Struct Biotechnol. 2023; 21: 5807-5817. https://doi.org/10.1016/j.csbj.2023.11.030.

[92] Ye YN, Liang DF, Yi JH, et al. IGTCM: an integrative genome database of traditional Chinese medicine plants. The Plant Genome. 2023; 16(2):e20317. https://doi.org/10.1002/tpg2.20317.

[93] Ren ZX, Ren YM, Li ZT, et al. TCMM: a unified database for traditional Chinese medicine modernization and therapeutic innovations. Comput Struct Biotechnol J. 2024; 23:1619-1630. https://doi.org/10.1016/j.csbj.2024.04.016.

[94] Alexander S, Friedl P. Cancer invasion and resistance: interconnected processes of disease progression and therapy failure. Trends Mol Med. 2012; 18(1):13-26. https://doi.org/10.1016/j.molmed.2011.11.003.

[95] Luo M, Jiao JF, Wang RQ. Screening drug target combinations in disease-related molecular networks. BMC Bioinf. 2019; 20(Suppl 7):198. https://doi.org/10.1186/s12859-019-2730-8.

[96] Mahase E. Breast cancer: NICE approves new drug combination treatment. BMJ. 2019; 2019: l4727. https://doi.org/10.1136/bmj.l4727.

[97] Peraman R, Sure SK, Dusthackeer VNA, et al. Insights on recent approaches in drug discovery strategies and untapped drug targets against drug resistance. Future J Pharm Sci. 2021; 7(1):56. https://doi.org/10.1186/s43094-021-00196-5.

[98] Zhou X, Seto SW, Chang D, et al. Synergistic effects of Chinese herbal medicine: a comprehensive review of methodology and current research. Front Pharmacol. 2016;7:201. https://doi.org/10.3389/fphar.2016.00201.

[99] Malik MA, Wani MY, Hashmi AA. Combination therapy:current status and future perspectives. In: Combination Therapy Against Multidrug Resistance Bacteria. Springer. 2020:1-38. https://doi.org/10.1016/B978-0-12-820576-1.00001-1.

[100] Pan YC, Ren HT, Lan L, et al. Review of predicting synergistic drug combinations. Life (Basel). 2023; 13(9):1878. https://doi.org/10.3390/life13091878.

[101] Naveja JJ, Stumpfe D, Medina-Franco JL, et al. Exploration of target synergy in cancer treatment by cell-based screening assay and network propagation analysis. J Chem Inf Model. 2019; 59(6):3072-3079. https://doi.org/10.1021/acs.jcim.9b00036.

[102] Cheng FX, Kovács IA, Barabási AL.Network-based prediction of drug combinations. Nat Commun. 2019; 10(1):1-11. https://doi.org/10.1038/s41467-018-07882-8.

[103] Zheng SC, Baak JP, Li S, et al.Network pharmacology analysis of the therapeutic mechanisms of the traditional Chinese herbal formula Lian Hua Qing Wen in corona virus disease 2019 (COVID-19), gives fundamental support to the clinical use of LHQW. Phytomedicine.2020; 79:153336. https://doi.org/10.1016/j.phymed.2020.153336.

[104] Li S, Zhang B. Traditional Chinese medicine network pharmacology: theory, methodology and application. Chin J Nat Med. 2013; 11(2):110-120. https://doi.org/10.1016/S1875-5364(13)60037-0.

[105] Wang YY, Yang HB, Chen LX, et al. Network-based modeling of herb combinations in traditional Chinese medicine. Briefings Bioinf. 2021; 22(5):bbab106. https://doi.org/10.1093/bib/bbab106.

[106] Gan X, Shu Z, Wang X, et al. Network medicine framework reveals generic herb-symptom effectiveness of traditional Chinese medicine. Sci Adv. 2023;9:eadh0215. https://doi.org/10.1126/sciadv.adh0215.

[107] Shi YL, Liu J, Guan S, et al. Syn-COM: a multi-level predictive synergy framework for innovative drug combinations. Pharmaceuticals. 2024; 17(9):1230. https://doi.org/10.3390/ph17091230.

[108] Jeon M, Kim S, Park S, et al. In silico drug combination discovery for personalized cancer therapy. BMC Syst Biol. 2018; 12(2):16. https://doi.org/10.1186/s12918-018-0546-1.

[109] Ung CY, Li H, Cao ZW, et al. Are herb-pairs of traditional Chinese medicine distinguishable from others? Pattern analysis and artificial intelligence classification study of traditionally defined herbal properties. J Ethnopharmacol. 2007; 111(2):371-377. https://doi.org/10.1016/j.jep.2006.11.037.

[110] Xue Q, Gao B, Wen J, et al. A deep learning-based herb pair discovering approach. Big Data. 2022; 1496: 135-149. https://doi.org/10.1007/978-981-16-9709-8_10.

[111] Yang F, Zhang SJ, Pan W, et al. Signaling repurposable drug combinations against COVID-19 by developing the heterogeneous deep herb-graph method. Briefings Bioinf. 2022; 23(5):bbac124. https://doi.org/10.1093/bib/bbac124.

[112] Preuer K, Lewis RPI, Hochreiter S, et al. DeepSynergy: predicting anti-cancer drug synergy with deep learning. Bioinformatics. 2018; 34(9):1538-1546. https://doi.org/10.1093/bioinformatics/btx806.

[113] Chen J, Wang YK, Gao Y, et al. Protection against COVID-19 injury by Qingfei Paidu Decoction via anti-viral, anti-inflammatory activity and metabolic programming. Biomed Pharmacother. 2020;129:110281. https://doi.org/10.1016/j.biopha.2020.110281.

[114] Li S, Zhang B, Zhang NB. Network target for screening synergistic drug combinations with application to traditional Chinese medicine. BMC Syst Biol. 2011; 5(1):S10. https://doi.org/10.1186/1752-0509-5-10.

[115] Huang L, Li FH, Sheng JT, et al. DrugComboRanker: drug combination discovery based on target network analysis. Bioinformatics. 2014; 30(12):i228-i236. https://doi.org/10.1093/bioinformatics/btu278.

[116] Sun BZ, Liu CX, Li YL, et al. An approach to co-medication mechanism mining of Chinese Materia Medica and western medicines based on complex networks with the multi-source heterogeneous information. Inf Fusion. 2024;102:102081. https://doi.org/10.1016/j.inffus.2023.102081.

[117] Li X, Liu Z, Liao J, et al. Network pharmacology approaches for research of traditional Chinese medicines. Chin J Nat Med. 2023; 21(5):323-332. https://doi.org/10.1016/S1875-5364(23)60429-7.

[118] Zhu X, Yao Q, Yang P, et al. Multi-omics approaches for in-depth understanding of therapeutic mechanism for traditional Chinese medicine. Front Pharmacol. 2022;13:1031051. https://doi.org/10.3389/fphar.2022.1031051.

[119] Xiong H, Li N, Zhao LQQ, et al. Integrated serum pharmacochemistry, metabolomics, and network pharmacology to reveal the material basis and mechanism of Danggui Shaoyao San in the treatment of primary dysmenorrhea. Front Pharmacol. 2022;13:942955. https://doi.org/10.3389/fphar.2022.942955.

[120] Lu X, Ma W, Fan B, et al. Integrating network pharmacology, transcriptome and artificial intelligence for investigating into the effect and mechanism of Ning Fei Ping Xue Decoction against the acute respiratory distress syndrome. Front Pharmacol. 2021;12:731377. https://doi.org/10.3389/fphar.2021.731377.

[121] Jiang YP, Zheng YF, Dong Q, et al. Metabolomics combined with network pharmacology to study the mechanism of Shentong Zhuyu Decoction in the treatment of rheumatoid arthritis. J Ethnopharmacol. 2022;285:114846. https://doi.org/10.1016/j.jep.2021.114846.

[122] Ma C, Zhao J, Zheng GY, et al. Qijiao Shengbai Capsule alleviated leukopenia by interfering leukotriene pathway: integrated network study of multi-omics. Phytomedicine. 2024;128:155424. https://doi.org/10.1016/j.phymed.2024.155424.

[123] Ruchawapol C, Fu WW, Xu HX.A review on computational approaches that support the researches on traditional Chinese medicines (TCM) against COVID-19. Phytomedicine. 2022;104:154324. https://doi.org/10.1016/j.phymed.2022.154324.

[124] Wang ZH, Li L, Song M, et al. Evaluating the traditional Chinese medicine (TCM) officially recommended in china for COVID-19 using ontology-based side-effect prediction framework (OSPF) and deep learning. J Ethnopharmacol. 2021;272:113957. https://doi.org/10.1016/j.jep.2021.113957.

[125] Zhao J, Tian S, Lu D, et al.Systems pharmacological study illustrates the immune regulation, anti-infection, anti-inflammation, and multi-organ protection mechanism of Qing-Fei-Pai-Du Decoction in the treatment of COVID-19. Phytomedicine. 2021;85:153315. https://doi.org/10.1016/j.phymed.2020.153315.

[126] Qi GP, Zhang AJ, Xu Z, et al. Application of a complex network modeling approach to explore the material basis and mechanisms of traditional Chinese medicine: a case study of Xuefu Zhuyu Decoction for the treatment of two types of angina pectoris. IEEE Access. 2022; 10:114103-114117. https://doi.org/10.1109/ACCESS.2022.3217926.

[127] Fu Y, Fang YY, Gong S, et al.Deep learning-based network pharmacology for exploring the mechanism of licorice for the treatment of COVID-19. Sci Rep. 2023; 13(1):5844. https://doi.org/10.1038/s41598-023-31380-7.

[128] Li J, Liu H, Yang ZZ, et al. Synergistic effects of cryptotanshinone and senkyunolide I in Guanxinning Tablet against endogenous thrombus formation in zebrafish. Front Pharmacol. 2020;11:622787. https://doi.org/10.3389/fphar.2020.622787.

[129] Eltager M, Abdelaal T, Mahfouz A, et al.scMoC: single-cell multi-omics clustering. Bioinf Adv. 2022; 2(1): vbac011. https://doi.org/10.1093/bioadv/vbac011.

[130] Flores JE, Claborne DM, Weller ZD, et al. Missing data in multi-omics integration: recent advances through artificial intelligence. Front Artif Intell. 2023; 6:1098308. https://doi.org/10.3389/frai.2023.1098308.

[131] Cao ZJ, Gao G. Multi-omics single-cell data integration and regulatory inference with graph-linked embedding. Nat Biotechnol. 2022; 40(10):1458-1466. https://doi.org/10.1038/s41587-022-01284-4.

[132] Pan HW, He SN, Zhang KJ, et al. AMAM: an attention-based multimodal alignment model for medical visual question answering. Knowledge-Based Syst. 2022;255:109763. https://doi.org/10.1016/j.knosys.2022.109763.

[133] Shen T, Zhang F, Cheng J. A comprehensive overview of knowledge graph completion. Knowledge-Based Syst. 2022;255:109597. https://doi.org/10.1016/j.knosys.2022.109597.

[134] Yu ZX, Li JT, Chen L, et al. Unifying multi-associations through hypergraph for bundle recommendation. Knowledge-Based Syst. 2022;255:109755. https://doi.org/10.1016/j.knosys.2022.109755.

[135] Alshahrani M, Almansour A, Alkhaldi A, et al. Combining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications. PeerJ. 2022;10:e13061. https://doi.org/10.7717/peerj.13061.

[136] Bonner S, Barrett IP, Ye C, et al. Understanding the performance of knowledge graph embeddings in drug discovery. Artif Intell Life Sci. 2022;2:100036. https://doi.org/10.1016/j.ailsci.2022.100036.

[137] Ali M, Hoyt CT, Domingo-Fernández D, et al. BioKEEN: a library for learning and evaluating biological knowledge graph embeddings. Bioinformatics. 2019; 35(18):3538-3540. https://doi.org/10.1093/bioinformatics/btz117.

[138] Zhou C, Cai CP, Huang XT, et al. TarKG: a comprehensive biomedical knowledge graph for target discovery. Bioinformatics. 2024; 40(10):btae598. https://doi.org/10.1093/bioinformatics/btae598.

[139] Zhu X, Gu YM, Xiao ZF. HerbKG: constructing a herbal-molecular medicine knowledge graph using a two-stage framework based on deep dtransfer learning. Front Genet. 2022;13:799349. https://doi.org/10.3389/fgene.2022.799349.

[140] Liu Z, Luo CY, Zheng ZY, et al. TCMNER and PubMed: a novel Chinese character-level-based model and a dataset for TCM named entity recognition. J Healthcare Eng. 2021;2021:3544281. https://doi.org/10.1155/2021/3544281.

[141] Cyranoski D. Why Chinese medicine is heading for clinics around the world. Nature. 2018; 561(7724):448-450. https://doi.org/10.1038/d41586-018-06782-7.

[142] He SB, Zhang XL, Lu S, et al. A computational toxicology approach to screen the hepatotoxic ingredients in traditional Chinese medicines: polygonum multiflorum thunb as a case study. Biomolecules. 2019; 9(10):577. https://doi.org/10.3390/biom9100577.

[143] Wu KD, Wei GW. Quantitative toxicity prediction using topology based multitask deep neural networks. J Chem Inf Model. 2018; 58(2):520-531. https://doi.org/10.1021/acs.jcim.7b00558.

[144] Tran TTV, Surya Wibowo A, Tayara H, et al. Artificial intelligence in drug toxicity prediction: recent advances, challenges, and future perspectives. J Chem Inf Model. 2023; 63(9):2628-2643. https://doi.org/10.1021/acs.jcim.3c00200.

[145] Ryu JY, Lee MY, Lee JH, et al. DeepHIT: a deep learning framework for prediction of hERG-induced cardiotoxicity. Bioinformatics. 2020; 36(10):3049-3055. https://doi.org/10.1093/bioinformatics/btaa075.

[146] Venkatraman V. FP-ADMET: a compendium of fingerprint-based ADMET prediction models. J Cheminf. 2021; 13(1):75. https://doi.org/10.1186/s13321-021-00557-5.

[147] Chen Z, Jiang Y, Zhang XY, et al. ResNet18DNN: prediction approach of drug-induced liver injury by deep neural network with ResNet18. Briefings Bioinf. 2021; 23(1):bbab503. https://doi.org/10.1093/bib/bbab503.

[148] Zhou WA, Yang K, Zeng JY, et al. FordNet: recommending traditional Chinese medicine formula via deep neural network integrating phenotype and molecule. Pharmacol Res. 2021;173:105752. https://doi.org/10.1016/j.phrs.2021.105752.

[149] Zhao W, Lu WK, Li ZY, et al. TCM herbal prescription recommendation model based on multi-graph convolutional network. J Ethnopharmacol. 2022;297:115109. https://doi.org/10.1016/j.jep.2022.115109.

[150] Jin YY, Ji WD, Shi Y, et al. Meta-path guided graph attention network for explainable herb recommendation. Health Inf Sci. 2023; 11(1):5. https://doi.org/10.1007/s13755-022-00207-6.

[151] Basu T, Wolf S, Menzer O. The ethics of machine learning in medical sciences: where do we stand today? Indian J Dermatol. 2020;65:358. https://doi.org/10.4103/ijd.IJD_419_20.

[152] Shimao H, Khern-am-nuai W, Kannan K, et al. Strategic best response fairness in fair machine learning.AIES '22. 2022; 664. https://doi.org/10.1145/3514094.3534194.

[153] Li MY, Zhang J. A focus on harnessing big data and artificial intelligence: revolutionizing drug discovery from traditional Chinese medicine sources. Chem Sci. 2023; 14(39):10628-10630. https://doi.org/10.1039/D3SC90185H.

[154] Wang YY, Liu MX, Jafari M, et al. A critical assessment of traditional Chinese medicine databases as a source for drug discovery. Front Pharmacol. 2024;15:1303693. https://doi.org/10.3389/fphar.2024.1303693.

[155] Qu QQ, Kan HX, Wu YT, et al. Named entity recognition of TCM text based on bert model. Proc-Int Forum Electr Eng Autom. 2020; 2020: 652-655. https://doi.org/10.1109/IFEEA51475.2020.00139.

[156] Sandfort V, Yan K, Pickhardt PJ, et al. Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks. Sci Rep. 2019; 9(1):1-9. https://doi.org/10.1038/s41598-018-37186-2.

[157] Zou JP, Yu JL, Hu PW, et al. STAGAN: an approach for improve the stability of molecular graph generation based on generative adversarial networks. Comput Biol Med. 2023;167:107691. https://doi.org/10.1016/j.compbiomed.2023.107691.

[158] Chen YY, Wang ZX, WangL, et al. Deep generative model for drug design from protein target sequence. J Cheminf. 2023; 15(1):38. https://doi.org/10.1186/s13321-023-00702-2.

[159] Taneja SB, Callahan TJ, Paine MF, et al. Developing a knowledge graph for pharmacokinetic natural product-drug interactions. J Biomed Inf. 2023;140:104341. https://doi.org/10.1016/j.jbi.2023.104341.

[160] Tsubaki M, Tomii K, Sese J. Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences. Bioinformatics. 2019; 35(2):309-318. https://doi.org/10.1093/bioinformatics/bty535.

[161] Georgousis S, Kenning MP, Xie X. Graph deep learning: state of the art and challenges. IEEE Access. 2021; 9:22106-22140. https://doi.org/10.1109/ACCESS.2021.3055280.

[162] Kojima R, Ishida S, Ohta M, et al. kGCN: a graph-based deep learning framework for chemical structures. J Cheminf. 2020; 12(1):32. https://doi.org/10.1186/s13321-020-00435-6.

[163] Wu ZH, Zhang XL, Lin XL. KGAT: predicting drug-target interaction based on knowledge graph attention network.ICIC 2022. 2022; 13394: 438-450. https://doi.org/10.1007/978-3-031-13829-4_38.

[164] Gong Z, Zhang NX, He J. KGRN: knowledge graph relational path network for target prediction of TCM prescriptions.ICIC 2021. 2021; 2021: 148-161. https://doi.org/10.1007/978-3-030-84532-2_14.

[165] Duan P, Yang K, Su X, et al. HTINet2: herb-target prediction via knowledge graph embedding and residual-like graph neural network. Briefings Bioinf. 2024; 25(5):bbae414. https://doi.org/10.1093/bib/bbae414.

[166] Guo SY, Deng ZH, Li Y, et al. A review of theoretical research on interpretation of scientific connotation of compatibility of traditional Chinese medicine compounds. Chin J Exp Tradit Med Formulae. 2024; 30(23):338-344. https://doi.org/10.13422/j.cnki.syfjx.20241645.

[167] Amador-Domínguez E, Serrano E, Manrique D. GEnI: a framework for the generation of explanations and insights of knowledge graph embedding predictions. Neurocomputing. 2023; 521:199-212. https://doi.org/10.1016/j.neucom.2022.12.010.

[168] Gaur M, Faldu K, Sheth A. Semantics of the black-box: can knowledge graphs help make deep learning systems more interpretable and explainable? IEEE Internet Comput. 2021; 25(1):51-59. https://doi.org/10.1109/MIC.2020.3031769.

[169] Qu XL, Tian ZW, Cui JM, et al. A review of knowledge graph in traditional Chinese medicine: analysis, construction, application and prospects. Cmc-Comput Mater Con. 2024; 81(3): 3583--3616. https://doi.org/10.32604/cmc.2024.055671.

[170] Qian YY, Wang XT, Cai LL, et al. Model informed precision medicine of Chinese herbal medicines formulas-A multi-scale mechanistic intelligent model. J Pharm Anal. 2024; 14(4):100914. https://doi.org/10.1016/j.jpha.2023.12.004.

[171] Cheng H, Liu W, Qu Y, et al. Unleashing the potential of decentralized federated learning in healthcare: a comprehensive survey on insights and challenges. IEEE Int Conf Data Sci Cyberspace. 2024; 2024: 53-60. https://doi.org/10.1109/DSC63484.2024.00015.

[172] Acar A, Aksu H, Uluagac AS, et al. A survey on homomorphic encryption schemes: theory and implementation. ACM Comput Surv. 2018; 51(4): 79. https://doi.org/10.1145/3214303.