doi:10.48130/targetome-0026-0027

[1]

Guo DA, Lu A, Liu L. 2012. Modernization of traditional Chinese medicine. Journal of Ethnopharmacology 141:547−548

doi: 10.1016/j.jep.2012.05.001

[2]

Newman DJ, Cragg GM. 2020. Natural products as sources of new drugs over the nearly four decades from 01/1981 to 09/2019. The Journal of Natural Products 83:770−803

doi: 10.1021/acs.jnatprod.9b01285

[3]

Saldívar-González FI, Aldas-Bulos VD, Medina-Franco JL, Plisson F. 2022. Natural product drug discovery in the artificial intelligence era. Chemical Science 13:1526−1546

doi: 10.1039/D1SC04471K

[4]

Banihashemi ZS, Azizi-Fini I, Rajabi M, Maghami M, Yadollahi S. 2025. Chronic fatigue syndrome post-COVID-19: triple-blind randomised clinical trial of Astragalus root extract. BMJ Support Palliat Care 15:359−366

doi: 10.1136/spcare-2023-004595

[5]

Zhang R, Zhu X, Bai H, Ning K. 2019. Network pharmacology databases for traditional Chinese medicine: review and assessment. Frontiers in Pharmacology 10:123

doi: 10.3389/fphar.2019.00123

[6]

Wang Y, Liu B, Fu X, Tong T, Yu Z. 2021. Efficacy and safety of Si-Jun-Zi-Tang-based therapies for functional (non-ulcer) dyspepsia: a meta-analysis of randomized controlled trials. BMC Complementary Medicine and Therapies 21:11

doi: 10.1186/s12906-020-03176-z

[7]

Wang X, Shi X, Xi Z, Zhang Z, Luo Z, et al. 2026. The scientific basis of synergy in traditional Chinese medicine: physicochemical, pharmacokinetic, and pharmacodynamic perspectives. Chinese Medicine 21:15

doi: 10.1186/s13020-025-01291-y

[8]

Liu J, Wei LX, Wang Q, Lu YF, Zhang F, et al. 2018. A review of cinnabar (HgS) and/or realgar (As₄S₄)-containing traditional medicines. Journal of Ethnopharmacology 210:340−350

doi: 10.1016/j.jep.2017.08.037

[9]

Huang C, Zhu Z, Cao X, Chen X, Fu Y, et al. 2017. A pectic polysaccharide from Sijunzi decoction promotes the antioxidant defenses of SW480 cells. Molecules 22:1341

doi: 10.3390/molecules22081341

[10]

Xu B, Li X, Hu S, Bao Y, Chen F, et al. 2022. Safety and efficacy of Yupingfeng granules in children with recurrent respiratory tract infection: a randomized clinical trial. Pediatric Investigation 6:75−84

doi: 10.1002/ped4.12326

[11]

Song T, Hou X, Yu X, Wang Z, Wang R, et al. 2016. Adjuvant treatment with yupingfeng formula for recurrent respiratory tract infections in children: a meta-analysis of randomized controlled trials. Phytotherapy Research 30:1095−103

doi: 10.1002/ptr.5628

[12]

Xin S, Cheng X, Zhu B, Liao X, Yang F, et al. 2020. Clinical retrospective study on the efficacy of Qingfei Paidu decoction combined with Western medicine for COVID-19 treatment. Biomedicine & Pharmacotherapy 129:110500

doi: 10.1016/j.biopha.2020.110500

[13]

Liu S, Yao C, Xie J, Liu H, Wang H, et al. 2023. Effect of an herbal-based injection on 28-day mortality in patients with sepsis: the EXIT-SEP randomized clinical trial. JAMA Internal Medicine 183:647−655

doi: 10.1001/jamainternmed.2023.0780

[14]

Li X, Zhang J, Huang J, Ma A, Yang J, et al. 2013. A multicenter, randomized, double-blind, parallel-group, placebo-controlled study of the effects of qili qiangxin capsules in patients with chronic heart failure. Journal of the American College of Cardiology 62:1065−1072

doi: 10.1016/j.jacc.2013.05.035

[15]

Li S, Zhang B. 2013. Traditional Chinese medicine network pharmacology: theory, methodology and application. Chinese Journal of Natural Medicines 11:110−20

doi: 10.1016/S1875-5364(13)60037-0

[16]

Qu J, Zhang T, Liu J, Su Y, Wang H. 2019. Considerations for the quality control of newly registered traditional Chinese medicine in China: a review. Journal of AOAC International 102:689−694

doi: 10.5740/jaoacint.18-0301

[17]

Newman DJ. 2019. The impact of decreasing biodiversity on novel drug discovery: is there a serious cause for concern? Expert Opinion on Drug Discovery 14:521−525

doi: 10.1080/17460441.2019.1593370

[18]

Ren YS, Li HL, Piao XH, Yang ZY, Wang SM, et al. 2021. Drug affinity responsive target stability (DARTS) accelerated small molecules target discovery: principles and application. Biochemical Pharmacology 194:114798

doi: 10.1016/j.bcp.2021.114798

[19]

Xu Y, Liu X, Cao X, Huang C, Liu E, et al. 2021. Artificial intelligence: a powerful paradigm for scientific research. Innovation 2:100179

doi: 10.1016/j.xinn.2021.100179

[20]

Kaul V, Enslin S, Gross SA. 2020. History of artificial intelligence in medicine. Gastrointestinal Endoscopy 92:807−812

doi: 10.1016/j.gie.2020.06.040

[21]

Alakhdar A, Poczos B, Washburn N. 2024. Diffusion models in de novo drug design. Journal of Chemical Information and Modeling 64:7238−7256

doi: 10.1021/acs.jcim.4c01107

[22]

Jiang J, Chen L, Ke L, Dou B, Zhang C, et al. 2025. A review of transformer models in drug discovery and beyond. Journal of Pharmaceutical Analysis 15:101081

doi: 10.1016/j.jpha.2024.101081

[23]

He X, Sun M, Battulga T, Copp BR, Rustamovna DK, et al. 2025. Application of artificial intelligence in the development of traditional Chinese medicine. Basic & Clinical Pharmacology and Toxicology 137:e70066

doi: 10.1111/bcpt.70066

[24]

Chen W, Liu X, Zhang S, Chen S. 2023. Artificial intelligence for drug discovery: resources, methods, and applications. Molecular Therapy Nucleic Acids 31:691−702

doi: 10.1016/j.omtn.2023.02.019

[25]

Graber D, Stockinger P, Meyer F, Mishra S, Horn C, et al. 2025. Resolving data bias improves generalization in binding affinity prediction. Nature Machine Intelligence 7:1713−1725

doi: 10.1038/s42256-025-01124-5

[26]

Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, et al. 2021. Artificial intelligence in drug discovery and development. Drug Discovery Today 26:80−93

doi: 10.1016/j.drudis.2020.10.010

[27]

Noor F, Asif M, Ashfaq UA, Qasim M, Tahir ul Qamar M. 2023. Machine learning for synergistic network pharmacology: a comprehensive overview. Briefings in Bioinformatics 24:bbad120

doi: 10.1093/bib/bbad120

[28]

Liu P, Huang F, Zheng X, Hao H. 2025. Targetome-guided combination drug discovery as next-generation therapeutics. Targetome 1:e002

doi: 10.48130/targetome-0025-0002

[29]

Mullowney MW, Duncan KR, Elsayed SS, Garg N, van der Hooft JJJ, et al. 2023. Artificial intelligence for natural product drug discovery. Nature Reviews Drug Discovery 22:895−916

doi: 10.1038/s41573-023-00774-7

[30]

Li X, Li X, Wang L, Hou Y, Liu Y, et al. 2025. Advancing traditional Chinese medicine research through network pharmacology: strategies for target identification, mechanism elucidation and innovative therapeutic applications. The American Journal of Chinese Medicine 53:2021−2042

doi: 10.1142/S0192415X25500752

[31]

Tian S, Zhang J, Yuan S, Wang Q, Lv C, et al. 2023. Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM. Briefings in Bioinformatics 24:bbad027

doi: 10.1093/bib/bbad027

[32]

Yan D, Zheng G, Wang C, Chen Z, Mao T, et al. 2022. HIT 2.0: an enhanced platform for Herbal Ingredients' Targets. Nucleic Acids Research 50:D1238−D1243

doi: 10.1093/nar/gkab1011

[33]

Knox C, Wilson M, Klinger CM, Franklin M, Oler E, et al. 2024. DrugBank 6.0: the DrugBank Knowledgebase for 2024. Nucleic Acids Research 52:D1265−D1275

doi: 10.1093/nar/gkad976

[34]

Liu Z, Cai C, Du J, Liu B, Cui L, et al. 2020. TCMIO: a comprehensive database of traditional Chinese medicine on immuno-oncology. Frontiers in Pharmacology 11:439

doi: 10.3389/fphar.2020.00439

[35]

Zhang Y, Li X, Shi Y, Chen T, Xu Z, et al. 2023. ETCM v2.0: an update with comprehensive resource and rich annotations for traditional Chinese medicine. Acta Pharmaceutica Sinica B 13:2559−2571

doi: 10.1016/j.apsb.2023.03.012

[36]

Fang S, Dong L, Liu L, Guo J, Zhao L, et al. 2021. HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine. Nucleic Acids Research 49:D1197−D1206

doi: 10.1093/nar/gkaa1063

[37]

Chen Q, Springer L, Gohlke BO, Goede A, Dunkel M, et al. 2021. SuperTCM: a biocultural database combining biological pathways and historical linguistic data of Chinese Materia Medica for drug development. Biomedicine & Pharmacotherapy 144:112315

doi: 10.1016/j.biopha.2021.112315

[38]

Kim S, Chen J, Cheng T, Gindulyte A, He J, et al. 2019. PubChem 2019 update: improved access to chemical data. Nucleic Acids Research 47:D1102−D1109

doi: 10.1093/nar/gky1033

[39]

Zdrazil B, Felix E, Hunter F, Manners EJ, Blackshaw J, et al. 2024. The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic Acids Research 52:D1180−D1192

doi: 10.1093/nar/gkad1004

[40]

Liu T, Hwang L, Burley SK, Nitsche CI, Southan C, et al. 2025. BindingDB in 2024: a FAIR knowledgebase of protein-small molecule binding data. Nucleic Acids Research 53:D1633−D1644

doi: 10.1093/nar/gkae1075

[41]

Avram S, Wilson TB, Curpan R, Halip L, Borota A, et al. 2023. DrugCentral 2023 extends human clinical data and integrates veterinary drugs. Nucleic Acids Research 51:D1276−D1287

doi: 10.1093/nar/gkac1085

[42]

Siramshetty VB, Eckert OA, Gohlke BO, Goede A, Chen Q, et al. 2018. SuperDRUG2: a one stop resource for approved/marketed drugs. Nucleic Acids Research 46:D1137−D1143

doi: 10.1093/nar/gkx1088

[43]

Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, et al. 2016. The GeneCards Suite: from gene data mining to disease genome sequence analyses. Current Protocols in Bioinformatics 54:1.30.1−1.30.33

doi: 10.1002/cpbi.5

[44]

Zhou Y, Zhang Y, Zhao D, Yu X, Shen X, et al. 2024. TTD: Therapeutic Target Database describing target druggability information. Nucleic Acids Research 52:D1465−D1477

doi: 10.1093/nar/gkad751

[45]

Piñero J, Ramírez-Anguita JM, Saüch-Pitarch J, Ronzano F, Centeno E, et al. 2020. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Research 48:D845−D855

doi: 10.1093/nar/gkz1021

[46]

Lv Q, Chen G, He H, Yang Z, Zhao L, et al. 2023. TCMBank: bridges between the largest herbal medicines, chemical ingredients, target proteins, and associated diseases with intelligence text mining. Chemical Science 14:10684−10701

doi: 10.1039/D3SC02139D

[47]

Gao K, Liu L, Lei S, Li Z, Huo P, et al. 2025. HERB 2.0: an updated database integrating clinical and experimental evidence for traditional Chinese medicine. Nucleic Acids Research 53:D1404−D1414

doi: 10.1093/nar/gkae1037

[48]

Kim SK, Lee MK, Jang H, Lee JJ, Lee S, et al. 2024. TM-MC 2.0: an enhanced chemical database of medicinal materials in Northeast Asian traditional medicine. BMC Complementary Medicine and Therapies 24:40

doi: 10.1186/s12906-023-04331-y

[49]

Yang P, Lang J, Li H, Lu J, Lin H, et al. 2022. TCM-Suite: a comprehensive and holistic platform for Traditional Chinese Medicine component identification and network pharmacology analysis. iMeta 1:e47

doi: 10.1002/imt2.47

[50]

Liu X, Liu J, Fu B, Chen R, Jiang J, et al. 2023. DCABM-TCM: a database of constituents absorbed into the blood and metabolites of traditional Chinese medicine. Journal of Chemical Information and Modeling 63:4948−4959

doi: 10.1021/acs.jcim.3c00365

[51]

Zhang LX, Dong J, Wei H, Shi SH, Lu AP, et al. 2022. TCMSID: a simplified integrated database for drug discovery from traditional chinese medicine. Journal of Cheminformatics 14:89

doi: 10.1186/s13321-022-00670-z

[52]

Wu Y, Zhang F, Yang K, Fang S, Bu D, et al. 2019. SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping. Nucleic Acids Research 47:D1110−D1117

doi: 10.1093/nar/gky1021

[53]

Huang L, Xie D, Yu Y, Liu H, Shi Y, et al. 2018. TCMID 2. 0: a comprehensive resource for TCM. Nucleic Acids Research 46:D1117−D1120

doi: 10.1093/nar/gkx1028

[54]

Li B, Ma C, Zhao X, Hu Z, Du T, et al. 2018. YaTCM: yet another traditional Chinese medicine database for drug discovery. Computational and Structural Biotechnology Journal 16:600−610

doi: 10.1016/j.csbj.2018.11.002

[55]

Ru J, Li P, Wang J, Zhou W, Li B, et al. 2014. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. Journal of Cheminformatics 6:13

doi: 10.1186/1758-2946-6-13

[56]

Yang D, Zhu Z, Yao Q, Chen C, Chen F, et al. 2023. ccTCM: a quantitative component and compound platform for promoting the research of traditional Chinese medicine. Computational and Structural Biotechnology Journal 21:5807−5817

doi: 10.1016/j.csbj.2023.11.030

[57]

Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, et al. 2009. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Research 37:W623−W633

doi: 10.1093/nar/gkp456

[58]

Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, et al. 2012. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Research 40:D1100−D1107

doi: 10.1093/nar/gkr777

[59]

Sun X, Wei Z, Yu X, Liu K, Yao S, et al. 2026. DRESIS 2.0: the comprehensive landscape of drug resistance information. Nucleic Acids Research 54:D1387−D1396

doi: 10.1093/nar/gkaf1219

[60]

Li F, Mou M, Li X, Xu W, Yin J, et al. 2025. DrugMAP 2.0: molecular atlas and pharma-information of all drugs. Nucleic Acids Research 53:D1372−D1382

doi: 10.1093/nar/gkae791

[61]

Rebhan M, Chalifa-Caspi V, Prilusky J, Lancet D. 1997. GeneCards: integrating information about genes, proteins and diseases. Trends in Genetics 13:163

doi: 10.1016/S0168-9525(97)01103-7

[62]

Kong X, Liu C, Zhang Z, Cheng M, Mei Z, et al. 2024. BATMAN-TCM 2.0: an enhanced integrative database for known and predicted interactions between traditional Chinese medicine ingredients and target proteins. Nucleic Acids Research 52:D1110−D1120

doi: 10.1093/nar/gkad926

[63]

Lin H, Hou D, Jiang X, Yin R, Yu S, et al. 2026. NPASS database update 2026: comprehensive quantitative composition, bioactivity, and ADME-Tox data of natural products for biomedical research. Nucleic Acids Research 54:D1519−d27

doi: 10.1093/nar/gkaf1196

[64]

Lv Q, Chen G, He H, Yang Z, Zhao L, et al. 2023. TCMBank-the largest TCM database provides deep learning-based Chinese-Western medicine exclusion prediction. Signal Transduction and Targeted Therapy 8:127

doi: 10.1038/s41392-023-01339-1

[65]

Willett P. 2006. Similarity-based virtual screening using 2D fingerprints. Drug Discovery Today 11:1046−53

doi: 10.1016/j.drudis.2006.10.005

[66]

Rogers D, Hahn M. 2010. Extended-connectivity fingerprints. Journal of Chemical Information and Modeling 50:742−754

doi: 10.1021/ci100050t

[67]

Duvenaud DK, Maclaurin D, Aguilera-Iparraguirre J, Gómez-Bombarelli R, Hirzel T, et al. 2015. Convolutional networks on graphs for learning molecular fingerprints. NIPS'15: Proceedings of the 29th International Conference on Neural Information Processing Systems, December 7−12, 2015, Montreal, Canada. Vol. 2. Cambridge, United States: MIT Press. pp. 2224−2232 https://dl.acm.org/doi/proceedings/10.5555/2969442

[68]

Yang K, Swanson K, Jin W, Coley C, Eiden P, et al. 2019. Analyzing learned molecular representations for property prediction. Journal of Chemical Information and Modeling 59:3370−3388

doi: 10.1021/acs.jcim.9b00237

[69]

Del Prete E, Facchiano A, Liò P. 2020. Bioinformatics methodologies for coeliac disease and its comorbidities. Briefings in Bioinformatics 21:355−367

doi: 10.1093/bib/bby109

[70]

Lin Y, Zhang Y, Wang D, Yang B, Shen YQ. 2022. Computer especially AI-assisted drug virtual screening and design in traditional Chinese medicine. Phytomedicine 107:154481

doi: 10.1016/j.phymed.2022.154481

[71]

Ma J, Sheridan RP, Liaw A, Dahl GE, Svetnik V. 2015. Deep neural nets as a method for quantitative structure-activity relationships. Journal of Chemical Information and Modeling 55:263−74

doi: 10.1021/ci500747n

[72]

Arora B, Coudrat T, Wootten D, Christopoulos A, Noronha SB, Sexton PM. 2016. Prediction of loops in G protein-coupled receptor homology models: effect of imprecise surroundings and constraints. Journal of Chemical Information and Modeling 56:671−686

doi: 10.1021/acs.jcim.5b00554

[73]

Wu Z, Ramsundar B, Feinberg EN, Gomes J, Geniesse C, et al. 2018. MoleculeNet: a benchmark for molecular machine learning. Chemical Science 9:513−530

doi: 10.1039/C7SC02664A

[74]

Zhang S, Huo D, Horne RI, Qi Y, Pujalte Ojeda S, et al. 2025. Sequence-based virtual screening using transformers. Nature Communications 16:6925

doi: 10.1038/s41467-025-61833-8

[75]

López-Pérez K, López-López E, Medina-Franco JL, Miranda-Quintana RA. 2023. Sampling and mapping chemical space with extended similarity indices. Molecules 28(17):28

doi: 10.3390/molecules28176333

[76]

Wu H, Liu J, Zhang R, Lu Y, Cui G, et al. 2024. A review of deep learning methods for ligand based drug virtual screening. Fundamental Research 4:715−737

doi: 10.1016/j.fmre.2024.02.011

[77]

Koirala M, Yan L, Mohamed Z, DiPaola M. 2025. AI-integrated QSAR modeling for enhanced drug discovery: from classical approaches to deep learning and structural insight. International Journal of Molecular Sciences 26:9384

doi: 10.3390/ijms26199384

[78]

Kattuparambil AA, Chaurasia DK, Shekhar S, Srinivasan A, Mondal S, et al. 2025. Exploring chemical space for "druglike" small molecules in the age of AI. Frontiers in Molecular Biosciences 12:1553667

doi: 10.3389/fmolb.2025.1553667

[79]

Xie S, Zhu H, Huang N. 2025. AI-designed molecules in drug discovery, structural novelty evaluation, and implications. Journal of Chemical Information and Modeling 65:8924−8933

doi: 10.1021/acs.jcim.5c00921

[80]

Pathan I, Raza A, Sahu A, Joshi M, Sahu Y, et al. 2025. Revolutionizing pharmacology: AI-powered approaches in molecular modeling and ADMET prediction. Medicine in Drug Discovery 28:100223

doi: 10.1016/j.medidd.2025.100223

[81]

Niazi SK. 2025. Artificial Intelligence in Small-molecule drug discovery: a critical review of methods, applications, and real-world outcomes. Pharmaceuticals 18:1271

doi: 10.3390/ph18091271

[82]

Sim J, Kim D, Kim B, Choi J, Lee J. 2025. Recent advances in AI-driven protein-ligand interaction predictions. Current Opinion in Structural Biology 92:103020

doi: 10.1016/j.sbi.2025.103020

[83]

Zhou G, Rusnac DV, Park H, Canzani D, Nguyen HM, et al. 2024. An artificial intelligence accelerated virtual screening platform for drug discovery. Nature Communications 15:7761

doi: 10.1038/s41467-024-52061-7

[84]

Rashid MA, Radhika NM, Yogeeta OA, Sameer NG, Kartik TN, et al. 2025. Recent advances in molecular docking techniques: transforming perspectives in distinct drug targeting and drug discovery approaches. Medicinal Chemistry 00:in press

doi: 10.2174/0115734064384472250716144736

[85]

Zhang Z, Quan L, Wang J, Peng L, Chen Q, et al. 2025. LABind: identifying protein binding ligand-aware sites via learning interactions between ligand and protein. Nature Communications 16:7712

doi: 10.1038/s41467-025-62899-0

[86]

Yang Z, Ji J, He S, Li J, He T, et al. 2024. Dockformer: A transformer-based molecular docking paradigm for large-scale virtual screening. arXiv 00:2411.06740

doi: 10.48550/arXiv.2411.06740

[87]

Sharma G, Kumar N, Sharma CS, Alqahtani T, Tiruneh YK, et al. 2025. Identification of promising SARS-CoV-2 main protease inhibitor through molecular docking, dynamics simulation, and ADMET analysis. Scientific Reports 15:2830

doi: 10.1038/s41598-025-86016-9

[88]

Hasan GM, Mohammad T, Zaidi S, Shamsi A, Hassan MI. 2025. Molecular docking and dynamics in protein serine/threonine kinase drug discovery: advances, challenges, and future perspectives. Frontiers in Pharmacology 16:1696204

doi: 10.3389/fphar.2025.1696204

[89]

Wang Y, Li Y, Chen J, Lai L. 2025. Modeling protein-ligand interactions for drug discovery in the era of deep learning. Chemical Society Reviews 54:11141−11183

doi: 10.1039/D5CS00415B

[90]

Wang S, Zhang L, Zhang W, Zeng X, Mei J, et al. 2025. Structure-based molecular screening and dynamic simulation of phytocompounds targeting VEGFR-2: a novel therapeutic approach for papillary thyroid carcinoma. Frontiers in Pharmacology 16:1583329

doi: 10.3389/fphar.2025.1583329

[91]

Li D, Hu J, Zhang L, Li L, Yin Q, et al. 2022. Deep learning and machine intelligence: new computational modeling techniques for discovery of the combination rules and pharmacodynamic characteristics of Traditional Chinese Medicine. European Journal of Pharmacology 933:175260

doi: 10.1016/j.ejphar.2022.175260

[92]

Abramson J, Adler J, Dunger J, Evans R, Green T, et al. 2024. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630:493−500

doi: 10.1038/s41586-024-07487-w

[93]

Takaba K, Friedman AJ, Cavender CE, Behara PK, Pulido I, et al. 2024. Machine-learned molecular mechanics force fields from large-scale quantum chemical data. Chemical Science 15:12861−12878

doi: 10.1039/D4SC00690A

[94]

Hopkins AL. 2008. Network pharmacology: the next paradigm in drug discovery. Nature Chemical Biology 4:682−690

doi: 10.1038/nchembio.118

[95]

Cui G, Li M, Guo W, Gao M, Zhu Q, et al. 2025. AI driven network pharmacology: multi-scale mechanisms of traditional Chinese medicine from molecular to patient analysis. Computational and Structural Biotechnology Journal 27:5087−5104

doi: 10.1016/j.csbj.2025.11.016

[96]

Jiang W, Ye W, Tan X, Bao YJ. 2025. Network-based multi-omics integrative analysis methods in drug discovery: a systematic review. BioData Mining 18:27

doi: 10.1186/s13040-025-00442-z

[97]

Picard M, Scott-Boyer MP, Bodein A, Périn O, Droit A. 2021. Integration strategies of multi-omics data for machine learning analysis. Computational and Structural Biotechnology Journal 19:3735−3746

doi: 10.1016/j.csbj.2021.06.030

[98]

Wei S, Sasi C, Piepenbrock J, Huynen MA, t Hoen PAC. 2025. The use of knowledge graphs for drug repurposing: from classical machine learning algorithms to graph neural networks. Computers in Biology and Medicine 196:110873

doi: 10.1016/j.compbiomed.2025.110873

[99]

Ying H, Kong W, Xu X. 2025. Integrated network pharmacology, machine learning and experimental validation to identify the key targets and compounds of TiaoShenGongJian for the treatment of breast cancer. OncoTargets and Therapy 18:49−71

doi: 10.2147/OTT.S486300

[100]

Ding C, Liao Q, Zuo R, Zhang S, Guo Z, et al. 2024. Machine learning potential predictor of idiopathic pulmonary fibrosis. Frontiers in Genetics 15:1464471

doi: 10.3389/fgene.2024.1464471

[101]

Zhao H, He L, Yin D, Song B. 2019. Identification of β-catenin target genes in colorectal cancer by interrogating gene fitness screening data. Oncology Letters 18:3769−3777

doi: 10.3892/ol.2019.10724

[102]

LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436−444

doi: 10.1038/nature14539

[103]

Chen H, King FJ, Zhou B, Wang Y, Canedy CJ, et al. 2024. Drug target prediction through deep learning functional representation of gene signatures. Nature Communications 15:1853

doi: 10.1038/s41467-024-46089-y

[104]

Qu Z, Wang W, Hou C, Hou C. 2019. Radar signal intra-pulse modulation recognition based on convolutional denoising autoencoder and deep convolutional neural network. IEEE Access 7:112339−11234

doi: 10.1109/ACCESS.2019.2935247

[105]

Zhang S, Zhang X, Du J, Wang W, Pi X. 2024. Multi-target meridians classification based on the topological structure of anti-cancer phytochemicals using deep learning. Journal of Ethnopharmacology 319:117244

doi: 10.1016/j.jep.2023.117244

[106]

Zhang Z, Chen L, Zhong F, Wang D, Jiang J, et al. 2022. Graph neural network approaches for drug-target interactions. Current Opinion in Structural Biology 73:102327

doi: 10.1016/j.sbi.2021.102327

[107]

Guo X, Zhao X, Lu X, Zhao L, Zeng Q, et al. 2024. A deep learning-driven discovery of berberine derivatives as novel antibacterial against multidrug-resistant Helicobacter pylori. Signal Transduction and Targeted Therapy 9:183

doi: 10.1038/s41392-024-01895-0

[108]

Duan YJ, Fu L, Zhang XC, Long TZ, He YH, et al. 2023. Improved GNNs for log D(7.4) prediction by transferring knowledge from low-fidelity data. Journal of Chemical Information and Modeling 63:2345−2359

doi: 10.1021/acs.jcim.2c01564

[109]

Kimber TB, Chen Y, Volkamer A. 2021. Deep learning in virtual screening: recent applications and developments. International Journal of Molecular Sciences 22:4435

doi: 10.3390/ijms22094435

[110]

Zakharov AV, Peach ML, Sitzmann M, Nicklaus MC. 2014. QSAR modeling of imbalanced high-throughput screening data in PubChem. Journal of Chemical Information and Modeling 54:705−12

doi: 10.1021/ci400737s

[111]

Huang S, Zhang L, Liu X. 2025. Bioinformatics approach to identifying molecular targets of isoliquiritigenin affecting chronic obstructive pulmonary disease: a machine learning pharmacology study. International Journal of Molecular Sciences 26(8):3907

doi: 10.3390/ijms26083907

[112]

Lu X, Xie L, Xu L, Mao R, Xu X, et al. 2024. Multimodal fused deep learning for drug property prediction: integrating chemical language and molecular graph. Computational and Structural Biotechnology Journal 23:1666−1679

doi: 10.1016/j.csbj.2024.04.030

[113]

MacLean F. 2021. Knowledge graphs and their applications in drug discovery. Expert Opinion on Drug Discovery 16:1057−1069

doi: 10.1080/17460441.2021.1910673

[114]

Yu Z, Li T, Zheng Z, Yang X, Guo X, et al. 2025. Tailoring a traditional Chinese medicine prescription for complex diseases: a novel multi-targets-directed gradient weighting strategy. Journal of Pharmaceutical Analysis 15:101199

doi: 10.1016/j.jpha.2025.101199

[115]

Zhang J, Li H, Zhang Y, Huang J, Ren L, et al. 2025. Computational toxicology in drug discovery: applications of artificial intelligence in ADMET and toxicity prediction. Briefings in Bioinformatics 26:bbaf533

doi: 10.1093/bib/bbaf533

[116]

Wu C, Wu C, Peng L, Wu M, Li Z, et al. 2024. Multi-omics approaches for the understanding of therapeutic mechanism for Huang-Qi-Long-Dan Granule against ischemic stroke. Pharmacological Research 205:107229

doi: 10.1016/j.phrs.2024.107229

[117]

Khanal S, Kumar A, Kumar P, Thakur P, Chander AM, et al. 2025. Unraveling bioactive potential and production in Ganoderma lucidum through omics and machine learning modeling. Chinese Herbal Medicines 17:414−427

doi: 10.1016/j.chmed.2025.05.003

[118]

Mohammadi-Shemirani P, Sood T, Paré G. 2023. From 'omics to multi-omics technologies: the discovery of novel causal mediators. Current Atherosclerosis Reports 25:55−65

doi: 10.1007/s11883-022-01078-8

[119]

Chen LG, Tubbs JD, Liu Z, Thach TQ, Sham PC. 2024. Mendelian randomization: causal inference leveraging genetic data. Psychological Medicine 54:1461−74

doi: 10.1017/S0033291724000321

[120]

Li Y, Miao Y, Feng Q, Zhu W, Chen Y, et al. 2024. Mitochondrial dysfunction and onset of type 2 diabetes along with its complications: a multi-omics Mendelian randomization and colocalization study. Frontiers in Endocrinology 15:1401531

doi: 10.3389/fendo.2024.1401531

[121]

Huang L, Liu J, Zheng X, Zhang K, Chen Y, et al. 2026. Integrated multi-omics mapping of the causal landscape of gout across the circulating-tissue axis. Frontiers in Immunology 17:1776456

doi: 10.3389/fimmu.2026.1776456

[122]

Garcia-Argibay M, Wootton RE, Larsson H, Mattheisen M, Polimanti R, et al. 2026. Causal inference in psychiatric research: how to critically evaluate and interpret mendelian randomization studies. Molecular Psychiatry 31:3533−3543

doi: 10.1038/s41380-026-03484-9

[123]

Yazdani A, Yazdani A, Mendez-Giraldez R, Samiei A, Kosorok MR, et al. 2022. From classical mendelian randomization to causal networks for systematic integration of multi-omics. Frontiers in Genetics 13:990486

doi: 10.3389/fgene.2022.990486

[124]

Yang J, Yang H, Wang F, Dai Y, Deng Y, et al. 2025. Bioinformatics identification based on causal association inference using multi-omics reveals the underlying mechanism of Gui-Zhi-Shao-Yao-Zhi-Mu decoction in modulating rheumatoid arthritis. Phytomedicine 136:156332

doi: 10.1016/j.phymed.2024.156332

[125]

Schölkopf B, Locatello F, Bauer S, Ke NR, Kalchbrenner N, et al. 2021. Toward causal representation learning. Proceedings of the IEEE 109:612−634

doi: 10.1109/JPROC.2021.3058954

[126]

Dixit A, Parnas O, Li B, Chen J, Fulco CP, et al. 2016. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167:1853−1866.e17

doi: 10.1016/j.cell.2016.11.038

[127]

Subramanian I, Verma S, Kumar S, Jere A, Anamika K. 2020. Multi-omics data integration, interpretation, and its application. Bioinformatics and Biology Insights 14:1177932219899051

doi: 10.1177/1177932219899051

[128]

Hasin Y, Seldin M, Lusis A. 2017. Multi-omics approaches to disease. Genome Biology 18:83

doi: 10.1186/s13059-017-1215-1

[129]

Shataer D, Cao S, Liu X, Aierken K, Bhattacharya P, et al. 2025. Application of large language models in traditional Chinese medicine: a state-of-the-art review. American Journal of Chinese Medicine 53:973−997

doi: 10.1142/S0192415X25500375

[130]

Guo P, Jiang M, Hu S, Jiang Q, Li L, et al. 2026. Advancing the modernization of traditional Chinese medicine through artificial intelligence and multimodal data integration. Chinese Medicine 21:54

doi: 10.1186/s13020-025-01194-y

[131]

Hogan A, Blomqvist E, Cochez M, D'amato C, De Melo G, et al. 2021. Knowledge graphs. arXiv 00:2003.02320

doi: 10.48550/arXiv.2003.02320

[132]

Han C, Yang G, Li H, Zhu L, Feng M. 2026. Tuning and clinical application of large language models in Traditional Chinese Medicine: scoping review. Chinese Medicine 21:71

doi: 10.1186/s13020-026-01346-8

[133]

Wang L, Tang K, Wang Y, Zhang P, Li S. 2025. Advancements in Artificial intelligence-driven diagnostic models for traditional Chinese medicine. American Journal of Chinese Medicine 53:647−673

doi: 10.1142/S0192415X25500259

[134]

Dai Y, Shao X, Zhang J, Chen Y, Chen Q, et al. 2024. TCMChat: A generative large language model for traditional Chinese medicine. Pharmacological Research 210:107530

doi: 10.1016/j.phrs.2024.107530

[135]

Liu Z, Yang T, Wang J, Chen Y, Gao Z, et al. 2026. Tianyi: a traditional Chinese medicine all-rounder language model and its real-world clinical practice. Information Fusion 126:103663

doi: 10.1016/j.inffus.2025.103663

[136]

Chen Y, Wang Z, Xing X, Zheng H, Xu Z, et al. 2023. BianQue: balancing the questioning and suggestion ability of health LLMs with multi-turn health conversations polished by ChatGPT. arXiv 00:2310.15896

doi: 10.48550/arXiv.2310.15896

[137]

Zhou W, Gao Y. 2026. Phenotype-target coupled drug screening: a high-efficiency framework for innovative drug discovery from CHMs. Engineering 57:10−13

doi: 10.1016/j.eng.2025.11.019

[138]

Li Y, Liu X, Zhou J, Li F, Wang Y, et al. 2025. Artificial intelligence in traditional Chinese medicine: advances in multi-metabolite multi-target interaction modeling. Frontiers in Pharmacology 16:1541509

doi: 10.3389/fphar.2025.1541509

[139]

Bittner MI, Farajnia S. 2022. AI in drug discovery: applications, opportunities, and challenges. Patterns 3:100529

doi: 10.1016/j.patter.2022.100529

[140]

Bastos M, Abian O, Johnson CM, Ferreira-da-Silva F, Vega S, et al. 2023. Isothermal titration calorimetry. Nature Reviews Methods Primers 3:17

doi: 10.1038/s43586-023-00199-x

[141]

Li L, Zhang J, Li Y, Huang C, Xu J, et al. 2024. Dielectric surface-based biosensors for enhanced detection of biomolecular interactions: advances and applications. Biosensors 14:524

doi: 10.3390/bios14110524

[142]

Hong M, Du Y, Chen D, Shi Y, Hu M, et al. 2023. Martynoside rescues 5-fluorouracil-impaired ribosome biogenesis by stabilizing RPL27A. Science Bulletin 68:1662−1677

doi: 10.1016/j.scib.2023.07.018

[143]

Baell JB, Holloway GA. 2010. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. Journal of Medicinal Chemistry 53:2719−2740

doi: 10.1021/jm901137j

[144]

McGovern SL, Caselli E, Grigorieff N, Shoichet BK. 2002. A common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening. Journal of Medicinal Chemistry 45:1712−1722

doi: 10.1021/jm010533y

[145]

Leelananda SP, Lindert S. 2016. Computational methods in drug discovery. Beilstein Journal of Organic Chemistry 12:2694−2718

doi: 10.3762/bjoc.12.267

[146]

Chen X, Wang Y, Ma N, Tian J, Shao Y, et al. 2020. Target identification of natural medicine with chemical proteomics approach: probe synthesis, target fishing and protein identification. Signal Transduction and Targeted Therapy 5:72

doi: 10.1038/s41392-020-0186-y

[147]

Wang S, Zhang Y, Yu R, Chai Y, Liu R, et al. 2024. Labeled and label-free target identifications of natural products. Journal of Medicinal Chemistry 67:17980−17996

doi: 10.1021/acs.jmedchem.4c01576

[148]

Wang Q, Du T, Zhang Z, Zhang Q, Zhang J, et al. 2024. Target fishing and mechanistic insights of the natural anticancer drug candidate chlorogenic acid. Acta Pharmaceutica Sinica B 14:4431−4442

doi: 10.1016/j.apsb.2024.07.005

[149]

Parker CG, Pratt MR. 2020. Click chemistry in proteomic investigations. Cell 180:605−632

doi: 10.1016/j.cell.2020.01.025

[150]

Schenone M, Dančík V, Wagner BK, Clemons PA. 2013. Target identification and mechanism of action in chemical biology and drug discovery. Nature Chemical Biology 9:232−240

doi: 10.1038/nchembio.1199

[151]

Martinez Molina D, Jafari R, Ignatushchenko M, Seki T, Larsson EA, et al. 2013. Monitoring drug target engagement in cells and tissues using the cellular thermal shift assay. Science 341:84−87

doi: 10.1126/science.1233606

[152]

Savitski MM, Reinhard FB, Franken H, Werner T, Savitski MF, et al. 2014. Tracking cancer drugs in living cells by thermal profiling of the proteome. Science 346:1255784

doi: 10.1126/science.1255784

[153]

Lomenick B, Hao R, Jonai N, Chin RM, Aghajan M, et al. 2009. Target identification using drug affinity responsive target stability (DARTS). Proceedings of the National Academy of Sciences of the United States of America 106:21984−21989

doi: 10.1038/scibx.2010.71

[154]

Yu R, Zhang ZQ, Wang B, Jiang HX, Cheng L, et al. 2014. Berberine-induced apoptotic and autophagic death of HepG2 cells requires AMPK activation. Cancer Cell International 14:49

doi: 10.1186/1475-2867-14-49

[155]

Lin A, Giuliano CJ, Palladino A, John KM, Abramowicz C, et al. 2019. Off-target toxicity is a common mechanism of action of cancer drugs undergoing clinical trials. Science Translational Medicine 11:eaaw8412

doi: 10.1126/scitranslmed.aaw8412

[156]

Shalem O, Sanjana NE, Hartenian E, Shi X, Scott DA, et al. 2014. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343:84−87

doi: 10.1126/science.1247005

[157]

Tian S, Qin Y, Wu Y, Dong M. 2025. Design, performance, processing, and validation of a pooled CRISPR perturbation screen for bacterial toxins. Nature Protocols 20:1158−1195

doi: 10.1038/s41596-024-01075-y

[158]

Jansson-Löfmark R, Fridén M, Badolo L, Ahlström C, Gurrell I, et al. 2025. Translational PK/PD: a retrospective analysis of performance and impact from a drug portfolio. Drug Discovery Today 30:104417

doi: 10.1016/j.drudis.2025.104417

[159]

Haid RTU, Reichel A. 2025. PK/PD modeling of targeted protein degraders: charting new waters and navigating the shallows. Drug Discovery Today 30:104311

doi: 10.1016/j.drudis.2025.104311

[160]

Sun Q, Tu K, Xu Q, Yan L, Yang S, et al. 2025. Berberine suppresses colorectal cancer progression by inducing ferroptosis-mediated energy metabolism disorders. Journal of Advanced Research 00:In Press

doi: 10.1016/j.jare.2025.10.025

[161]

Sweis RF. 2015. Target (In)Validation: a critical, sometimes unheralded, role of modern medicinal chemistry. ACS Medicinal Chemistry Letters 6:618−621

doi: 10.1021/acsmedchemlett.5b00183

[162]

Qi H, Chen S, Chen X, Ma Q, Shi J, et al. 2025. Multi-dimensional PK-PD insights into Lianhua Qingwen's formula compatibility. Journal of Ethnopharmacology 353:120251

doi: 10.1016/j.jep.2025.120251

[163]

Topol EJ. 2019. High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine 25:44−56

doi: 10.1038/s41591-018-0300-7

[164]

Waman VP, Sen N, Varadi M, Daina A, Wodak SJ, et al. 2021. The impact of structural bioinformatics tools and resources on SARS-CoV-2 research and therapeutic strategies. Briefings in Bioinformatics 22:742−68

doi: 10.1093/bib/bbaa362

[165]

Patchipala S. 2023. Tackling data and model drift in AI: strategies for maintaining accuracy during ML model inference. International Journal of Science and Research Archive 10:1198−1209

doi: 10.30574/ijsra.2023.10.2.0855

[166]

Huang K, Chandak P, Wang Q, Havaldar S, Vaid A, et al. 2024. A foundation model for clinician-centered drug repurposing. Nature Medicine 30:3601−3613

doi: 10.1038/s41591-024-03233-x

[167]

Guo XX, An S, Bao F, Xu TR. 2023. Challenges and perspectives in target identification and mechanism illustration for Chinese medicine. Chinese Journal of Integrative Medicine 29:644−654

doi: 10.1007/s11655-023-3629-9

[168]

Jumper J, Evans R, Pritzel A, Green T, Figurnov M, et al. 2021. Highly accurate protein structure prediction with AlphaFold. Nature 596:583−589

doi: 10.1038/s41586-021-03819-2

[169]

Zhang P, Zhang D, Zhou W, Wang L, Wang B, et al. 2023. Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine. Briefings in Bioinformatics 25:bbad518

doi: 10.1093/bib/bbad518

[170]

Kauffmann J, Dippel J, Ruff L, Samek W, Müller KR, et al. 2025. Explainable AI reveals Clever Hans effects in unsupervised learning models. Nature Machine Intelligence 7:412−422

doi: 10.1038/s42256-025-01000-2

[171]

Tong X, Wang D, Ding X, Tan X, Ren Q, et al. 2022. Blood-brain barrier penetration prediction enhanced by uncertainty estimation. Journal of Cheminformatics 14:44

doi: 10.1186/s13321-022-00619-2

[172]

Vatansever S, Schlessinger A, Wacker D, Kaniskan H, Jin J, et al. 2021. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: state-of-the-arts and future directions. Medicinal Research Reviews 41:1427−1473

doi: 10.1002/med.21764

[173]

Xu H, Zhao H, Ding C, Jiang D, Zhao Z, et al. 2023. Celastrol suppresses colorectal cancer via covalent targeting peroxiredoxin 1. Signal Transduction and Targeted Therapy 8:51

doi: 10.1038/s41392-022-01231-4

[174]

Begley CG, Ellis LM. 2012. Drug development: raise standards for preclinical cancer research. Nature 483:531−533

doi: 10.1038/483531a

[175]

Clevers H. 2016. Modeling development and disease with organoids. Cell 165:1586−1597

doi: 10.1016/j.cell.2016.05.082

[176]

Vlachogiannis G, Hedayat S, Vatsiou A, Jamin Y, Fernández-Mateos J, et al. 2018. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science 359:920−926

doi: 10.1126/science.aao2774