Advancing network pharmacology with artificial intelligence: the next paradigm in traditional Chinese medicine

Abstract

Network pharmacology has gained widespread application in drug discovery, particularly in traditional Chinese medicine (TCM) research, which is characterized by its “multi-component, multi-target, and multi-pathway” nature. Through the integration of network biology, TCM network pharmacology enables systematic evaluation of therapeutic efficacy and detailed elucidation of action mechanisms, establishing a novel research paradigm for TCM modernization. The rapid advancement of machine learning, particularly revolutionary deep learning methods, has substantially enhanced artificial intelligence (AI) technology, offering significant potential to advance TCM network pharmacology research. This paper describes the methodology of TCM network pharmacology, encompassing ingredient identification, network construction, network analysis, and experimental validation. Furthermore, it summarizes key strategies for constructing various networks and analyzing constructed networks using AI methods. Finally, it addresses challenges and future directions regarding cell-cell communication (CCC)-based network construction, analysis, and validation, providing valuable insights for TCM network pharmacology.

Keywords

Traditional Chinese medicine / Network pharmacology / Artificial intelligence / Efficacy evaluation / Mechanism elucidation / Network construction / Network analysis

References

[1] Hopkins AL. Network pharmacology. Nat Biotechnol. 2007; 25(10):1110-1111. https://doi.org/10.1038/nbt1007-1110.

[2] Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008; 4(11):682-690. https://doi.org/10.1038/nchembio.118.

[3] 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.

[4] 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.

[5] Zhang P, Zhang D, Zhou W, et al. Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine. Brief Bioinform. 2023; 25(1):bbad518. https://doi.org/10.1093/bib/bbad518.

[6] Wu L, Wang Y, Li Z, et al. Identifying roles of “Jun-Chen-Zuo-Shi”component herbs of QiShenYiQi Formula in treating acute myocardial ischemia by network pharmacology. Chin Med. 2014;9:24. https://doi.org/10.1186/1749-8546-9-24.

[7] Liao J, Hao C, Huang W, et al. Network pharmacology study reveals energy metabolism and apoptosis pathways-mediated cardioprotective effects of Shenqi Fuzheng. J Ethnopharmacol. 2018; 227:155-165. https://doi.org/10.1016/j.jep.2018.08.029.

[8] Yu KH, Beam AL, Kohane IS.Artificial intelligence in healthcare. Nat Biomed Eng. 2018; 2(10):719-731. https://doi.org/10.1038/s41551-018-0305-z.

[9] Shao X, Yang H, Zhuang X, et al. scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network. Nucleic Acids Res. 2021; 49(21):e122. https://doi.org/10.1093/nar/gkab775.

[10] Fang Y, Zhang Q, Zhang N, et al. Knowledge graph-enhanced molecular contrastive learning with functional prompt. Nat Mach Intell. 2023; 5(5):542-553. https://doi.org/10.1038/s42256-023-00654-0.

[11] Guo D, Yang D, Zhang H, et al. Deepseek-r1: incentivizing reasoning capability in llms via reinforcement learning. arXiv. 2025;2501:12948. https://doi.org/10.48550/arXiv.2501.12948.

[12] Brown T, Mann B, Ryder N, et al.Language models are few-shot learners. Adv Neural Inf Process Syst. 2020; 33:1877-1901. https://doi.org/10.48550/arXiv.2005.14165.

[13] Touvron H, Lavril T, Izacard G, et al. Llama: open and efficient foundation language models. arXiv. 2023;2302:13971. https://doi.org/10.48550/arXiv.2302.13971.

[14] Dai Y, Shao X, Zhang J, et al. TCMChat: a generative large language model for traditional Chinese medicine. Pharmacol Res. 2024;210:107530. https://doi.org/10.1016/j.phrs.2024.107530.

[15] Chen Y, Wang Z, Xing X, et al. Bianque: balancing the questioning and suggestion ability of health llms with multi-turn health conversations polished by chatgpt. arXiv. 2023;2310:15896. https://doi.org/10.48550/arXiv.2310.15896.

[16] Zhang H, Chen J, Jiang F, et al. Huatuogpt, towards taming language model to be a doctor. arXiv. 2023;2305:15075. https://doi.org/10.48550/arXiv.2305.15075.

[17] Chen S, Yin X, Han J, et al. DNA barcoding in herbal medicine: retrospective and prospective. J Pharm Anal. 2023; 13(5):431-441. https://doi.org/10.1016/j.jpha.2023.03.008.

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

[19] Yan D, Zheng G, Wang C, et al. HIT 2.0: an enhanced platform for herbal ingredients’ targets. Nucleic Acids Res. 2022; 50(D1):D1238-D1243. https://doi.org/10.1093/nar/gkab1011.

[20] Wu Y, Zhang F, 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.

[21] Fang S, Dong L, Liu L, et al. HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine. Nucleic Acids Res. 2021; 49(D1):D1197-D1206. https://doi.org/10.1093/nar/gkaa1063.

[22] Tian S, Zhang J, Yuan S, et al. Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM. Brief Bioinform. 2023; 24(2):bbad027. https://doi.org/10.1093/bib/bbad027.

[23] Zhang Y, Li X, Shi Y, et al. ETCM v2.0: an update with comprehensive resource and rich annotations for traditional Chinese medicine. Acta Pharm Sin B. 2023; 13(6):2559-2571. https://doi.org/10.1016/j.apsb.2023.03.012.

[24] Lv Q, Chen G, He H, et al. TCMBank-the largest TCM database provides deep learning-based Chinese-Western medicine exclusion prediction. Signal Transduct Target Ther. 2023; 8(1):127. https://doi.org/10.1038/s41392-023-01339-1.

[25] Li X, Ren J, Zhang W, et al. LTM-TCM: a comprehensive database for the linking of traditional Chinese medicine with modern medicine at molecular and phenotypic levels. Pharmacol Res. 2022; 178:106185. https://doi.org/10.1016/j.phrs.2022.106185.

[26] Liu X, Liu J, Fu B, 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.

[27] Yang P, Lang J, Li H, et al. TCM-Suite: a comprehensive and holistic platform for traditional Chinese medicine component identification and network pharmacology analysis. iMeta. 2022; 1(4):e47. https://doi.org/10.1002/imt2.47.

[28] Pinzi L, Rastelli G. Molecular docking: shifting paradigms in drug discovery. Int J Mol Sci. 2019; 20(18):4331. https://doi.org/10.3390/ijms20184331.

[29] Allen WJ, Balius TE, Mukherjee S, et al. DOCK 6: impact of new features and current docking performance. J Comput Chem. 2015; 36(15):1132-1156. https://doi.org/10.1002/jcc.23905.

[30] McGann M. FRED and HYBRID docking performance on standardized datasets. J Comput Aided Mol Des. 2012; 26(8):897-906. https://doi.org/10.1007/s10822-012-9584-8.

[31] Spitzer R, Jain AN. Surflex-Dock: docking benchmarks and real-world application. J Comput Aided Mol Des. 2012; 26(6):687-699. https://doi.org/10.1007/s10822-011-9533-y.

[32] Verdonk ML, Cole JC, Hartshorn MJ, et al.Improved protein-ligand docking using GOLD. Proteins. 2003; 52(4):609-623. https://doi.org/10.1002/prot.10465.

[33] Morris GM, Huey R, Lindstrom W, et al. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem. 2009; 30(16):2785-2791. https://doi.org/10.1002/jcc.21256.

[34] Zsoldos Z, Reid D, Simon A, et al. eHiTS: an innovative approach to the docking and scoring function problems. Curr Protein Pept Sci. 2006; 7(5):421-435. https://doi.org/10.2174/138920306778559412.

[35] Friesner RA, Banks JL, Murphy RB, et al. Glide: a new approach for rapid, accurate docking and scoring.1. Method and assessment of docking accuracy. J Med Chem. 2004; 47(7):1739-1749. https://doi.org/10.1021/jm0306430.

[36] Pang YP, Perola E, Xu K, et al. EUDOC: a computer program for identification of drug interaction sites in macromolecules and drug leads from chemical databases. J Comput Chem. 2001; 22(15):1750-1771. https://doi.org/10.1002/jcc.1129.

[37] Rarey M, Kramer B, Lengauer T, et al. A fast flexible docking method using an incremental construction algorithm. J Mol Biol. 1996; 261(3):470-489. https://doi.org/10.1006/jmbi.1996.0477.

[38] Miller MD, Kearsley SK, Underwood DJ, et al. FLOG: a system to select ‘quasi-flexible’ ligands complementary to a receptor of known three-dimensional structure. J Comput Aided Mol Des. 1994; 8(2):153-174. https://doi.org/10.1007/BF00119865.

[39] Zavodszky MI, Rohatgi A, Van Voorst JR, et al. Scoring ligand similarity in structure-based virtual screening. J Mol Recognit. 2009; 22(4):280-292. https://doi.org/10.1002/jmr.942.

[40] Wu G, Robertson DH, Brooks CL, et al. Detailed analysis of grid-based molecular docking: a case study of CDOCKER-A CHARMm-based MD docking algorithm. J Comput Chem. 2003; 24(13):1549-1562. https://doi.org/10.1002/jcc.10306.

[41] Tietze S, Apostolakis J. GlamDock: development and validation of a new docking tool on several thousand protein-ligand complexes. J Chem Inf Model. 2007; 47(4):1657-1672. https://doi.org/10.1021/ci7001236.

[42] Korb O, Stutzle T, Exner TE. Empirical scoring functions for advanced protein-ligand docking with PLANTS. J Chem Inf Model. 2009; 49(1):84-96. https://doi.org/10.1021/ci800298z.

[43] Thomsen R, Christensen MH. MolDock: a new technique for high-accuracy molecular docking. J Med Chem. 2006; 49(11):3315-3321. https://doi.org/10.1021/jm051197e.

[44] Grosdidier A, Zoete V, Michielin O. EADock: docking of small molecules into protein active sites with a multiobjective evolutionary optimization. Proteins. 2007; 67(4):1010-1025. https://doi.org/10.1002/prot.21367.

[45] Zhang X, Zhang O, Shen C, et al. Efficient and accurate large library ligand docking with KarmaDock. Nat Comput Sci. 2023; 3(9):789-804. https://doi.org/10.1038/s43588-023-00511-5.

[46] Cai H, Shen C, Jian T, et al. CarsiDock: a deep learning paradigm for accurate protein-ligand docking and screening based on large-scale pre-training. Chem Sci. 2024; 15(4):1449-1471. https://doi.org/10.1039/d3sc05552c.

[47] Xue M, Liu B, Cao S, et al. FeatureDock for protein-ligand docking guided by physicochemical feature-based local environment learning using transformer. npj Drug Discov. 2025; 2(1):4. https://doi.org/10.1038/s44386-025-00005-6.

[48] Corso G, Stärk H, Jing B, et al. Diffdock: Diffdock: diffusion steps, twists, and turns for molecular docking. In: International Conference on Learning Representations. 2023;2210.01776. https://doi.org/10.48550/arXiv.2210.01776.

[49] Lu W, Zhang J, Huang W, et al. DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model. Nat Commun. 2024; 15(1):1071. https://doi.org/10.1038/s41467-024-45461-2.

[50] Cao D, Chen M, Zhang R, et al. SurfDock is a surface-informed diffusion generative model for reliable and accurate protein-ligand complex prediction. Nat Methods. 2025; 22(2):310-322. https://doi.org/10.1038/s41592-024-02516-y.

[51] Case DA, Cheatham TE, Darden T, et al.The Amber biomolecular simulation programs. J Comput Chem. 2005; 26(16):1668-1688. https://doi.org/10.1002/jcc.20290.

[52] Brooks BR, Brooks CL, Mackerell AD, et al.CHARMM: the biomolecular simulation program. J Comput Chem. 2009; 30(10):1545-1614. https://doi.org/10.1002/jcc.21287.

[53] Van Der Spoel D, Lindahl E, Hess B, et al. GROMACS: fast, flexible, and free. J Comput Chem. 2005; 26(16):1701-1718. https://doi.org/10.1002/jcc.20291.

[54] Love MI, Huber W, Anders S.Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014; 15(12):550. https://doi.org/10.1186/s13059-014-0550-8.

[55] Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010; 26(1):139-140. https://doi.org/10.1093/bioinformatics/btp616.

[56] Leng N, Dawson JA, Thomson JA, et al. EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics. 2013; 29(8):1035-1043. https://doi.org/10.1093/bioinformatics/btt087.

[57] Frazee AC, Pertea G, Jaffe AE, et al. Ballgown bridges the gap between transcriptome assembly and expression analysis. Nat Biotechnol. 2015; 33(3):243-246. https://doi.org/10.1038/nbt.3172.

[58] Tarazona S, García F, Ferrer A, et al. NOIseq: a RNA-seq differential expression method robust for sequencing depth biases. EMBnet J. 2011; 17(B):18-19. https://doi.org/10.14806/ej.17.B.265.

[59] Yu JL, Dai QQ, Li GB. Deep learning in target prediction and drug repositioning: recent advances and challenges. Drug Discov Today. 2022; 27(7):1796-1814. https://doi.org/10.1016/j.drudis.2021.10.010.

[60] Zhang H, Liu X, Cheng W, et al. Prediction of drug-target binding affinity based on deep learning models. Comput Biol Med. 2024;174:108435. https://doi.org/10.1016/j.compbiomed.2024.108435.

[61] Shi JY, Yiu SM. SRP: a concise non-parametric similarity-rank-based model for predicting drug-target interactions. In: International Conference on Bioinformatics and Biomedicine (BIBM). IEEE. 2015;1636-1641. https://doi.org/10.1109/BIBM.2015.7359921.

[62] Xia Z, Wu LY, Zhou X, et al. Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces. BMC Syst Biol. 2010; 4 Suppl 2(Suppl 2):S6. https://doi.org/10.1186/1752-0509-4-S2-S6.

[63] Mei JP, Kwoh CK, Yang P, et al. Drug-target interaction prediction by learning from local information and neighbors. Bioinformatics. 2013; 29(2):238-245. https://doi.org/10.1093/bioinformatics/bts670.

[64] Cheng F, Liu C, Jiang J, et al. Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput Biol. 2012; 8(5):e1002503. https://doi.org/10.1371/journal.pcbi.1002503.

[65] Chen X, Liu MX, Yan GY. Drug-target interaction prediction by random walk on the heterogeneous network. Mol Biosyst. 2012; 8(7):1970-1978. https://doi.org/10.1039/c2mb00002d.

[66] Fakhraei S, Huang B, Raschid L, et al. Network-based drug-target interaction prediction with probabilistic soft logic. IEEE/ACM Trans Comput Biol Bioinform. 2014; 11(5):775-787. https://doi.org/10.1109/TCBB.2014.2325031.

[67] Ba-Alawi W, Soufan O, Essack M, et al. DASPfind: new efficient method to predict drug-target interactions. J Cheminform. 2016;8:15. https://doi.org/10.1186/s13321-016-0128-4.

[68] Gonen M. Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization. Bioinformatics. 2012; 28(18):2304-2310. https://doi.org/10.1093/bioinformatics/bts360.

[69] Cobanoglu MC, Liu C, Hu F, et al. Predicting drug-target interactions using probabilistic matrix factorization. J Chem Inf Model. 2013; 53(12):3399-3409. https://doi.org/10.1021/ci400219z.

[70] Ling C, Zeng T, Dang Q, et al. Predicting drug-target interactions using matrix factorization with self-paced learning and dual similarity information. Technol Health Care. 2024; 32(S1):49-64. https://doi.org/10.3233/THC-248005.

[71] Ezzat A, Zhao P, Wu M, et al. Drug-target interaction prediction with graph regularized matrix factorization. IEEE/ACM Trans Comput Biol Bioinform. 2017; 14(3):646-656. https://doi.org/10.1109/TCBB.2016.2530062.

[72] Liu Y, Wu M, Miao C, et al. Neighborhood regularized logistic matrix factorization for drug-target interaction prediction. PLoS Comput Biol. 2016; 12(2):e1004760. https://doi.org/10.1371/journal.pcbi.1004760.

[73] Hao M, Bryant SH, Wang Y. Predicting drug-target interactions by dual-network integrated logistic matrix factorization. Sci Rep. 2017;7:40376. https://doi.org/10.1038/srep40376.

[74] Xiao X, Min JL, Wang P, et al. iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking. PLoS One. 2013; 8(8):e72234. https://doi.org/10.1371/journal.pone.0072234.

[75] Ezzat A, Wu M, Li XL, et al. Drug-target interaction prediction using ensemble learning and dimensionality reduction. Methods. 2017; 129:81-88. https://doi.org/10.1016/j.ymeth.2017.05.016.

[76] Perlman L, Gottlieb A, Atias N, et al. Combining drug and gene similarity measures for drug-target elucidation. J Comput Biol. 2011; 18(2):133-145. https://doi.org/10.1089/cmb.2010.0213.

[77] Wang L, You ZH, Chen X, et al. RFDT: a rotation forest-based predictor for predicting drug-target interactions using drug structure and protein sequence information. Curr Protein Pept Sci. 2018; 19(5):445-454. https://doi.org/10.2174/1389203718666161114111656.

[78] Meng FR, You ZH, Chen X, et al. Prediction of drug-target interaction networks from the integration of protein sequences and drug chemical structures. Molecules. 2017; 22(7):1119. https://doi.org/10.3390/molecules22071119.

[79] Huang YA, You ZH, Chen X. A systematic prediction of drug-target interactions using molecular fingerprints and protein sequences. Curr Protein Pept Sci. 2018; 19(5):468-478. https://doi.org/10.2174/1389203718666161122103057.

[80] Zhang S, Yang K, Liu Z, et al. DrugAI: a multi-view deep learning model for predicting drug-target activating/inhibiting mechanisms. Brief Bioinform. 2023; 24(1):bbac526. https://doi.org/10.1093/bib/bbac526.

[81] Yuan W, Chen G, Chen CY. FusionDTA: attention-based feature polymerizer and knowledge distillation for drug-target binding affinity prediction. Brief Bioinform. 2022; 23(1):bbab506. https://doi.org/10.1093/bib/bbab506.

[82] Liu S, Wang Y, Deng Y, et al. Improved drug-target interaction prediction with intermolecular graph transformer. Brief Bioinform. 2022; 23(5):bbac162. https://doi.org/10.1093/bib/bbac162.

[83] Huang K, Xiao C, Glass LM, et al. MolTrans: molecular interaction transformer for drug-target interaction prediction. Bioinformatics. 2021; 37(6):830-836. https://doi.org/10.1093/bioinformatics/btaa880.

[84] Zhang P, Wei Z, Che C, et al. DeepMGT-DTI: transformer network incorporating multilayer graph information for drug-target interaction prediction. Comput Biol Med. 2022;142:105214. https://doi.org/10.1016/j.compbiomed.2022.105214.

[85] Zhang R, Wang Z, Wang X, et al. MHTAN-DTI: metapath-based hierarchical transformer and attention network for drug-target interaction prediction. Brief Bioinform. 2023; 24(2):bbad079. https://doi.org/10.1093/bib/bbad079.

[86] Gao M, Zhang D, Chen Y, et al. GraphormerDTI: a graph transformer-based approach for drug-target interaction prediction. Comput Biol Med. 2024;173:108339. https://doi.org/10.1016/j.compbiomed.2024.108339.

[87] Hu J, Yu W, Pang C, et al. DrugormerDTI: drug graphormer for drug-target interaction prediction. Comput Biol Med. 2023;161:106946. https://doi.org/10.1016/j.compbiomed.2023.106946.

[88] Monteiro NRC, Oliveira JL, Arrais JP. DTITR: end-to-end drug-target binding affinity prediction with transformers. Comput Biol Med. 2022;147:105772. https://doi.org/10.1016/j.compbiomed.2022.105772.

[89] Wu H, Liu J, Jiang T, et al. AttentionMGT-DTA: a multi-modal drug-target affinity prediction using graph transformer and attention mechanism. Neural Netw. 2024; 169:623-636. https://doi.org/10.1016/j.neunet.2023.11.018.

[90] Rao J, Xie J, Yuan Q, et al. A variational expectation-maximization framework for balanced multi-scale learning of protein and drug interactions. Nat Commun. 2024; 15(1):4476. https://doi.org/10.1038/s41467-024-48801-4.

[91] Ahmed KT, Ansari MI, Zhang W. DTI-LM: language model powered drug-target interaction prediction. Bioinformatics. 2024; 40(9):btae533. https://doi.org/10.1093/bioinformatics/btae533.

[92] Lu Z, Song G, Zhu H, et al. DTIAM: a unified framework for predicting drug-target interactions, binding affinities and drug mechanisms. Nat Commun. 2025; 16(1):2548. https://doi.org/10.1038/s41467-025-57828-0.

[93] He H, Chen G, Tang Z, et al. Dual modality feature fused neural network integrating binding site information for drug target affinity prediction. npj Digit Med. 2025; 8(1):67. https://doi.org/10.1038/s41746-025-01464-x.

[94] Liu S, Liu Y, Xu H, et al. SP-DTI: subpocket-informed transformer for drug-target interaction prediction. Bioinformatics. 2025; 41(3):btaf011. https://doi.org/10.1093/bioinformatics/btaf011.

[95] Amberger JS, Bocchini CA, Schiettecatte F, et al. OMIM. org: online mendelian inheritance in man (OMIM(R)), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 2015; 43(D1):D789-D798. https://doi.org/10.1093/nar/gku1205.

[96] Rappaport N, Twik M, Plaschkes I, et al. MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search. Nucleic Acids Res. 2017; 45(D1):D877-D887. https://doi.org/10.1093/nar/gkw1012.

[97] Pinero J, Bravo A, Queralt-Rosinach N, et al. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 2017; 45(D1):D833-D839. https://doi.org/10.1093/nar/gkw943.

[98] Zhou Y, Zhang Y, Zhao D, et al. TTD: therapeutic target database describing target druggability information. Nucleic Acids Res. 2024; 52(D1):D1465-D1477. https://doi.org/10.1093/nar/gkad751.

[99] Davis AP, Wiegers TC, Johnson RJ, et al. Comparative toxicogenomics database (CTD):update 2023. Nucleic Acids Res. 2023; 51(D1):D1257-D1262. https://doi.org/10.1093/nar/gkac833.

[100] Wu L, Li X, Yang J, et al. CHD@ZJU: a knowledgebase providing network-based research platform on coronary heart disease. Database. 2013;2013:bat047. https://doi.org/10.1093/database/bat047.

[101] Wishart DS, Bartok B, Oler E, et al. MarkerDB: an online database of molecular biomarkers. Nucleic Acids Res. 2021; 49(D1):D1259-D1267. https://doi.org/10.1093/nar/gkaa1067.

[102] Zheng X, Tian Z, Che X, et al. DMRdb: a disease-centric Mendelian randomization database for systematically assessing causal relationships of diseases with genes, proteins, CpG sites, metabolites and other diseases. Nucleic Acids Res. 2025; 53(D1):D1363-D1371. https://doi.org/10.1093/nar/gkae853.

[103] Deng YT, You J, He Y, et al. Atlas of the plasma proteome in health and disease in 53,026 adults. Cell. 2025; 188(1):253-271.E7. https://doi.org/10.1016/j.cell.2024.10.045.

[104] Del Toro N, Shrivastava A, Ragueneau E, et al. The IntAct database: efficient access to fine-grained molecular interaction data. Nucleic Acids Res. 2022; 50(D1):D648-D653. https://doi.org/10.1093/nar/gkab1006.

[105] Oughtred R, Stark C, Breitkreutz BJ, et al. The BioGRID interaction database:2019 update. Nucleic Acids Res. 2019; 47(D1):D529-D541. https://doi.org/10.1093/nar/gky1079.

[106] Szklarczyk D, Franceschini A, Wyder S, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43(Database issue):D447-D452. https://doi.org/10.1093/nar/gku1003.

[107] Kanehisa M, Goto S, Kawashima S, et al. The KEGG resource for deciphering the genome. Nucleic Acids Res. 2004; 32(D1):D277-D2780. https://doi.org/10.1093/nar/gkh063.

[108] Rothfels K, Milacic M, Matthews L, et al.Using the reactome database. Curr Protoc. 2023; 3(4):e722. https://doi.org/10.1002/cpz1.722.

[109] Patil A, Nakai K, Nakamura H. HitPredict: a database of quality assessed protein-protein interactions in nine species. Nucleic Acids Res. 2011; 39(D1):D744-D749. https://doi.org/10.1093/nar/gkq897.

[110] Warde-Farley D, Donaldson SL, Comes O, et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 2010;38(Web Server issue):W214-W220. https://doi.org/10.1093/nar/gkq537.

[111] Kim CY, Baek S, Cha J, et al. HumanNet v3:an improved database of human gene networks for disease research. Nucleic Acids Res. 2022; 50(D1):D632-D639. https://doi.org/10.1093/nar/gkab1048.

[112] Pan Y, Li R, Li W, et al. HPC-Atlas: computationally constructing a comprehensive atlas of human protein complexes. Genomics Proteomics Bioinformatics. 2023; 21(5):976-990. https://doi.org/10.1016/j.gpb.2023.05.001.

[113] Laman Trip DS, van Oostrum M, Memon D, et al. A tissue-specific atlas of protein-protein associations enables prioritization of candidate disease genes. Nat Biotechnol. 2025. https://doi.org/10.1038/s41587-025-02659-z.

[114] Han H, Cho JW, Lee S, et al. TRRUST v2:an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res. 2018; 46(D1):D380-D386. https://doi.org/10.1093/nar/gkx1013.

[115] Feng C, Song C, Song S, et al. KnockTF 2.0:a comprehensive gene expression profile database with knockdown/knockout of transcription (co-)factors in multiple species. Nucleic Acids Res. 2024; 52(D1):D183-D193. https://doi.org/10.1093/nar/gkad1016.

[116] Shen WK, Chen SY, Gan ZQ, et al. AnimalTFDB 4. 0:a comprehensive animal transcription factor database updated with variation and expression annotations. Nucleic Acids Res. 2023; 51(D1):D39-D45. https://doi.org/10.1093/nar/gkac907.

[117] Zhang Q, Liu W, Zhang HM, et al. hTFtarget: a comprehensive database for regulations of human transcription factors and their targets. Genom Proteom Bioinf. 2020; 18(2):120-128. https://doi.org/10.1016/j.gpb.2019.09.006.

[118] Zhou KR, Liu S, Sun WJ, et al. ChIPBase v2.0: decoding transcriptional regulatory networks of non-coding RNAs and protein-coding genes from ChIP-seq data. Nucleic Acids Res. 2017; 45(D1):D43-D50. https://doi.org/10.1093/nar/gkw965.

[119] Liska O, Bohar B, Hidas A, et al. TFLink: an integrated gateway to access transcription factor-target gene interactions for multiple species. Database (Oxford). 2022;2022:baac083. https://doi.org/10.1093/database/baac083.

[120] Tong Z, Cui Q, Wang J, et al. TransmiR v2.0:an updated transcription factor-microRNA regulation database. Nucleic Acids Res. 2019; 47(D1):D253-D258. https://doi.org/10.1093/nar/gky1023.

[121] Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559. https://doi.org/10.1186/1471-2105-9-559.

[122] Margolin AA, Nemenman I, Basso K, et al. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics. 2006; 7 Suppl 1(Suppl 1):S7. https://doi.org/10.1186/1471-2105-7-S1-S7.

[123] Lachmann A, Giorgi FM, Lopez G, et al. ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information. Bioinformatics. 2016; 32(14):2233-2235. https://doi.org/10.1093/bioinformatics/btw216.

[124] Faith JJ, Hayete B, Thaden JT, et al. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 2007; 5(1):e8. https://doi.org/10.1371/journal.pbio.0050008.

[125] Meyer PE, Kontos K, Lafitte F, et al. Information-theoretic inference of large transcriptional regulatory networks. EURASIP J Bioinform Syst Biol. 2007; 2007(1): 79879. https://doi.org/10.1155/2007/79879.

[126] Sales G, Romualdi C. parmigene--a parallel R package for mutual information estimation and gene network reconstruction. Bioinformatic. 2011; 27(13):1876-1877. https://doi.org/10.1093/bioinformatics/btr274.

[127] Mordelet F, Vert JP. SIRENE: supervised inference of regulatory networks. Bioinformatics. 2008; 24(16):i76-i82. https://doi.org/10.1093/bioinformatics/btn273.

[128] Huynh-Thu VA, Irrthum A, Wehenkel L, et al. Inferring regulatory networks from expression data using tree-based methods. PLoS One. 2010; 5(9):e12776. https://doi.org/10.1371/journal.pone.0012776.

[129] Shao X, Lu X, Liao J, et al. New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data. Protein Cell. 2020; 11(12):866-880. https://doi.org/10.1007/s13238-020-00727-5.

[130] Shao X, Liao J, Li C, et al. CellTalkDB: a manually curated database of ligand-receptor interactions in humans and mice. Brief Bioinform. 2021; 22(4):bbaa269. https://doi.org/10.1093/bib/bbaa269.

[131] Shao X, Li C, Yang H, et al. Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk. Nat Commun. 2022; 13(1):4429. https://doi.org/10.1038/s41467-022-32111-8.

[132] Shao X, Wang Z, Wang K, et al. A single-cell landscape of human liver transplantation reveals a pathogenic immune niche associated with early allograft dysfunction. Engineering. 2024; 36:193-208. https://doi.org/10.1016/j.eng.2023.12.004.

[133] Cang Z, Zhao Y, Almet AA, et al. Screening cell-cell communication in spatial transcriptomics via collective optimal transport. Nat Methods. 2023; 20(2):218-228. https://doi.org/10.1038/s41592-022-01728-4.

[134] Li H, Ma T, Hao M, et al. Decoding functional cell-cell communication events by multi-view graph learning on spatial transcriptomics. Brief Bioinform. 2023; 24(6):bbad359. https://doi.org/10.1093/bib/bbad359.

[135] Jin S, Guerrero-Juarez CF, Zhang L, et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun. 2021; 12(1):1088. https://doi.org/10.1038/s41467-021-21246-9.

[136] Efremova M, Vento-Tormo M, Teichmann SA, et al. CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat Protoc. 2020; 15(4):1484-1506. https://doi.org/10.1038/s41596-020-0292-x.

[137] Browaeys R, Saelens W, Saeys Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat Methods. 2020; 17(2):159-162. https://doi.org/10.1038/s41592-019-0667-5.

[138] Zheng R, Zhang Y, Tsuji T, et al. MEBOCOST maps metabolite-mediated intercellular communications using single-cell RNA-seq. Nucleic Acids Res. 2025; 53(12):gkaf569. https://doi.org/10.1093/nar/gkaf569.

[139] Shao X, Yu L, Li C, et al. Extracellular vesicle-derived miRNA-mediated cell-cell communication inference for single-cell transcriptomic data with miRTalk. Genome Biol. 2025; 26(1):95. https://doi.org/10.1186/s13059-025-03566-x.

[140] Shan N, Lu Y, Guo H, et al. CITEdb: a manually curated database of cell-cell interactions in human. Bioinformatics. 2022; 38(22):5144-5148. https://doi.org/10.1093/bioinformatics/btac654.

[141] Gao J, Mo S, Wang J, et al. MACC: a visual interactive knowledgebase of metabolite-associated cell communications. Nucleic Acids Res. 2024; 52(D1):D633-D639. https://doi.org/10.1093/nar/gkad914.

[142] Subramanian A, Narayan R, Corsello SM, et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell. 2017; 171(6):1437-1452.e17. https://doi.org/10.1016/j.cell.2017.10.049.

[143] Lamb J, Crawford ED, Peck D, et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006; 313(5795):1929-1935. https://doi.org/10.1126/science.1132939.

[144] Ding F, Tan A, Ju W, et al. The prediction of key cytoskeleton components involved in glomerular diseases based on a protein-protein interaction network. PLoS One. 2016; 11(5):e0156024. https://doi.org/10.1371/journal.pone.0156024.

[145] Wang L, Li Z, Shao Q, et al. Dissecting active ingredients of Chinese medicine by content-weighted ingredient-target network. Mol Biosyst. 2014; 10(7):1905-1911. https://doi.org/10.1039/c3mb70581a.

[146] Cheng F, Kovacs IA, Barabasi AL.Network-based prediction of drug combinations. Nat Commun. 2019; 10(1):1197. https://doi.org/10.1038/s41467-019-09186-x.

[147] Wang S, Lee D. Community cohesion looseness in gene networks reveals individualized drug targets and resistance. Brief Bioinform. 2024; 25(3):bbae175. https://doi.org/10.1093/bib/bbae175.

[148] Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019; 10(1):1523. https://doi.org/10.1038/s41467-019-09234-6.

[149] Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005; 102(43):15545-15550. https://doi.org/10.1073/pnas.0506580102.

[150] Alhamdoosh M, Ng M, Wilson NJ, et al. Combining multiple tools outperforms individual methods in gene set enrichment analyses. Bioinformatics. 2017; 33(3):414-424. https://doi.org/10.1093/bioinformatics/btw623.

[151] Zhu S, Qian T, Hoshida Y, et al. GIGSEA: genotype imputed gene set enrichment analysis using GWAS summary level data. Bioinformatics. 2019; 35(1):160-163. https://doi.org/10.1093/bioinformatics/bty529.

[152] Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7. https://doi.org/10.1186/1471-2105-14-7.

[153] Tomfohr J, Lu J, Kepler TB. Pathway level analysis of gene expression using singular value decomposition. BMC Bioinformatics. 2005;6:225. https://doi.org/10.1186/1471-2105-6-225.

[154] Peng C, Chen Q, Tan S, et al. Generalized reporter score-based enrichment analysis for omics data. Brief Bioinform. 2024; 25(3):bbae116. https://doi.org/10.1093/bib/bbae116.

[155] Liu H, Yuan M, Mitra R, et al. CTpathway: a crosstalk-based pathway enrichment analysis method for cancer research. Genome Med. 2022; 14(1):118. https://doi.org/10.1186/s13073-022-01119-6.

[156] Yeganeh PN, Mostafavi MT. Causal disturbance analysis: a novel graph centrality based method for pathway enrichment analysis. IEEE/ACM Trans Comput Biol Bioinform. 2020; 17(5):1613-1624. https://doi.org/10.1109/TCBB.2019.2907246.

[157] Alexeyenko A, Lee W, Pernemalm M, et al. Network enrichment analysis: extension of gene-set enrichment analysis to gene networks. BMC Bioinformatics. 2012;13:226. https://doi.org/10.1186/1471-2105-13-226.

[158] Glaab E, Baudot A, Krasnogor N, et al. EnrichNet: network-based gene set enrichment analysis. Bioinformatics. 2012; 28(18):i451-i457. https://doi.org/10.1093/bioinformatics/bts389.

[159] Pham L, Christadore L, Schaus S, et al. Network-based prediction for sources of transcriptional dysregulation using latent pathway identification analysis. Proc Natl Acad Sci U S A. 2011; 108(32):13347-13352. https://doi.org/10.1073/pnas.1100891108.

[160] Dong X, Hao Y, Wang X, et al. LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights. Sci Rep. 2016;6:18871. https://doi.org/10.1038/srep18871.

[161] Fang Z, Tian W, Ji H. A network-based gene-weighting approach for pathway analysis. Cell Res. 2012; 22(3):565-580. https://doi.org/10.1038/cr.2011.149.

[162] Pandey V. MiNEApy: enhancing enrichment network analysis in metabolic networks. Bioinformatics. 2025; 41(3): btaf077. https://doi.org/10.1093/bioinformatics/btaf077.

[163] Assenov Y, Ramirez F, Schelhorn SE, et al. Computing topological parameters of biological networks. Bioinformatics. 2008; 24(2):282-284. https://doi.org/10.1093/bioinformatics/btm554.

[164] Scardoni G, Petterlini M, Laudanna C. Analyzing biological network parameters with CentiScaPe. Bioinformatics. 2009; 25(21):2857-2859. https://doi.org/10.1093/bioinformatics/btp517.

[165] Chin CH, Chen SH, Wu HH, et al. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014; 8(Suppl 4):S11. https://doi.org/10.1186/1752-0509-8-S4-S11.

[166] Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 2003;4:2. https://doi.org/10.1186/1471-2105-4-2.

[167] Nepusz T, Yu H, Paccanaro A. Detecting overlapping protein complexes in protein-protein interaction networks. Nat Methods. 2012; 9(5):471-472. https://doi.org/10.1038/nmeth.1938.

[168] Li M, Chen JE, Wang JX, et al. Modifying the DPClus algorithm for identifying protein complexes based on new topological structures. BMC Bioinformatics. 2008;9:398. https://doi.org/10.1186/1471-2105-9-398.

[169] Perozzi B, Al-Rfou R, Skiena S. Deepwalk: online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 2014;701-710.. 2014;701-710. https://doi.org/10.1145/2623330.2623732.

[170] Jiang P, Singh M. SPICi: a fast clustering algorithm for large biological networks. Bioinformatics. 2010; 26(8):1105-1111. https://doi.org/10.1093/bioinformatics/btq078.

[171] Leung HC, Xiang Q, Yiu SM, et al. Predicting protein complexes from PPI data: a core-attachment approach. J Comput Biol. 2009; 16(2):133-144. https://doi.org/10.1089/cmb.2008.01TT.

[172] Meng X, Xiang J, Zheng R, et al. DPCMNE: detecting protein complexes from protein-protein interaction networks via multi-level network embedding. IEEE/ACM Trans Comput Biol Bioinform. 2022; 19(3):1592-1602. https://doi.org/10.1109/TCBB.2021.3050102.

[173] Zaki N, Efimov D, Berengueres J. Protein complex detection using interaction reliability assessment and weighted clustering coefficient. BMC Bioinformatics. 2013;14:163. https://doi.org/10.1186/1471-2105-14-163.

[174] Zhang J, Ubas AA, de Borja R, et al. Tahoe-100M: a giga-scale single-cell perturbation atlas for context-dependent gene function and cellular modeling. bioRxiv. 2025:2025.02.20.639398. https://doi.org/10.1101/2025.02.20.639398.

[175] Ramezani M, Weisbart E, Bauman J, et al. A genome-wide atlas of human cell morphology. Nat Methods. 2025; 22(3):621-633. https://doi.org/10.1038/s41592-024-02537-7.

[176] Zhan L, Wang Y, Wang A, et al. A genome-scale deep learning model to predict gene expression changes of genetic perturbations from multiplex biological networks. Brief Bioinform. 2024; 25(5):bbae433. https://doi.org/10.1093/bib/bbae433.

[177] Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003; 13(11):2498-2504. https://doi.org/10.1101/gr.1239303.

[178] Lv Q, Lin J, Wu X, et al. Novel active compounds and the anti-diabetic mechanism of mulberry leaves. Front Pharmacol. 2022;13:986931. https://doi.org/10.3389/fphar.2022.986931.

[179] Chen G, Yang R, Yang X, et al. Mulberry twig alkaloids for type 2 diabetes mellitus: a systematic review and meta-analysis. Front Pharmacol. 2025;16:1475080. https://doi.org/10.3389/fphar.2025.1475080.

[180] Gomez-Verjan JC, Zepeda-Arzate EA, Santiago-de-la-Cruz JA, et al. Unraveling the neuroprotective effect of natural bioactive compounds involved in the modulation of ischemic stroke by network pharmacology. Pharmaceuticals (Basel). 2023; 16(10):1376. https://doi.org/10.3390/ph16101376.

[181] Zhang Q, Wang A, Xu Q, et al. Efficacy and safety of ginkgo diterpene lactone meglumine in acute ischemic stroke: a randomized clinical trial. JAMA Netw Open. 2023; 6(8):e2328828. https://doi.org/10.1001/jamanetworkopen.2023.28828.

[182] Wang L, Liang X, Chen Y, et al. Ginkgo diterpene lactone meglumine for functional recovery in patients with acute ischemic stroke: a systematic review and Meta-analysis. J Ethnopharmacol. 2025;342:119416. https://doi.org/10.1016/j.jep.2025.119416.

[183] Gan X, Shu Z, Wang X, et al. Network medicine framework reveals generic herb-symptom effectiveness of traditional Chinese medicine. Sci Adv. 2023; 9(43):eadh0215. https://doi.org/10.1126/sciadv.adh0215.

[184] Zong W, Tian S, Niu Q, et al. Comparable clinical advantages identification of three formulae on rheumatic disease using a modular-based network proximity approach. J Ethnopharmacol. 2025; 337(Pt 1):118764. https://doi.org/10.1016/j.jep.2024.118764.

[185] Zhang Z, Yang W, Chen J, et al. Efficacy and mechanism of Schisandra chinensis active component Gomisin A on diabetic skin wound healing: network pharmacology and in vivo experimental validation. J Ethnopharmacol. 2025; 337(Pt 1):118828. https://doi.org/10.1016/j.jep.2024.118828.

[186] Morselli Gysi D, Barabasi AL. Noncoding RNAs improve the predictive power of network medicine. Proc Natl Acad Sci U S A. 2023; 120(45):e2301342120. https://doi.org/10.1073/pnas.2301342120.

[187] Xu Z, Zhang T, Chen H, et al. High-throughput single nucleus total RNA sequencing of formalin-fixed paraffin-embedded tissues by snRandom-seq. Nat Commun. 2023; 14(1):2734. https://doi.org/10.1038/s41467-023-38409-5.

[188] McKellar DW, Mantri M, Hinchman MM, et al. Spatial mapping of the total transcriptome by in situ polyadenylation. Nat Biotechnol. 2023; 41(4):513-520. https://doi.org/10.1038/s41587-022-01517-6.

[189] Sun F, Li H, Sun D, et al. Single-cell omics: experimental workflow, data analyses and applications. Sci China Life Sci. 2025; 68(1):5-102. https://doi.org/10.1007/s11427-023-2561-0.

[190] Liao J, Lu X, Shao X, et al. Uncovering an organ’s molecular architecture at single-cell resolution by spatially resolved transcriptomics. Trends Biotechnol. 2021; 39(1):43-58. https://doi.org/10.1016/j.tibtech.2020.05.006.

[191] Zhang J, Fang Y, Shao X, et al. The future of molecular studies through the lens of large language models. J Chem Inf Model. 2024; 64(3):563-566. https://doi.org/10.1021/acs.jcim.3c01977.

[192] Zhu B, Li Z, Jin Z, et al. Knowledge-based in silico fragmentation and annotation of mass spectra for natural products with MassKG. Comput Struct Biotechnol J. 2024; 23:3327-3341. https://doi.org/10.1016/j.csbj.2024.09.001.

[193] Armingol E, Baghdassarian HM, Lewis NE. The diversification of methods for studying cell-cell interactions and communication. Nat Rev Genet. 2024; 25(6):381-400. https://doi.org/10.1038/s41576-023-00685-8.

[194] Cui K, Gao X, Wang B, et al. Epsin nanotherapy regulates cholesterol transport to fortify atheroma regression. Circ Res. 2023; 132(1):e22-e42. https://doi.org/10.1161/CIRCRESAHA.122.321723.

[195] Li C, Shao X, Zhang S, et al. scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network. Cell Rep Med. 2024; 5(6):101568. https://doi.org/10.1016/j.xcrm.2024.101568.

[196] Qian J, Shao X, Bao H, et al. Identification and characterization of cell niches in tissue from spatial omics data at single-cell resolution. Nat Commun. 2025; 16(1):1693. https://doi.org/10.1038/s41467-025-57029-9.

[197] Verstegen MMA, Coppes RP, Beghin A, et al.Clinical applications of human organoids. Nat Med. 2025; 31(2):409-421. https://doi.org/10.1038/s41591-024-03489-3.

[198] Kumar N, Prakash PG, Wentland C, et al. Decoding spatiotemporal transcriptional dynamics and epithelial fibroblast crosstalk during gastroesophageal junction development through single cell analysis. Nat Commun. 2024; 15(1):3064. https://doi.org/10.1038/s41467-024-47173-z.

[199] Onesto MM, Kim JI, Pasca SP. Assembloid models of cell-cell interaction to study tissue and disease biology. Cell Stem Cell. 2024; 31(11):1563-1573. https://doi.org/10.1016/j.stem.2024.09.017.

[200] Li L, Mou X, Xie H, et al. In vitro tests to evaluate embryotoxicity and irritation of Chinese herbal medicine (Pentaherbs formulation) for atopic dermatitis. J Ethnopharmacol. 2023;305:116149. https://doi.org/10.1016/j.jep.2023.116149.

[201] Kang Y, Zhang H, Guan J. scINRB: single-cell gene expression imputation with network regularization and bulk RNA-seq data. Brief Bioinform. 2024; 25(3):bbae148. https://doi.org/10.1093/bib/bbae148.