[1] YANG JB, GAO HY, SONG YF, et al.Advances in understanding the metabolites and metabolomics of polygonum multiflorum thunb: a mini-review[J]. Current Drug Metabolism, 2021, 22(3): 165-172. [2] TEKA T, WANG L, GAO J, et al.Polygonum multiflorum: recent updates on newly isolated compounds, potential hepatotoxic compounds and their mechanisms[J]. Journal of Ethnopharmacology, 2021, 271: 113864. [3] ZHANG Q, XU Y, LV J, et al.Structure characterization of two functional polysaccharides from polygonum multiflorum and its immunomodulatory[J]. International Journal of Biological Macromolecules, 2018, 113: 195-204. [4] YANG JB, YE F, TIAN JY, et al.Multiflorumisides HK, stilbene glucosides isolated from polygonum multiflorum and their in vitro PTP1B inhibitory activities[J]. Fitoterapia, 2020, 146: 104703. [5] RAO T, LIU YT, ZENG XC, et al.The hepatotoxicity of polygonum multiflorum: The emerging role of the immune-mediated liver injury[J]. Acta Pharmacologica Sinica, 2021, 42(1): 27-35. [6] LI HY, YANG JB, LI WF, et al.In vivo hepatotoxicity screening of different extracts, components, and constituents of Polygoni Multiflori Thunb. in zebrafish (Danio rerio) larvae[J]. Biomedicine & Pharmacotherapy, 2020, 131: 110524. [7] LIN Y, XIAO R, XIA BH, et al.Investigation of the idiosyncratic hepatotoxicity of Polygonum multiflorum Thunb. through metabol-omics using GC-MS[J]. BMC Complementary Medicine and Therapies, 2021, 21(1): 120. [8] LI C, RAO T, CHEN X, et al.HLA-B*35:01 allele is a potential biomarker for predicting polygonum multiflorum-induced liver injury in humans[J]. Hepatology, 2019, 70(1): 346-357. [9] YANG JB, SONG YF, LIU Y, et al.UHPLC-QQQ-MS/MS assay for the quantification of dianthrones as potential toxic markers of Polygonum multiflorum Thunb: applications for the standardization of traditional Chinese medicines (TCMs) with endogenous toxicity[J]. Chinese Medicine, 2021, 16(1): 51. [10] RIFAIOGLU AS, ATAS H, MARTIN MJ, et al.Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases[J]. Briefings in Bioinformatics, 2019, 20(5): 1878-1912. [11] BANERJEE P, EREHMAN J, GOHLKE BO, et al.Super Natural II-a database of natural products[J]. Nucleic Acids Research, 2015, 43: D935-939. [12] ULTSCH A, LOTSCH J.Machine-learned cluster identification in high-dimensional data[J]. Journal of Biomedical Informatics, 2017, 66: 95-104. [13] EUN JW, BAE HJ, SHEN Q, et al.Characteristic molecular and proteomic signatures of drug-induced liver injury in a rat model[J]. Journal of Applied Toxicology, 2015, 35(2): 152-64. [14] GAN S, COSGROVE DA, GARDINER EJ, et al.Investigation of the use of spectral clustering for the analysis of molecular data[J]. Journal of Chemical Information and Modeling, 2014, 54(12): 3302-3319. [15] AWALE M, REYMOND JL.A multi-fingerprint browser for the ZINC database[J]. Nucleic Acids Research, 2014, 42: W234-239. [16] WANG Y, XIAO Q, CHEN P, et al.In silico prediction of drug-induced liver injury based on ensemble classifier method[J]. Intern-ational Journal of Molecular Sciences, 2019, 20(17): 4106-4119. [17] ALONSO-BETANZOS A, BOLON-CANEDO V.Big-data analysis, cluster analysis, and machine-learning approaches[J]. Advances in Experimental Medicine and Biology, 2018, 1065: 607-626. [18] CHEN WY, SONG Y, BAI H, et al.Parallel spectral clustering in distributed systems[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(3): 568-86. [19] ADEFIOYE AA, LIU X, DE MOOR B.Multi-view spectral clustering and its chemical application[J]. International Journal of Computational Biology and Drug Design, 2013, 6(1-2): 32-49. [20] CHEN M, BORLAK J, TONG W.High lipophilicity and high daily dose of oral medications are associated with significant risk for drug-induced liver injury[J]. Hepatology, 2013, 58(1): 388-396. |