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For: He S, Ye T, Wang R, Zhang C, Zhang X, Sun G, Sun X. An In Silico Model for Predicting Drug-Induced Hepatotoxicity. Int J Mol Sci 2019;20:E1897. [PMID: 30999595 DOI: 10.3390/ijms20081897] [Cited by in Crossref: 19] [Cited by in F6Publishing: 20] [Article Influence: 6.3] [Reference Citation Analysis]
Number Citing Articles
1 Gijbels E, Pieters A, De Muynck K, Vinken M, Devisscher L. Rodent models of cholestatic liver disease: A practical guide for translational research. Liver Int 2021;41:656-82. [PMID: 33486884 DOI: 10.1111/liv.14800] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
2 Belfield SJ, Enoch SJ, Firman JW, Madden JC, Schultz TW, Cronin MTD. Determination of "fitness-for-purpose" of quantitative structure-activity relationship (QSAR) models to predict (eco-)toxicological endpoints for regulatory use. Regul Toxicol Pharmacol 2021;123:104956. [PMID: 33979632 DOI: 10.1016/j.yrtph.2021.104956] [Reference Citation Analysis]
3 Ivanov SM, Lagunin AA, Filimonov DA, Poroikov VV. Relationships between the Structure and Severe Drug-Induced Liver Injury for Low, Medium, and High Doses of Drugs. Chem Res Toxicol 2022. [PMID: 35172101 DOI: 10.1021/acs.chemrestox.1c00307] [Reference Citation Analysis]
4 Ancuceanu R, Hovanet MV, Anghel AI, Furtunescu F, Neagu M, Constantin C, Dinu M. Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset. Int J Mol Sci 2020;21:E2114. [PMID: 32204453 DOI: 10.3390/ijms21062114] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]
5 Wang B, Tan X, Guo J, Xiao T, Jiao Y, Zhao J, Wu J, Wang Y. Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning. Pharmaceutics 2022;14:943. [DOI: 10.3390/pharmaceutics14050943] [Reference Citation Analysis]
6 Kim H, Kim E, Lee I, Bae B, Park M, Nam H. Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches. Biotechnol Bioprocess Eng 2020;25:895-930. [PMID: 33437151 DOI: 10.1007/s12257-020-0049-y] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 7.0] [Reference Citation Analysis]
7 Minerali E, Foil DH, Zorn KM, Lane TR, Ekins S. Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI). Mol Pharm 2020;17:2628-37. [PMID: 32422053 DOI: 10.1021/acs.molpharmaceut.0c00326] [Cited by in Crossref: 16] [Cited by in F6Publishing: 13] [Article Influence: 8.0] [Reference Citation Analysis]
8 Wang MWH, Goodman JM, Allen TEH. Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models. Chem Res Toxicol 2021;34:217-39. [PMID: 33356168 DOI: 10.1021/acs.chemrestox.0c00316] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
9 Vall A, Sabnis Y, Shi J, Class R, Hochreiter S, Klambauer G. The Promise of AI for DILI Prediction. Front Artif Intell 2021;4:638410. [PMID: 33937745 DOI: 10.3389/frai.2021.638410] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Mora JR, Marrero-Ponce Y, García-Jacas CR, Suarez Causado A. Ensemble Models Based on QuBiLS-MAS Features and Shallow Learning for the Prediction of Drug-Induced Liver Toxicity: Improving Deep Learning and Traditional Approaches. Chem Res Toxicol 2020;33:1855-73. [PMID: 32406679 DOI: 10.1021/acs.chemrestox.0c00030] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]
11 Liu X, Zheng D, Zhong Y, Xia Z, Luo H, Weng Z. Machine-Learning Prediction of Oral Drug-Induced Liver Injury (DILI) via Multiple Features and Endpoints. Biomed Res Int 2020;2020:4795140. [PMID: 32509859 DOI: 10.1155/2020/4795140] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Domínguez-Villa FX, Durán-Iturbide NA, Ávila-Zárraga JG. Synthesis, molecular docking, and in silico ADME/Tox profiling studies of new 1-aryl-5-(3-azidopropyl)indol-4-ones: Potential inhibitors of SARS CoV-2 main protease. Bioorg Chem 2021;106:104497. [PMID: 33261847 DOI: 10.1016/j.bioorg.2020.104497] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
13 Garcia de Lomana M, Morger A, Norinder U, Buesen R, Landsiedel R, Volkamer A, Kirchmair J, Mathea M. ChemBioSim: Enhancing Conformal Prediction of In Vivo Toxicity by Use of Predicted Bioactivities. J Chem Inf Model 2021;61:3255-72. [PMID: 34153183 DOI: 10.1021/acs.jcim.1c00451] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Li T, Tong W, Roberts R, Liu Z, Thakkar S. DeepDILI: Deep Learning-Powered Drug-Induced Liver Injury Prediction Using Model-Level Representation. Chem Res Toxicol 2021;34:550-65. [PMID: 33356151 DOI: 10.1021/acs.chemrestox.0c00374] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
15 Allen TEH, Wedlake AJ, Gelžinytė E, Gong C, Goodman JM, Gutsell S, Russell PJ. Neural network activation similarity: a new measure to assist decision making in chemical toxicology. Chem Sci 2020;11:7335-48. [PMID: 34123016 DOI: 10.1039/d0sc01637c] [Cited by in Crossref: 5] [Article Influence: 2.5] [Reference Citation Analysis]
16 Kurosaki K, Uesawa Y. Development of in silico prediction models for drug-induced liver malignant tumors based on the activity of molecular initiating events: Biologically interpretable features. J Toxicol Sci 2022;47:89-98. [PMID: 35236804 DOI: 10.2131/jts.47.89] [Reference Citation Analysis]
17 Jaganathan K, Tayara H, Chong KT. Prediction of Drug-Induced Liver Toxicity Using SVM and Optimal Descriptor Sets. Int J Mol Sci 2021;22:8073. [PMID: 34360838 DOI: 10.3390/ijms22158073] [Reference Citation Analysis]
18 Feng H, Zhang L, Li S, Liu L, Yang T, Yang P, Zhao J, Arkin IT, Liu H. Predicting the reproductive toxicity of chemicals using ensemble learning methods and molecular fingerprints. Toxicol Lett 2021;340:4-14. [PMID: 33421549 DOI: 10.1016/j.toxlet.2021.01.002] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
19 Durán-Iturbide NA, Díaz-Eufracio BI, Medina-Franco JL. In Silico ADME/Tox Profiling of Natural Products: A Focus on BIOFACQUIM. ACS Omega 2020;5:16076-84. [PMID: 32656429 DOI: 10.1021/acsomega.0c01581] [Cited by in Crossref: 14] [Cited by in F6Publishing: 10] [Article Influence: 7.0] [Reference Citation Analysis]
20 Liu A, Walter M, Wright P, Bartosik A, Dolciami D, Elbasir A, Yang H, Bender A. Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure. Biol Direct 2021;16:6. [PMID: 33461600 DOI: 10.1186/s13062-020-00285-0] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
21 Kang W, Podtelezhnikov AA, Tanis KQ, Pacchione S, Su M, Bleicher KB, Wang Z, Laws GM, Griffiths TG, Kuhls MC, Chen Q, Knemeyer I, Marsh DJ, Mitra K, Lebron J, Sistare FD. Development and Application of a Transcriptomic Signature of Bioactivation in an Advanced In Vitro Liver Model to Reduce Drug-induced Liver Injury Risk Early in the Pharmaceutical Pipeline. Toxicol Sci 2020;177:121-39. [PMID: 32559289 DOI: 10.1093/toxsci/kfaa094] [Cited by in Crossref: 11] [Cited by in F6Publishing: 10] [Article Influence: 11.0] [Reference Citation Analysis]
22 Zhou Y, Li S, Zhao Y, Guo M, Liu Y, Li M, Wen Z. Quantitative Structure-Activity Relationship (QSAR) Model for the Severity Prediction of Drug-Induced Rhabdomyolysis by Using Random Forest. Chem Res Toxicol 2021;34:514-21. [PMID: 33393765 DOI: 10.1021/acs.chemrestox.0c00347] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
23 He S, Zhang X, Lu S, Zhu T, Sun G, Sun X. A Computational Toxicology Approach to Screen the Hepatotoxic Ingredients in Traditional Chinese Medicines: Polygonum multiflorum Thunb as a Case Study. Biomolecules 2019;9:E577. [PMID: 31591318 DOI: 10.3390/biom9100577] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 2.3] [Reference Citation Analysis]