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For: Liu J, Mansouri K, Judson RS, Martin MT, Hong H, Chen M, Xu X, Thomas RS, Shah I. Predicting hepatotoxicity using ToxCast in vitro bioactivity and chemical structure. Chem Res Toxicol 2015;28:738-51. [PMID: 25697799 DOI: 10.1021/tx500501h] [Cited by in Crossref: 87] [Cited by in F6Publishing: 70] [Article Influence: 12.4] [Reference Citation Analysis]
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7 Hong H, Chen M, Ng HW, Tong W. QSAR Models at the US FDA/NCTR. Methods Mol Biol 2016;1425:431-59. [PMID: 27311476 DOI: 10.1007/978-1-4939-3609-0_18] [Cited by in Crossref: 12] [Cited by in F6Publishing: 8] [Article Influence: 2.4] [Reference Citation Analysis]
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9 Sakkiah S, Kusko R, Tong W, Hong H. Applications of Molecular Dynamics Simulations in Computational Toxicology. In: Hong H, editor. Advances in Computational Toxicology. Cham: Springer International Publishing; 2019. pp. 181-212. [DOI: 10.1007/978-3-030-16443-0_10] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 1.7] [Reference Citation Analysis]
10 Zhu X, Xin Y, Chen Q. Chemical and in vitro biological information to predict mouse liver toxicity using recursive random forests. SAR and QSAR in Environmental Research 2016;27:559-72. [DOI: 10.1080/1062936x.2016.1201142] [Cited by in Crossref: 8] [Cited by in F6Publishing: 2] [Article Influence: 1.3] [Reference Citation Analysis]
11 Liu L, Fu L, Zhang JW, Wei H, Ye WL, Deng ZK, Zhang L, Cheng Y, Ouyang D, Cao Q, Cao DS. Three-Level Hepatotoxicity Prediction System Based on Adverse Hepatic Effects. Mol Pharm 2019;16:393-408. [PMID: 30475633 DOI: 10.1021/acs.molpharmaceut.8b01048] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
12 Hao Y, Moore JH. TargetTox: A Feature Selection Pipeline for Identifying Predictive Targets Associated with Drug Toxicity. J Chem Inf Model 2021;61:5386-94. [PMID: 34757743 DOI: 10.1021/acs.jcim.1c00733] [Reference Citation Analysis]
13 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]
14 Seal S, Yang H, Vollmers L, Bender A. Comparison of Cellular Morphological Descriptors and Molecular Fingerprints for the Prediction of Cytotoxicity- and Proliferation-Related Assays. Chem Res Toxicol 2021;34:422-37. [PMID: 33522793 DOI: 10.1021/acs.chemrestox.0c00303] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
15 Thomas RS, Bahadori T, Buckley TJ, Cowden J, Deisenroth C, Dionisio KL, Frithsen JB, Grulke CM, Gwinn MR, Harrill JA, Higuchi M, Houck KA, Hughes MF, Hunter ES, Isaacs KK, Judson RS, Knudsen TB, Lambert JC, Linnenbrink M, Martin TM, Newton SR, Padilla S, Patlewicz G, Paul-Friedman K, Phillips KA, Richard AM, Sams R, Shafer TJ, Setzer RW, Shah I, Simmons JE, Simmons SO, Singh A, Sobus JR, Strynar M, Swank A, Tornero-Valez R, Ulrich EM, Villeneuve DL, Wambaugh JF, Wetmore BA, Williams AJ. The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency. Toxicol Sci 2019;169:317-32. [PMID: 30835285 DOI: 10.1093/toxsci/kfz058] [Cited by in Crossref: 91] [Cited by in F6Publishing: 79] [Article Influence: 45.5] [Reference Citation Analysis]
16 Kassotis CD, Stapleton HM. Endocrine-Mediated Mechanisms of Metabolic Disruption and New Approaches to Examine the Public Health Threat. Front Endocrinol (Lausanne) 2019;10:39. [PMID: 30792693 DOI: 10.3389/fendo.2019.00039] [Cited by in Crossref: 23] [Cited by in F6Publishing: 19] [Article Influence: 7.7] [Reference Citation Analysis]
17 Landry KA, Boyer TH. Fixed Bed Modeling of Nonsteroidal Anti-Inflammatory Drug Removal by Ion-Exchange in Synthetic Urine: Mass Removal or Toxicity Reduction? Environ Sci Technol 2017;51:10072-80. [DOI: 10.1021/acs.est.7b02273] [Cited by in Crossref: 11] [Cited by in F6Publishing: 5] [Article Influence: 2.2] [Reference Citation Analysis]
18 Daneshian M, Kamp H, Hengstler J, Leist M, van de Water B. Highlight report: Launch of a large integrated European in vitro toxicology project: EU-ToxRisk. Arch Toxicol 2016;90:1021-4. [PMID: 27017488 DOI: 10.1007/s00204-016-1698-7] [Cited by in Crossref: 34] [Cited by in F6Publishing: 32] [Article Influence: 5.7] [Reference Citation Analysis]
19 Sun Y, Shi S, Li Y, Wang Q. Development of quantitative structure-activity relationship models to predict potential nephrotoxic ingredients in traditional Chinese medicines. Food Chem Toxicol 2019;128:163-70. [PMID: 30954639 DOI: 10.1016/j.fct.2019.03.056] [Cited by in Crossref: 11] [Cited by in F6Publishing: 10] [Article Influence: 3.7] [Reference Citation Analysis]
20 Ramirez T, Strigun A, Verlohner A, Huener HA, Peter E, Herold M, Bordag N, Mellert W, Walk T, Spitzer M, Jiang X, Sperber S, Hofmann T, Hartung T, Kamp H, van Ravenzwaay B. Prediction of liver toxicity and mode of action using metabolomics in vitro in HepG2 cells. Arch Toxicol 2018;92:893-906. [PMID: 28965233 DOI: 10.1007/s00204-017-2079-6] [Cited by in Crossref: 58] [Cited by in F6Publishing: 46] [Article Influence: 11.6] [Reference Citation Analysis]
21 Capuzzi SJ, Politi R, Isayev O, Farag S, Tropsha A. QSAR Modeling of Tox21 Challenge Stress Response and Nuclear Receptor Signaling Toxicity Assays. Front Environ Sci 2016;4. [DOI: 10.3389/fenvs.2016.00003] [Cited by in Crossref: 39] [Cited by in F6Publishing: 15] [Article Influence: 6.5] [Reference Citation Analysis]
22 Ambe K, Ishihara K, Ochibe T, Ohya K, Tamura S, Inoue K, Yoshida M, Tohkin M. In Silico Prediction of Chemical-Induced Hepatocellular Hypertrophy Using Molecular Descriptors. Toxicol Sci 2018;162:667-75. [PMID: 29309657 DOI: 10.1093/toxsci/kfx287] [Cited by in Crossref: 12] [Cited by in F6Publishing: 11] [Article Influence: 4.0] [Reference Citation Analysis]
23 Zhao L, Russo DP, Wang W, Aleksunes LM, Zhu H. Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data. Toxicol Sci 2020;174:178-88. [PMID: 32073637 DOI: 10.1093/toxsci/kfaa005] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
24 Takeshita J, Nakayama H, Kitsunai Y, Tanabe M, Oki H, Sasaki T, Yoshinari K. Discriminative models using molecular descriptors for predicting increased serum ALT levels in repeated-dose toxicity studies of rats. Computational Toxicology 2018;6:64-70. [DOI: 10.1016/j.comtox.2017.05.002] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 1.3] [Reference Citation Analysis]
25 Ng HW, Leggett C, Sakkiah S, Pan B, Ye H, Wu L, Selvaraj C, Tong W, Hong H. Competitive docking model for prediction of the human nicotinic acetylcholine receptor α7 binding of tobacco constituents. Oncotarget 2018;9:16899-916. [PMID: 29682193 DOI: 10.18632/oncotarget.24458] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
26 Alves VM, Golbraikh A, Capuzzi SJ, Liu K, Lam WI, Korn DR, Pozefsky D, Andrade CH, Muratov EN, Tropsha A. Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure-Activity Relationship Models. J Chem Inf Model 2018;58:1214-23. [PMID: 29809005 DOI: 10.1021/acs.jcim.8b00124] [Cited by in Crossref: 21] [Cited by in F6Publishing: 17] [Article Influence: 5.3] [Reference Citation Analysis]
27 Mahmoud SY, Svensson F, Zoufir A, Módos D, Afzal AM, Bender A. Understanding Conditional Associations between ToxCast in Vitro Readouts and the Hepatotoxicity of Compounds Using Rule-Based Methods. Chem Res Toxicol 2020;33:137-53. [DOI: 10.1021/acs.chemrestox.8b00382] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 Dai W, Tang T, Dai Z, Shi D, Mo L, Zhang Y. Probing the Mechanism of Hepatotoxicity of Hexabromocyclododecanes through Toxicological Network Analysis. Environ Sci Technol 2020;54:15235-45. [DOI: 10.1021/acs.est.0c03998] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
29 Neagu M, Caruntu C, Constantin C, Boda D, Zurac S, Spandidos DA, Tsatsakis AM. Chemically induced skin carcinogenesis: Updates in experimental models (Review). Oncol Rep 2016;35:2516-28. [PMID: 26986013 DOI: 10.3892/or.2016.4683] [Cited by in Crossref: 77] [Cited by in F6Publishing: 72] [Article Influence: 12.8] [Reference Citation Analysis]
30 Watford S, Ly Pham L, Wignall J, Shin R, Martin MT, Friedman KP. ToxRefDB version 2.0: Improved utility for predictive and retrospective toxicology analyses. Reprod Toxicol 2019;89:145-58. [PMID: 31340180 DOI: 10.1016/j.reprotox.2019.07.012] [Cited by in Crossref: 18] [Cited by in F6Publishing: 19] [Article Influence: 6.0] [Reference Citation Analysis]
31 Humphreys WG, Will Y, Guengerich FP. Toxicology Strategies for Drug Discovery - Present and Future: Introduction. Chem Res Toxicol 2016;29:437. [PMID: 27087588 DOI: 10.1021/acs.chemrestox.6b00049] [Cited by in Crossref: 2] [Article Influence: 0.3] [Reference Citation Analysis]
32 Banerjee P, Eckert AO, Schrey AK, Preissner R. ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res 2018;46:W257-63. [PMID: 29718510 DOI: 10.1093/nar/gky318] [Cited by in Crossref: 215] [Cited by in F6Publishing: 162] [Article Influence: 71.7] [Reference Citation Analysis]
33 Luo H, Ye H, Ng HW, Sakkiah S, Mendrick DL, Hong H. sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides. Sci Rep 2016;6:32115. [PMID: 27558848 DOI: 10.1038/srep32115] [Cited by in Crossref: 14] [Cited by in F6Publishing: 9] [Article Influence: 2.3] [Reference Citation Analysis]
34 Wang D. Infer the in vivo point of departure with ToxCast in vitro assay data using a robust learning approach. Arch Toxicol 2018;92:2913-22. [PMID: 29995190 DOI: 10.1007/s00204-018-2260-6] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
35 Hewitt M, Przybylak K. In Silico Models for Hepatotoxicity. Methods Mol Biol 2016;1425:201-36. [PMID: 27311469 DOI: 10.1007/978-1-4939-3609-0_11] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 1.2] [Reference Citation Analysis]
36 Kotsampasakou E, Montanari F, Ecker GF. Predicting drug-induced liver injury: The importance of data curation. Toxicology 2017;389:139-45. [PMID: 28652195 DOI: 10.1016/j.tox.2017.06.003] [Cited by in Crossref: 33] [Cited by in F6Publishing: 22] [Article Influence: 6.6] [Reference Citation Analysis]
37 Wu L, Liu Z, Auerbach S, Huang R, Chen M, McEuen K, Xu J, Fang H, Tong W. Integrating Drug's Mode of Action into Quantitative Structure-Activity Relationships for Improved Prediction of Drug-Induced Liver Injury. J Chem Inf Model 2017;57:1000-6. [PMID: 28350954 DOI: 10.1021/acs.jcim.6b00719] [Cited by in Crossref: 13] [Cited by in F6Publishing: 9] [Article Influence: 2.6] [Reference Citation Analysis]
38 Ye H, Luo H, Ng HW, Meehan J, Ge W, Tong W, Hong H. Applying network analysis and Nebula (neighbor-edges based and unbiased leverage algorithm) to ToxCast data. Environ Int 2016;89-90:81-92. [PMID: 26826365 DOI: 10.1016/j.envint.2016.01.010] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 0.7] [Reference Citation Analysis]
39 Egeghy PP, Sheldon LS, Isaacs KK, Özkaynak H, Goldsmith MR, Wambaugh JF, Judson RS, Buckley TJ. Computational Exposure Science: An Emerging Discipline to Support 21st-Century Risk Assessment. Environ Health Perspect 2016;124:697-702. [PMID: 26545029 DOI: 10.1289/ehp.1509748] [Cited by in Crossref: 46] [Cited by in F6Publishing: 26] [Article Influence: 6.6] [Reference Citation Analysis]
40 Wang P, Xia P, Yang J, Wang Z, Peng Y, Shi W, Villeneuve DL, Yu H, Zhang X. A Reduced Transcriptome Approach to Assess Environmental Toxicants Using Zebrafish Embryo Test. Environ Sci Technol 2018;52:821-30. [PMID: 29224359 DOI: 10.1021/acs.est.7b04073] [Cited by in Crossref: 28] [Cited by in F6Publishing: 26] [Article Influence: 7.0] [Reference Citation Analysis]
41 Adam M, Fleischer H, Thurow K. Generic and Automated Data Evaluation in Analytical Measurement. SLAS Technol 2017;22:186-94. [PMID: 27738238 DOI: 10.1177/2211068216672613] [Cited by in Crossref: 3] [Article Influence: 0.5] [Reference Citation Analysis]
42 Grenet I, Merlo K, Comet JP, Tertiaux R, Rouquié D, Dayan F. Stacked Generalization with Applicability Domain Outperforms Simple QSAR on in Vitro Toxicological Data. J Chem Inf Model 2019;59:1486-96. [PMID: 30735402 DOI: 10.1021/acs.jcim.8b00553] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
43 Sakhteman A, Failli M, Kublbeck J, Levonen AL, Fortino V. A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity. Environ Int 2021;156:106751. [PMID: 34271427 DOI: 10.1016/j.envint.2021.106751] [Reference Citation Analysis]
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45 Fraser K, Bruckner DM, Dordick JS. Advancing Predictive Hepatotoxicity at the Intersection of Experimental, in Silico, and Artificial Intelligence Technologies. Chem Res Toxicol 2018;31:412-30. [PMID: 29722533 DOI: 10.1021/acs.chemrestox.8b00054] [Cited by in Crossref: 14] [Cited by in F6Publishing: 10] [Article Influence: 3.5] [Reference Citation Analysis]
46 Hong H, Shen J, Ng HW, Sakkiah S, Ye H, Ge W, Gong P, Xiao W, Tong W. A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals. Int J Environ Res Public Health 2016;13:372. [PMID: 27023588 DOI: 10.3390/ijerph13040372] [Cited by in Crossref: 13] [Cited by in F6Publishing: 11] [Article Influence: 2.2] [Reference Citation Analysis]
47 Klaren WD, Ring C, Harris MA, Thompson CM, Borghoff S, Sipes NS, Hsieh JH, Auerbach SS, Rager JE. Identifying Attributes That Influence In Vitro-to-In Vivo Concordance by Comparing In Vitro Tox21 Bioactivity Versus In Vivo DrugMatrix Transcriptomic Responses Across 130 Chemicals. Toxicol Sci 2019;167:157-71. [PMID: 30202884 DOI: 10.1093/toxsci/kfy220] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 4.0] [Reference Citation Analysis]
48 Rose KA, Holman NS, Green AM, Andersen ME, LeCluyse EL. Co-culture of Hepatocytes and Kupffer Cells as an In Vitro Model of Inflammation and Drug-Induced Hepatotoxicity. J Pharm Sci 2016;105:950-64. [PMID: 26869439 DOI: 10.1016/S0022-3549(15)00192-6] [Cited by in Crossref: 48] [Cited by in F6Publishing: 13] [Article Influence: 8.0] [Reference Citation Analysis]
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50 van der Ven LTM, Rorije E, Sprong RC, Zink D, Derr R, Hendriks G, Loo LH, Luijten M. A Case Study with Triazole Fungicides to Explore Practical Application of Next-Generation Hazard Assessment Methods for Human Health. Chem Res Toxicol 2020;33:834-48. [PMID: 32041405 DOI: 10.1021/acs.chemrestox.9b00484] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
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52 Morger A, Mathea M, Achenbach JH, Wolf A, Buesen R, Schleifer KJ, Landsiedel R, Volkamer A. KnowTox: pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development. J Cheminform 2020;12:24. [PMID: 33431007 DOI: 10.1186/s13321-020-00422-x] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
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58 Chushak YG, Shows HW, Gearhart JM, Pangburn HA. In silico identification of protein targets for chemical neurotoxins using ToxCast in vitro data and read-across within the QSAR toolbox. Toxicol Res (Camb) 2018;7:423-31. [PMID: 30090592 DOI: 10.1039/c7tx00268h] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 0.8] [Reference Citation Analysis]
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