These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
207 related articles for article (PubMed ID: 28238947)
101. An In Silico Model for Predicting Drug-Induced Hepatotoxicity. He S; Ye T; Wang R; Zhang C; Zhang X; Sun G; Sun X Int J Mol Sci; 2019 Apr; 20(8):. PubMed ID: 30999595 [TBL] [Abstract][Full Text] [Related]
102. A Unique In Vitro Assay to Investigate ABCB4 Transport Function. Temesszentandrási-Ambrus C; Nagy G; Bui A; Gáborik Z Int J Mol Sci; 2023 Feb; 24(5):. PubMed ID: 36901890 [TBL] [Abstract][Full Text] [Related]
103. Exploring molecular fingerprints of different drugs having bile interaction: a stepping stone towards better drug delivery. Sardar S; Bhattacharya A; Amin SA; Jha T; Gayen S Mol Divers; 2024 Jun; 28(3):1471-1483. PubMed ID: 37369957 [TBL] [Abstract][Full Text] [Related]
104. Integrating Drug's Mode of Action into Quantitative Structure-Activity Relationships for Improved Prediction of Drug-Induced Liver Injury. Wu L; Liu Z; Auerbach S; Huang R; Chen M; McEuen K; Xu J; Fang H; Tong W J Chem Inf Model; 2017 Apr; 57(4):1000-1006. PubMed ID: 28350954 [TBL] [Abstract][Full Text] [Related]
105. Predicting mTOR inhibitors with a classifier using recursive partitioning and Naïve Bayesian approaches. Wang L; Chen L; Liu Z; Zheng M; Gu Q; Xu J PLoS One; 2014; 9(5):e95221. PubMed ID: 24819222 [TBL] [Abstract][Full Text] [Related]
106. Predicting drug-induced liver injury: The importance of data curation. Kotsampasakou E; Montanari F; Ecker GF Toxicology; 2017 Aug; 389():139-145. PubMed ID: 28652195 [TBL] [Abstract][Full Text] [Related]
107. In silico Prediction of Drug Induced Liver Toxicity Using Substructure Pattern Recognition Method. Zhang C; Cheng F; Li W; Liu G; Lee PW; Tang Y Mol Inform; 2016 Apr; 35(3-4):136-44. PubMed ID: 27491923 [TBL] [Abstract][Full Text] [Related]
108. Prediction of drug induced liver injury using molecular and biological descriptors. Muller C; Pekthong D; Alexandre E; Marcou G; Horvath D; Richert L; Varnek A Comb Chem High Throughput Screen; 2015; 18(3):315-22. PubMed ID: 25747442 [TBL] [Abstract][Full Text] [Related]
110. Quantitative structure-activity relationship analysis of β-amyloid aggregation inhibitors. Stempler S; Levy-Sakin M; Frydman-Marom A; Amir Y; Scherzer-Attali R; Buzhansky L; Gazit E; Senderowitz H J Comput Aided Mol Des; 2011 Feb; 25(2):135-44. PubMed ID: 21165759 [TBL] [Abstract][Full Text] [Related]
111. Identification of Organic Anion Transporter 2 Inhibitors: Screening, Structure-Based Analysis, and Clinical Drug Interaction Risk Assessment. Ryu S; Woody N; Chang G; Mathialagan S; Varma MVS J Med Chem; 2022 Nov; 65(21):14578-14588. PubMed ID: 36270005 [TBL] [Abstract][Full Text] [Related]
112. Structural Analysis and Identification of False Positive Hits in Luciferase-Based Assays. Yang ZY; Dong J; Yang ZJ; Lu AP; Hou TJ; Cao DS J Chem Inf Model; 2020 Apr; 60(4):2031-2043. PubMed ID: 32202787 [TBL] [Abstract][Full Text] [Related]
113. Classification of a Naïve Bayesian Fingerprint model to predict reproductive toxicity Marzo M; Benfenati E SAR QSAR Environ Res; 2018 Aug; 29(8):631-645. PubMed ID: 30063413 [TBL] [Abstract][Full Text] [Related]
115. Discovery of VEGFR2 inhibitors by integrating naïve Bayesian classification, molecular docking and drug screening approaches. Kang D; Pang X; Lian W; Xu L; Wang J; Jia H; Zhang B; Liu AL; Du GH RSC Adv; 2018 Jan; 8(10):5286-5297. PubMed ID: 35542432 [TBL] [Abstract][Full Text] [Related]
116. Predicting cytotoxicity from heterogeneous data sources with Bayesian learning. Langdon SR; Mulgrew J; Paolini GV; van Hoorn WP J Cheminform; 2010 Dec; 2(1):11. PubMed ID: 21143909 [TBL] [Abstract][Full Text] [Related]
117. Predicting Drug-Induced Liver Injury Using Convolutional Neural Network and Molecular Fingerprint-Embedded Features. Nguyen-Vo TH; Nguyen L; Do N; Le PH; Nguyen TN; Nguyen BP; Le L ACS Omega; 2020 Oct; 5(39):25432-25439. PubMed ID: 33043223 [TBL] [Abstract][Full Text] [Related]
118. A New Structure-Activity Relationship (SAR) Model for Predicting Drug-Induced Liver Injury, Based on Statistical and Expert-Based Structural Alerts. Pizzo F; Lombardo A; Manganaro A; Benfenati E Front Pharmacol; 2016; 7():442. PubMed ID: 27920722 [TBL] [Abstract][Full Text] [Related]
119. Vienna LiverTox Workspace-A Set of Machine Learning Models for Prediction of Interactions Profiles of Small Molecules With Transporters Relevant for Regulatory Agencies. Montanari F; Knasmüller B; Kohlbacher S; Hillisch C; Baierová C; Grandits M; Ecker GF Front Chem; 2019; 7():899. PubMed ID: 31998690 [TBL] [Abstract][Full Text] [Related]
120. Exploring the quality of protein structural models from a Bayesian perspective. Arroyuelo A; Vila JA; Martin OA J Comput Chem; 2021 Aug; 42(21):1466-1474. PubMed ID: 33990982 [TBL] [Abstract][Full Text] [Related] [Previous] [Next] [New Search]