226 related articles for article (PubMed ID: 34360838)
1. Prediction of Drug-Induced Liver Toxicity Using SVM and Optimal Descriptor Sets.
Jaganathan K; Tayara H; Chong KT
Int J Mol Sci; 2021 Jul; 22(15):. PubMed ID: 34360838
[TBL] [Abstract][Full Text] [Related]
2. A comprehensive support vector machine binary hERG classification model based on extensive but biased end point hERG data sets.
Shen MY; Su BH; Esposito EX; Hopfinger AJ; Tseng YJ
Chem Res Toxicol; 2011 Jun; 24(6):934-49. PubMed ID: 21504223
[TBL] [Abstract][Full Text] [Related]
3. An Explainable Supervised Machine Learning Model for Predicting Respiratory Toxicity of Chemicals Using Optimal Molecular Descriptors.
Jaganathan K; Tayara H; Chong KT
Pharmaceutics; 2022 Apr; 14(4):. PubMed ID: 35456666
[TBL] [Abstract][Full Text] [Related]
4. Gene Expression Data Based Deep Learning Model for Accurate Prediction of Drug-Induced Liver Injury in Advance.
Feng C; Chen H; Yuan X; Sun M; Chu K; Liu H; Rui M
J Chem Inf Model; 2019 Jul; 59(7):3240-3250. PubMed ID: 31188585
[TBL] [Abstract][Full Text] [Related]
5. In silico prediction of major drug clearance pathways by support vector machines with feature-selected descriptors.
Toshimoto K; Wakayama N; Kusama M; Maeda K; Sugiyama Y; Akiyama Y
Drug Metab Dispos; 2014 Nov; 42(11):1811-9. PubMed ID: 25128502
[TBL] [Abstract][Full Text] [Related]
6. Hybrid in silico models for drug-induced liver injury using chemical descriptors and in vitro cell-imaging information.
Zhu XW; Sedykh A; Liu SS
J Appl Toxicol; 2014 Mar; 34(3):281-8. PubMed ID: 23640866
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. ADMET Evaluation in Drug Discovery. Part 17: Development of Quantitative and Qualitative Prediction Models for Chemical-Induced Respiratory Toxicity.
Lei T; Chen F; Liu H; Sun H; Kang Y; Li D; Li Y; Hou T
Mol Pharm; 2017 Jul; 14(7):2407-2421. PubMed ID: 28595388
[TBL] [Abstract][Full Text] [Related]
9. Ensemble Models Based on QuBiLS-MAS Features and Shallow Learning for the Prediction of Drug-Induced Liver Toxicity: Improving Deep Learning and Traditional Approaches.
Mora JR; Marrero-Ponce Y; García-Jacas CR; Suarez Causado A
Chem Res Toxicol; 2020 Jul; 33(7):1855-1873. PubMed ID: 32406679
[TBL] [Abstract][Full Text] [Related]
10. [Quantitative structure-activity relationship model for prediction of cardiotoxicity of chemical components in traditional Chinese medicines].
Beijing Da Xue Xue Bao Yi Xue Ban; 2017 Jun; 49(3):551-556. PubMed ID: 28628163
[TBL] [Abstract][Full Text] [Related]
11. Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI).
Minerali E; Foil DH; Zorn KM; Lane TR; Ekins S
Mol Pharm; 2020 Jul; 17(7):2628-2637. PubMed ID: 32422053
[TBL] [Abstract][Full Text] [Related]
12. In Silico Prediction of Chemical-Induced Hepatocellular Hypertrophy Using Molecular Descriptors.
Ambe K; Ishihara K; Ochibe T; Ohya K; Tamura S; Inoue K; Yoshida M; Tohkin M
Toxicol Sci; 2018 Apr; 162(2):667-675. PubMed ID: 29309657
[TBL] [Abstract][Full Text] [Related]
13. Classification of nervous system withdrawn and approved drugs with ToxPrint features via machine learning strategies.
Onay A; Onay M; Abul O
Comput Methods Programs Biomed; 2017 Apr; 142():9-19. PubMed ID: 28325450
[TBL] [Abstract][Full Text] [Related]
14. hERG classification model based on a combination of support vector machine method and GRIND descriptors.
Li Q; Jørgensen FS; Oprea T; Brunak S; Taboureau O
Mol Pharm; 2008; 5(1):117-27. PubMed ID: 18197627
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Prediction models for drug-induced hepatotoxicity by using weighted molecular fingerprints.
Kim E; Nam H
BMC Bioinformatics; 2017 May; 18(Suppl 7):227. PubMed ID: 28617228
[TBL] [Abstract][Full Text] [Related]
17. Computational chemistry approach for the early detection of drug-induced idiosyncratic liver toxicity.
Cruz-Monteagudo M; Cordeiro MN; Borges F
J Comput Chem; 2008 Mar; 29(4):533-49. PubMed ID: 17705164
[TBL] [Abstract][Full Text] [Related]
18. In silico prediction of mitochondrial toxicity by using GA-CG-SVM approach.
Zhang H; Chen QY; Xiang ML; Ma CY; Huang Q; Yang SY
Toxicol In Vitro; 2009 Feb; 23(1):134-40. PubMed ID: 18940245
[TBL] [Abstract][Full Text] [Related]
19. Combinatorial QSAR of ambergris fragrance compounds.
Kovatcheva A; Golbraikh A; Oloff S; Xiao YD; Zheng W; Wolschann P; Buchbauer G; Tropsha A
J Chem Inf Comput Sci; 2004; 44(2):582-95. PubMed ID: 15032539
[TBL] [Abstract][Full Text] [Related]
20. Quantitative structure-activity relationship models for predicting drug-induced liver injury based on FDA-approved drug labeling annotation and using a large collection of drugs.
Chen M; Hong H; Fang H; Kelly R; Zhou G; Borlak J; Tong W
Toxicol Sci; 2013 Nov; 136(1):242-9. PubMed ID: 23997115
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
