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.
112 related articles for article (PubMed ID: 15962942)
1. Prediction of genotoxicity of chemical compounds by statistical learning methods. Li H; Ung CY; Yap CW; Xue Y; Li ZR; Cao ZW; Chen YZ Chem Res Toxicol; 2005 Jun; 18(6):1071-80. PubMed ID: 15962942 [TBL] [Abstract][Full Text] [Related]
2. Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods. Li H; Ung CY; Yap CW; Xue Y; Li ZR; Chen YZ J Mol Graph Model; 2006 Nov; 25(3):313-23. PubMed ID: 16497524 [TBL] [Abstract][Full Text] [Related]
3. Effect of selection of molecular descriptors on the prediction of blood-brain barrier penetrating and nonpenetrating agents by statistical learning methods. Li H; Yap CW; Ung CY; Xue Y; Cao ZW; Chen YZ J Chem Inf Model; 2005; 45(5):1376-84. PubMed ID: 16180914 [TBL] [Abstract][Full Text] [Related]
4. Prediction of factor Xa inhibitors by machine learning methods. Lin HH; Han LY; Yap CW; Xue Y; Liu XH; Zhu F; Chen YZ J Mol Graph Model; 2007 Sep; 26(2):505-18. PubMed ID: 17418603 [TBL] [Abstract][Full Text] [Related]
5. Prediction of P-glycoprotein substrates by a support vector machine approach. Xue Y; Yap CW; Sun LZ; Cao ZW; Wang JF; Chen YZ J Chem Inf Comput Sci; 2004; 44(4):1497-505. PubMed ID: 15272858 [TBL] [Abstract][Full Text] [Related]
6. Prediction of acetylcholinesterase inhibitors and characterization of correlative molecular descriptors by machine learning methods. Lv W; Xue Y Eur J Med Chem; 2010 Mar; 45(3):1167-72. PubMed ID: 20053484 [TBL] [Abstract][Full Text] [Related]
7. Classification of a diverse set of Tetrahymena pyriformis toxicity chemical compounds from molecular descriptors by statistical learning methods. Xue Y; Li H; Ung CY; Yap CW; Chen YZ Chem Res Toxicol; 2006 Aug; 19(8):1030-9. PubMed ID: 16918241 [TBL] [Abstract][Full Text] [Related]
8. Effect of molecular descriptor feature selection in support vector machine classification of pharmacokinetic and toxicological properties of chemical agents. Xue Y; Li ZR; Yap CW; Sun LZ; Chen X; Chen YZ J Chem Inf Comput Sci; 2004; 44(5):1630-8. PubMed ID: 15446820 [TBL] [Abstract][Full Text] [Related]
9. Prediction of antibacterial compounds by machine learning approaches. Yang XG; Chen D; Wang M; Xue Y; Chen YZ J Comput Chem; 2009 Jun; 30(8):1202-11. PubMed ID: 18988254 [TBL] [Abstract][Full Text] [Related]
10. Prediction of torsade-causing potential of drugs by support vector machine approach. Yap CW; Cai CZ; Xue Y; Chen YZ Toxicol Sci; 2004 May; 79(1):170-7. PubMed ID: 14976348 [TBL] [Abstract][Full Text] [Related]
11. Three new consensus QSAR models for the prediction of Ames genotoxicity. Votano JR; Parham M; Hall LH; Kier LB; Oloff S; Tropsha A; Xie Q; Tong W Mutagenesis; 2004 Sep; 19(5):365-77. PubMed ID: 15388809 [TBL] [Abstract][Full Text] [Related]
12. Quantitative structure-pharmacokinetic relationships for drug clearance by using statistical learning methods. Yap CW; Li ZR; Chen YZ J Mol Graph Model; 2006 Mar; 24(5):383-95. PubMed ID: 16290201 [TBL] [Abstract][Full Text] [Related]
13. Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions. Zernov VV; Balakin KV; Ivaschenko AA; Savchuk NP; Pletnev IV J Chem Inf Comput Sci; 2003; 43(6):2048-56. PubMed ID: 14632457 [TBL] [Abstract][Full Text] [Related]
14. Predicting the genotoxicity of thiophene derivatives from molecular structure. Mosier PD; Jurs PC; Custer LL; Durham SK; Pearl GM Chem Res Toxicol; 2003 Jun; 16(6):721-32. PubMed ID: 12807355 [TBL] [Abstract][Full Text] [Related]
15. Classification structure-activity relationship (CSAR) studies for prediction of genotoxicity of thiophene derivatives. Du H; Wang J; Watzl J; Zhang X; Hu Z Toxicol Lett; 2008 Feb; 177(1):10-9. PubMed ID: 18243595 [TBL] [Abstract][Full Text] [Related]
17. Strategy for genotoxicity testing: hazard identification and risk assessment in relation to in vitro testing. Thybaud V; Aardema M; Clements J; Dearfield K; Galloway S; Hayashi M; Jacobson-Kram D; Kirkland D; MacGregor JT; Marzin D; Ohyama W; Schuler M; Suzuki H; Zeiger E; Mutat Res; 2007 Feb; 627(1):41-58. PubMed ID: 17126066 [TBL] [Abstract][Full Text] [Related]
18. Effect of training data size and noise level on support vector machines virtual screening of genotoxic compounds from large compound libraries. Kumar P; Ma X; Liu X; Jia J; Bucong H; Xue Y; Li ZR; Yang SY; Wei YQ; Chen YZ J Comput Aided Mol Des; 2011 May; 25(5):455-67. PubMed ID: 21556903 [TBL] [Abstract][Full Text] [Related]
19. Prediction of novel and selective TNF-alpha converting enzyme (TACE) inhibitors and characterization of correlative molecular descriptors by machine learning approaches. Cong Y; Yang XG; Lv W; Xue Y J Mol Graph Model; 2009 Oct; 28(3):236-44. PubMed ID: 19729328 [TBL] [Abstract][Full Text] [Related]
20. 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] [Next] [New Search]