682 related articles for article (PubMed ID: 17125269)
1. QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation.
Votano JR; Parham M; Hall LM; Hall LH; Kier LB; Oloff S; Tropsha A
J Med Chem; 2006 Nov; 49(24):7169-81. PubMed ID: 17125269
[TBL] [Abstract][Full Text] [Related]
2. Prediction of HPLC retention index using artificial neural networks and IGroup E-state indices.
Albaugh DR; Hall LM; Hill DW; Kertesz TM; Parham M; Hall LH; Grant DF
J Chem Inf Model; 2009 Apr; 49(4):788-99. PubMed ID: 19309176
[TBL] [Abstract][Full Text] [Related]
3. A novel QSAR model for prediction of apoptosis-inducing activity of 4-aryl-4-H-chromenes based on support vector machine.
Fatemi MH; Gharaghani S
Bioorg Med Chem; 2007 Dec; 15(24):7746-54. PubMed ID: 17870538
[TBL] [Abstract][Full Text] [Related]
4. Application of validated QSAR models of D1 dopaminergic antagonists for database mining.
Oloff S; Mailman RB; Tropsha A
J Med Chem; 2005 Nov; 48(23):7322-32. PubMed ID: 16279792
[TBL] [Abstract][Full Text] [Related]
5. QSPR modeling of soil sorption coefficients (K(OC)) of pesticides using SPA-ANN and SPA-MLR.
Goudarzi N; Goodarzi M; Araujo MC; Galvão RK
J Agric Food Chem; 2009 Aug; 57(15):7153-8. PubMed ID: 19722589
[TBL] [Abstract][Full Text] [Related]
6. Prediction of aqueous solubility based on large datasets using several QSPR models utilizing topological structure representation.
Votano JR; Parham M; Hall LH; Kier LB; Hall LM
Chem Biodivers; 2004 Nov; 1(11):1829-41. PubMed ID: 17191819
[TBL] [Abstract][Full Text] [Related]
7. Comparison of Multiple Linear Regressions and Neural Networks based QSAR models for the design of new antitubercular compounds.
Ventura C; Latino DA; Martins F
Eur J Med Chem; 2013; 70():831-45. PubMed ID: 24246731
[TBL] [Abstract][Full Text] [Related]
8. Combinatorial QSAR modeling of P-glycoprotein substrates.
de Cerqueira Lima P; Golbraikh A; Oloff S; Xiao Y; Tropsha A
J Chem Inf Model; 2006; 46(3):1245-54. PubMed ID: 16711744
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. QSAR models for the prediction of binding affinities to human serum albumin using the heuristic method and a support vector machine.
Xue CX; Zhang RS; Liu HX; Yao XJ; Liu MC; Hu ZD; Fan BT
J Chem Inf Comput Sci; 2004; 44(5):1693-700. PubMed ID: 15446828
[TBL] [Abstract][Full Text] [Related]
11. Modeling drug albumin binding affinity with e-state topological structure representation.
Hall LM; Hall LH; Kier LB
J Chem Inf Comput Sci; 2003; 43(6):2120-8. PubMed ID: 14632464
[TBL] [Abstract][Full Text] [Related]
12. Predictive QSAR modeling of HIV reverse transcriptase inhibitor TIBO derivatives.
Mandal AS; Roy K
Eur J Med Chem; 2009 Apr; 44(4):1509-24. PubMed ID: 18760864
[TBL] [Abstract][Full Text] [Related]
13. Benchmarking of linear and nonlinear approaches for quantitative structure-property relationship studies of metal complexation with ionophores.
Tetko IV; Solov'ev VP; Antonov AV; Yao X; Doucet JP; Fan B; Hoonakker F; Fourches D; Jost P; Lachiche N; Varnek A
J Chem Inf Model; 2006; 46(2):808-19. PubMed ID: 16563012
[TBL] [Abstract][Full Text] [Related]
14. Prediction of intrinsic solubility of generic drugs using MLR, ANN and SVM analyses.
Louis B; Agrawal VK; Khadikar PV
Eur J Med Chem; 2010 Sep; 45(9):4018-25. PubMed ID: 20584562
[TBL] [Abstract][Full Text] [Related]
15. QSAR modeling of mono- and bis-quaternary ammonium salts that act as antagonists at neuronal nicotinic acetylcholine receptors mediating dopamine release.
Zheng F; Bayram E; Sumithran SP; Ayers JT; Zhan CG; Schmitt JD; Dwoskin LP; Crooks PA
Bioorg Med Chem; 2006 May; 14(9):3017-37. PubMed ID: 16431111
[TBL] [Abstract][Full Text] [Related]
16. ANN-QSAR model of drug-binding to human serum albumin.
Deeb O; Hemmateenejad B
Chem Biol Drug Des; 2007 Jul; 70(1):19-29. PubMed ID: 17630991
[TBL] [Abstract][Full Text] [Related]
17. Quantitative predictions of gas chromatography retention indexes with support vector machines, radial basis neural networks and multiple linear regression.
Chen HF
Anal Chim Acta; 2008 Feb; 609(1):24-36. PubMed ID: 18243870
[TBL] [Abstract][Full Text] [Related]
18. Modeling of p38 mitogen-activated protein kinase inhibitors using the Catalyst HypoGen and k-nearest neighbor QSAR methods.
Xiao Z; Varma S; Xiao YD; Tropsha A
J Mol Graph Model; 2004 Oct; 23(2):129-38. PubMed ID: 15363455
[TBL] [Abstract][Full Text] [Related]
19. QSPR study of Setschenow constants of organic compounds using MLR, ANN, and SVM analyses.
Xu J; Wang L; Wang L; Shen X; Xu W
J Comput Chem; 2011 Nov; 32(15):3241-52. PubMed ID: 21837634
[TBL] [Abstract][Full Text] [Related]
20. Quantitative structure migration relationship modeling of migration factor for some benzene derivatives in micellar electrokinetic chromatography.
Fatemi MH; Shamseddin H; Malekzadeh H
J Sep Sci; 2009 Jun; 32(11):1934-40. PubMed ID: 19425021
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]