189 related articles for article (PubMed ID: 27477267)
1. A Novel QSPR Model for Prediction of Gas to Dimethyl Sulfoxide Solvation Enthalpy of Organic Compounds Based on Support Vector Machine.
Golmohammadi H; Dashtbozorgi Z; Acree WE
Mol Inform; 2012 May; 31(5):385-97. PubMed ID: 27477267
[TBL] [Abstract][Full Text] [Related]
2. Prediction of Bovine Serum Albumin-Water Partition Coefficients of a Wide Variety of Neutral Organic Compounds by Means of Support Vector Machine.
Golmohammadi H; Dashtbozorgi Z; Acree WE
Mol Inform; 2012 Dec; 31(11-12):867-78. PubMed ID: 27476740
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Support vector machines-based quantitative structure-property relationship for the prediction of heat capacity.
Xue CX; Zhang RS; Liu HX; Liu MC; Hu ZD; Fan BT
J Chem Inf Comput Sci; 2004; 44(4):1267-74. PubMed ID: 15272834
[TBL] [Abstract][Full Text] [Related]
5. Support vector machine regression (LS-SVM)--an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?
Balabin RM; Lomakina EI
Phys Chem Chem Phys; 2011 Jun; 13(24):11710-8. PubMed ID: 21594265
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. Quantitative structure-property relationship modelling of the degradability rate constant of alkenes by OH radicals in atmosphere.
Fatemi MH; Baher E
SAR QSAR Environ Res; 2009; 20(1-2):77-90. PubMed ID: 19343584
[TBL] [Abstract][Full Text] [Related]
9. QSPR studies for predicting polarity parameter of organic compounds in methanol using support vector machine and enhanced replacement method.
Golmohammadi H; Dashtbozorgi Z
SAR QSAR Environ Res; 2016 Dec; 27(12):977-997. PubMed ID: 27658742
[TBL] [Abstract][Full Text] [Related]
10. Predicting the auto-ignition temperatures of organic compounds from molecular structure using support vector machine.
Pan Y; Jiang J; Wang R; Cao H; Cui Y
J Hazard Mater; 2009 May; 164(2-3):1242-9. PubMed ID: 18952371
[TBL] [Abstract][Full Text] [Related]
11. An accurate QSPR study of O-H bond dissociation energy in substituted phenols based on support vector machines.
Xue CX; Zhang RS; Liu HX; Yao XJ; Liu MC; Hu ZD; Fan BT
J Chem Inf Comput Sci; 2004; 44(2):669-77. PubMed ID: 15032549
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Exploring QSARs of the interaction of flavonoids with GABA (A) receptor using MLR, ANN and SVM techniques.
Deeb O; Shaik B; Agrawal VK
J Enzyme Inhib Med Chem; 2014 Oct; 29(5):670-6. PubMed ID: 24102524
[TBL] [Abstract][Full Text] [Related]
14. QSPR studies of impact sensitivity of nitro energetic compounds using three-dimensional descriptors.
Xu J; Zhu L; Fang D; Wang L; Xiao S; Liu L; Xu W
J Mol Graph Model; 2012 Jun; 36():10-9. PubMed ID: 22503858
[TBL] [Abstract][Full Text] [Related]
15. [Research on QSPR for n-octanol-water partition coefficients of organic compounds based on genetic algorithms-support vector machine and genetic algorithms-radial basis function neural networks].
Qi J; Niu JF; Wang LL
Huan Jing Ke Xue; 2008 Jan; 29(1):212-8. PubMed ID: 18441943
[TBL] [Abstract][Full Text] [Related]
16. Prediction of human volume of distribution values for drugs using linear and nonlinear quantitative structure pharmacokinetic relationship models.
Louis B; Agrawal VK
Interdiscip Sci; 2014 Mar; 6(1):71-83. PubMed ID: 24464707
[TBL] [Abstract][Full Text] [Related]
17. A quantitative prediction of the viscosity of ionic liquids using S(σ-profile) molecular descriptors.
Zhao Y; Huang Y; Zhang X; Zhang S
Phys Chem Chem Phys; 2015 Feb; 17(5):3761-7. PubMed ID: 25557501
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. A novel QSPR model for prediction of lower flammability limits of organic compounds based on support vector machine.
Pan Y; Jiang J; Wang R; Cao H; Cui Y
J Hazard Mater; 2009 Sep; 168(2-3):962-9. PubMed ID: 19329246
[TBL] [Abstract][Full Text] [Related]
20. Study on adsorption behavior of volatile and semivolatile organic vapors to air-dry soils based on QSPR methods.
Liu H; Yao X; Liu M; Hu Z; Fan B
Environ Pollut; 2007 May; 147(1):41-9. PubMed ID: 17240022
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
