462 related articles for article (PubMed ID: 18952371)
1. 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]
2. 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]
3. Prediction of auto-ignition temperatures of hydrocarbons by neural network based on atom-type electrotopological-state indices.
Pan Y; Jiang J; Wang R; Cao H; Zhao J
J Hazard Mater; 2008 Sep; 157(2-3):510-7. PubMed ID: 18280036
[TBL] [Abstract][Full Text] [Related]
4. The accurate QSPR models to predict the bioconcentration factors of nonionic organic compounds based on the heuristic method and support vector machine.
Liu H; Yao X; Zhang R; Liu M; Hu Z; Fan B
Chemosphere; 2006 May; 63(5):722-33. PubMed ID: 16226786
[TBL] [Abstract][Full Text] [Related]
5. Accurate quantitative structure-property relationship model to predict the solubility of C60 in various solvents based on a novel approach using a least-squares support vector machine.
Liu H; Yao X; Zhang R; Liu M; Hu Z; Fan B
J Phys Chem B; 2005 Nov; 109(43):20565-71. PubMed ID: 16853662
[TBL] [Abstract][Full Text] [Related]
6. Wavelet neural network modeling in QSPR for prediction of solubility of 25 anthraquinone dyes at different temperatures and pressures in supercritical carbon dioxide.
Tabaraki R; Khayamian T; Ensafi AA
J Mol Graph Model; 2006 Sep; 25(1):46-54. PubMed ID: 16337156
[TBL] [Abstract][Full Text] [Related]
7. Prediction of autoignition temperatures of organic compounds by the structural group contribution approach.
Chen CC; Liaw HJ; Kuo YY
J Hazard Mater; 2009 Mar; 162(2-3):746-62. PubMed ID: 18619731
[TBL] [Abstract][Full Text] [Related]
8. Prediction of the isoelectric point of an amino acid based on GA-PLS and SVMs.
Liu HX; Zhang RS; Yao XJ; Liu MC; Hu ZD; Fan BT
J Chem Inf Comput Sci; 2004; 44(1):161-7. PubMed ID: 14741023
[TBL] [Abstract][Full Text] [Related]
9. QSPR model of Henry's law constant for a diverse set of organic chemicals based on genetic algorithm-radial basis function network approach.
Modarresi H; Modarress H; Dearden JC
Chemosphere; 2007 Feb; 66(11):2067-76. PubMed ID: 17113627
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. Prediction of upper flammability limit percent of pure compounds from their molecular structures.
Gharagheizi F
J Hazard Mater; 2009 Aug; 167(1-3):507-10. PubMed ID: 19201088
[TBL] [Abstract][Full Text] [Related]
13. Prediction of impact sensitivity of nitro energetic compounds by neural network based on electrotopological-state indices.
Wang R; Jiang J; Pan Y; Cao H; Cui Y
J Hazard Mater; 2009 Jul; 166(1):155-86. PubMed ID: 19101083
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Quantitative structure-property relationship study of n-octanol-water partition coefficients of some of diverse drugs using multiple linear regression.
Ghasemi J; Saaidpour S
Anal Chim Acta; 2007 Dec; 604(2):99-106. PubMed ID: 17996529
[TBL] [Abstract][Full Text] [Related]
16. Development of binary classification of structural chromosome aberrations for a diverse set of organic compounds from molecular structure.
Serra JR; Thompson ED; Jurs PC
Chem Res Toxicol; 2003 Feb; 16(2):153-63. PubMed ID: 12588186
[TBL] [Abstract][Full Text] [Related]
17. Quantitative structure-property relationship studies for predicting flash points of alkanes using group bond contribution method with back-propagation neural network.
Pan Y; Jiang J; Wang Z
J Hazard Mater; 2007 Aug; 147(1-2):424-30. PubMed ID: 17292543
[TBL] [Abstract][Full Text] [Related]
18. Quantitative prediction of logk of peptides in high-performance liquid chromatography based on molecular descriptors by using the heuristic method and support vector machine.
Liu HX; Xue CX; Zhang RS; Yao XJ; Liu MC; Hu ZD; Fan BT
J Chem Inf Comput Sci; 2004; 44(6):1979-86. PubMed ID: 15554667
[TBL] [Abstract][Full Text] [Related]
19. Prediction of chemical carcinogenicity by machine learning approaches.
Tan NX; Rao HB; Li ZR; Li XY
SAR QSAR Environ Res; 2009; 20(1-2):27-75. PubMed ID: 19343583
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
