Calculation of Hildebrand Solubility Parameters of Some Polymers Using QSPR Methods Based on LS-SVM Technique and Theoretical Molecular Descriptors

Nasser Goudarzi; M. Arab Chamjangali; A.H. Amin

doi:10.1007/s10118-014-1423-z

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Calculation of Hildebrand Solubility Parameters of Some Polymers Using QSPR Methods Based on LS-SVM Technique and Theoretical Molecular Descriptors

PAPER | Updated：2021-06-21

- Calculation of Hildebrand Solubility Parameters of Some Polymers Using QSPR Methods Based on LS-SVM Technique and Theoretical Molecular Descriptors
- Chinese Journal of Polymer Science Vol. 32, Issue 5, Pages: 587-594(2014)
- Affiliations：
  
  Faculty of Chemistry, Shahrood University of Technology, Box 316, Shahrood, Iran
- Author bio：
  
  *Nasser Goudarzi, E-mail: goudarzi@shahroodut.ac.ir
- Funds：
- DOI：10.1007/s10118-014-1423-z
  CLC：
- Received：15 July 2013，
  
  Revised：2013-11-19，
  
  Accepted：24 November 2013，
  
  Published：2014-05
- Accepted：
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Nasser Goudarzi, M. Arab Chamjangali, A.H. Amin. Calculation of Hildebrand Solubility Parameters of Some Polymers Using QSPR Methods Based on LS-SVM Technique and Theoretical Molecular Descriptors[J]. Chinese Journal of Polymer Science, 2014, 32(5): 587-594.
DOI：

Nasser Goudarzi, M. Arab Chamjangali, A.H. Amin. Calculation of Hildebrand Solubility Parameters of Some Polymers Using QSPR Methods Based on LS-SVM Technique and Theoretical Molecular Descriptors[J]. Chinese Journal of Polymer Science, 2014, 32(5): 587-594. DOI： 10.1007/s10118-014-1423-z.

摘要

Abstract

In this work

some chemometrics methods are applied for the modeling and prediction of the Hildebrand solubility parameter of some polymers. A genetic algorithm (GA) method is designed for the selection of variables to construct two models using the multiple linear regression (MLR) and least square-support vector machine (LS-SVM) methods in order to predict the Hildebrand solubility parameter. The MLR method is used to build a linear relationship between the molecular descriptors and the Hildebrand solubility parameter for these compounds. Then the LS-SVM method is utilized to construct the non-linear quantitative structure-activity relationship (QSAR) models. The results obtained using the LS-SVM method are then compared with those obtained for the MLR method; it was revealed that the LS-SVM model was much better than the MLR one. The root-mean-square errors of the training set and the test set for the LS-SVM model were 0.2912 and 0.2427

and the correlation coefficients were 0.9662 and 0.9518

respectively. This paper provides a new and effective method for predicting the Hildebrand solubility parameter for some polymers

and also reveals that the LS-SVM method can be used as a powerful chemometrics tool for the quantitative structure-property relationship (QSPR) studies.

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