Application of a new SPA-SVM coupling method for QSPR study of electrophoretic mobilities of some organic and inorganic compounds

Nasser Goudarzi, Mohammad Goodarzi, M. Arab Chamjangali, M. H. Fatemi

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

In this work, two chemometrics methods are applied for the modeling and prediction of electrophoretic mobilities of some organic and inorganic compounds. The successive projection algorithm, feature selection (SPA) strategy, is used as the descriptor selection and model development method. Then, the support vector machine (SVM) and multiple linear regression (MLR) model are utilized to construct the non-linear and linear quantitative structure-property relationship models. The results obtained using the SVM model are compared with those obtained using MLR reveal that the SVM model is of much better predictive value than the MLR one. The root-mean-square errors for the training set and the test set for the SVM model were 0.1911 and 0.2569, respectively, while by the MLR model, they were 0.4908 and 0.6494, respectively. The results show that the SVM model drastically enhances the ability of prediction in QSPR studies and is superior to the MLR model.

Original languageEnglish (US)
Pages (from-to)904-908
Number of pages5
JournalChinese Chemical Letters
Volume24
Issue number10
DOIs
StatePublished - Oct 1 2013

Keywords

  • Electrophoretic mobility
  • Multiple linear regression
  • Quantitative structure-mobility relationship
  • Successive projection algorithm
  • Support vector machine

ASJC Scopus subject areas

  • Chemistry(all)

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