Prediction of the logarithmic of partition coefficients (log P) of some organic compounds by least square-support vector machine (LS-SVM)

Nasser Goudarzi, Mohammad Goodarzi

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

A new method least square-support vector machine (LS-SVM) was used to develop quantitative structure-property relationship (QSPR) models for predicting the logarithmic of n-octanol/water partition coefficient (log P) of some derivatives phenolic compounds. The calibration and predictive ability of LS-SVM were investigated and compared with those of three other methods; multiple linear regression (MLR), support vector linear regression (SVR) and artificial neural network (ANN). The results showed that the log P values calculated by LS-SVM were in good agreement with experimental values, and the performances of the LS-SVM models were comparable or superior to those of MLR, SVR and ANN methods. The root-mean-square errors of the training set and the predicting set for the LS-SVM model were 0.0855, 0.0746 and the squares of the correlation coefficients were 0.9960 and 0.9728, respectively. These values and other statistical parameters obtained for the LS-SVM model show the reliability of this model. LS-SVM is a new and effective method for predicting log P of some organic compounds, and can be used as a powerful chemometrics tool for QSPR studies.

Original languageEnglish (US)
Pages (from-to)2525-2535
Number of pages11
JournalMolecular Physics
Volume106
Issue number21-23
DOIs
StatePublished - Nov 1 2008

Keywords

  • Artificial neural network
  • Least square-support vector machine
  • MLR
  • Quantitative structure-property relationship
  • Support vector regression

ASJC Scopus subject areas

  • Biophysics
  • Molecular Biology
  • Condensed Matter Physics
  • Physical and Theoretical Chemistry

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