IngentaConnect Prediction of the logarithmic of partition coefficients (log P) o...

Authors: Goudarzi, Nasser¹; Goodarzi, Mohammad²

Source: Molecular Physics, Volume 106, Numbers 21-23, November 2008 , pp. 2525-2535(11)

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.

Keywords: least square-support vector machine; support vector regression; artificial neural network; quantitative structure-property relationship; MLR

Document Type: Research article

DOI: 10.1080/00268970802577834

Affiliations: 1: Faculty of Chemistry, Shahrood University of Technology, Shahrood, Iran 2: Faculty of Sciences, Department of Chemistry, Azad University, Arak, Iran Yong Researchers Club, Azad University, Arak, Iran

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