Predictions of chromatographic retention indices of alkylphenols with support vector machines and multiple linear regression.
In this study, quantitative structure-retention relationship (QSRR) was used for the prediction of Kováts retention indices of 180 alkylphenols and their derivatives using the multiple linear regression (MLR) and support vector machine (SVM). After the calculation of some molecular descriptors for all molecules, the data set was randomly divided into training and test sets. The diversity of training and test sets was examined by molecular diversity validation test. Then stepwise MLR was used for the selection of the most important descriptors and development of MLR models. Descriptors which appeared in these QSRR models are number of H atoms, relative number of O atoms, Balaban index, relation yz-shadow/yz-rectangle and partial charges hydrogen bond donor atoms HDCA(2) index. These descriptors were used as inputs for developing the SVM model. After optimizing the SVM parameters, it was used for the calculation of chromatographic retention of interest molecules. The values of SE in calculation of Kováts retention indices for training and test sets are 0.34 and 0.63, respectively, for MLR model and 0.35 and 0.63, respectively, for SVM model. The overall values of average absolute relative error were 13.24 and 13.83 for MLR and SVM models, respectively. In addition, the cross-validation tests were performed to further examine the obtained model. The calculated values of cross-validation correlation coefficient (Q(2)) and standard deviation based on predicted residual sum of square are 0.896 and 0.680 for MLR model and 0.893 and 0.67 for SVM model. These values and other obtained statistical parameters for these models reveal the suitability of QSRR in prediction of Kováts retention indices of alkylphenols using MLR and SVM techniques.
Fatemi
Mohammed Hossein
MH
Department of Chemistry, University of Mazandaran, Babolsar, Iran. mhfatemi@umz.ac.ir
Baher
Elham
E
Ghorbanzade'h
Mehdi
M