QSAR Studying of Oxidation Behavior of Benzoxazines as an Important Pharmaceutical Property.

In this work the electrooxidation half-wave potentials of some Benzoxazines were predicted from their structural molecular descriptors by using quantitative structure-property relationship (QSAR) approaches. The dataset consist the half-wave potential of 40 benzoxazine derivatives which were obtained by DC-polarography. Descriptors which were selected by stepwise multiple selection procedure are: HOMO energy, partial positive surface area, maximum valency of carbon atom, relative number of hydrogen atoms and maximum electrophilic reaction index for nitrogen atom. These descriptors were used for development of multiple linear regression (MLR) and artificial neural network (ANN) models. The statistical parameters of MLR model are standard errors of 0.016 and 0.018 for training and test sets, respectively. Also, these values are 0.012 and 0.017 for training and test sets of ANN model, respectively. The predictive power of these models was further examined by leave-eight-out cross validation procedure. The obtained statistical parameters are Q2 = 0.920 and SPRESS = 0.020 for MLR model and Q2 = 0.949 and SPRESS = 0.015 for ANN model, which reveals the superiority of ANN over MLR model. Moreover, the results of sensitivity analysis on ANN model indicate that the order of importance of descriptors is: Relative number of H atom > HOMO energy > Maximum electrophyl reaction index for N atom > Partial positive surface area (order-3) > maximum valency of C atom.

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.

A novel quantitative structure-activity relationship model for prediction of biomagnification factor of some organochlorine pollutants.

The biomagnification factor (BMF) is an important property for toxicology and environmental chemistry. In this work, quantitative structure-activity relationship (QSAR) models were used for the prediction of BMF for a data set including 30 polychlorinated biphenyls and 12 organochlorine pollutants. This set was divided into training and prediction sets. The result of diversity test reveals that the structure of the training and test sets can represent those of the whole ones. After calculation and screening of a large number of molecular descriptors, the methods of stepwise multiple linear regression and genetic algorithm (GA) were used for the selection of most important and significant descriptors which were related to BMF. Then multiple linear regression and artificial neural network (ANN) techniques were applied as linear and non-linear feature mapping techniques, respectively. By comparison between statistical parameters of these methods it was concluded that an ANN model, which used GA selected descriptors, was superior over constructed models. Descriptors which were used by this model are: topographic electronic index, complementary information content, XY shadow/XY rectangle and difference between partial positively and negatively charge surface area. The standard errors for training and test sets of this model are 0.03 and 0.20, respectively. The degree of importance of each descriptor was evaluated by sensitivity analysis approach for the nonlinear model. A good results (Q (2) = 0.97 and SPRESS = 0.084) is obtained by applying cross-validation test that indicating the validation of descriptors in the obtained model in prediction of BMF for these compounds.