A Comparative QSAR study of aryl-substituted isobenzofuran-1(3H)-ones inhibitors

A comparative workflow, including linear and non-linear QSAR models, was carried out to evaluate the predictive accuracy of models and predict the inhibition activity of a series of aryl-substituted isobenzofuran-1(3H)-ones. The data set consisted of 34 compounds was classified into the training and test sets, randomly. Molecular descriptors were selected using the genetic algorithm (GA) as a feature selection tool. Various linear models based on multiple linear regression (MLR), principle component regression (PCR) and partial least square (PLS) and non-linear models based on artificial neural network (ANN), adaptive network-based fuzzy inference system (ANFIS) and support vector machine (SVM) methods were developed and compared. The accuracy of the models was studied by leave-one-out cross-validation (Q(LOO)(2)), Y-randomization test and group of compounds as external test set. Six descriptors were selected by GA to develop predictive models. With respect to the linear models, GA-PCR method was more accurate than the reset with statistical results of R-train = 0.883 R-test(2) = 0.897 R-adj,train(2) = 0. 829, R-adj,test(2) = 0.849, F-train = 24. 07 and F-test = 34.17. In case of non-linear models, GA-SVM (R-train(2) = 0. 992 and R-test(2) = 0.997) showed high predictive accuracy for the inhibitory activity. It was found that the selected descriptors have the major roles in interpretation of biological activities of the compounds.