Classification of central nervous system agents by least squares support vector machine based on their structural descriptors: A comparative study

Abstract

Linear and quadratic discriminant analysis and least squares support vector machine (LS-SVM) were used to classify a data set of 326 central nervous system (CNS) drugs as active or inactive CNS agents according to their permeation into the blood–brain barrier. A pool of descriptors was calculated by DRAGON software and nine of them were selected based on Wilk's lambda and classification accuracy and used for classification of drugs in data set. The classification models were validated based on accuracy, sensitivity, specificity, Matthew's correlation coefficient and Cohen's kappa values. The developed LS-SVM model, as the superior model has the accuracy of 96.5% and 96.0%, Matthew's correlation coefficient of 0.930 and 0.920, Cohen's kappa value of 0.963 and 0.917, and area under recursive operating characteristic curve of 0.95 and 0.98 for training and test sets, respectively. The results of this study indicated the applicability of LS-SVM in classification of CNS drugs based on their structural descriptors.