A novel QSAR model for prediction of apoptosis-inducing activity of 4-aryl-4-H-chromenes based on support vector machine

Abstract

In this work some chemometrics methods were applied for modeling and prediction of the induction of apoptosis by 4-aryl-4-H-chromenes with descriptors calculated from the molecular structure alone. The genetic algorithm (GA) and stepwise multiple linear regression methods were used to select descriptors which are responsible for the apoptosis-inducing activity of these compounds. Then support vector machine (SVM), artificial neural network (ANN), and multiple linear regression (MLR) were utilized to construct the nonlinear and linear quantitative structure–activity relationship models. The obtained results using SVM were compared with ANN and MLR; it revealed that the GA–SVM model was much better than other models. The root-mean-square errors of the training set and the test set for GA–SVM model are 0.181, 0.241 and the correlation coefficients were 0.950, 0.924, respectively, and the obtained statistical parameters of cross validation test on GA–SVM model were Q 2 = 0.71 and SRESS = 0.345 which revealed the reliability of this model. The results were also compared with previous published model and indicate the superiority of the present GA–SVM model.