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

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), artifi. cial 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. (c) 2007 Elsevier Ltd. All rights reserved.