QSAR study of CK2 inhibitors by GA-MLR and GA-SVM methods

Abstract

In this work, the quantitative structure–activity relationship models were developed for predicting activity of a series of compounds such as CK2 inhibitors using multiple linear regressions and support vector machine methods. The data set consisted of 48 compounds was divided into two subsets of training and test set, randomly. The most relevant molecular descriptors were selected using the genetic algorithm as a feature selection tool. The predictive ability of the models was evaluated using Y-randomization test, cross-validation and external test set. The genetic algorithm-multiple linear regression model with six selected molecular descriptors was obtained and showed high statistical parameters (R2train = 0.893, R2test = 0.921, Q2LOO = 0.844, F = 43.17, RMSE = 0.287). Comparison of the results between GA-MLR and GA-SVM demonstrates that GA-SVM provided better results for the training set compounds; however, the predictive quality for both models is acceptable. The results suggest that atomic mass and polarizabilities and also number of heteroatom in molecules are the main independent factors contributing to the CK2 inhibition activity. The predicted results of this study can be used to design new and potent CK2 inhibitors. Keywords: QSAR, Support vector machine, Genetic algorithm, Multiple linear regressions, CK2 inhibitors