QSAR study of ACK1 inhibitors by genetic algorithm-multiple linear regression (GA-MLR)

In this work, a quantitative structure-activity relationship (QSAR) model was used to predict the ACK1 inhibitory activities. A data set of 37 compounds with known ACK1 inhibitory activities was used. The data set was divided into two subsets of training and test sets, based on hierarchical clustering technique. Genetic algorithm was applied to select the respective variables to build the model in the next step. Multiple linear regressions (MLR) were employed to give the QSAR model. The squared cross-validated correlation coefficient for leave-one-out (Q(LOO)(2)) of 0.712 and squared correlation coefficient (R-train(2)) of 0.806 were obtained for the training set compounds by GA-MLR model. The given model performed a good stability and predictability when it was verified by internal and external validation. The predicted results from this study can lead to design of better and potent ACK1 inhibitors. (C) 2014 King Saud University. Production and hosting by Elsevier B.V. All rights reserved.