Gravitational search algorithm: A new feature selection method for QSAR study of anticancer potency of imidazo[4,5-b]pyridine derivatives

Choosing the most suitable subset of descriptors among a large number of structural parameters is one of the most important and challenging steps in quantitative structure-activity relationship (QSAR) studies. So far, many feature selection algorithms have been applied in these studies, but none of them behave generally. In this study, a binary version of gravitational search algorithm (GSA) as a novel feature selection method is developed and coded for QSAR studies. The GSA is applied as a descriptor selection tool for anticancer potency modeling of a set of imidazo[4,5-b]pyridine derivatives consisting of 65 compounds. The GSA selected descriptors were subjected to Bayesian regularized artificial neural networks to model the anticancer potency. The generated model satisfactorily describes the experimental variation in the biological activity of the data set compounds. The results of external validation R-v(2) = 0.98) and internal cross-validation tests (Q(LOO)(2) = 0.94, R-L4O(2) = 0.93, R-L8O(2) = 0.92) in conjunction with Y-randomization confirm the predictive ability, robustness and effectiveness of the generated model. Also, comparison between GSA and genetic algorithm (GA) indicates that GSA has certain advantages over the GA. (C) 2013 Elsevier B.V. All rights reserved.