Background: Quantitative structure–activity relationship (QSAR) models could provide
both statistical significance and useful chemical insights for drug design. The QSAR method has
found applications for predicting diverse properties of organic compounds, including antiviral
activities, toxicities and biological activities. In this work, a quantitative structure-activity relationship
was utilized for the prediction of allosteric BRAF (V600E) inhibitory activities.
Methods: A data set which contains 54 molecules was classified into training and test sets. Stepwise
(SW) and genetic algorithm (GA) methods were employed for feature selection. The models were
validated using the cross-validation and external test set.
Results: Results showed that the GA approach is a more powerful technique than SW for the selection
of suitable descriptors. The squared cross-validated correlation coefficient for leave-one-out of 0.702
and squared correlation coefficient of 0.793 was obtained for the training set compounds by GA–MLR
Conclusion: The obtained GA–MLR model could be applied as a worthwhile model for designing
similar groups of the mentioned inhibitors.