Document Type: Research Article
Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
Department of Chemistry, Payame Noor University (PNU), P. O. Box, 19395-3697 Tehran, Iran
Quantitative structure-activity relationship (QSAR) is the most extensively used computational methodology for analogue-based design. In this research, QSAR model was used to predict antiproliferative properties of 4-(2-fluorophenoxy) quinoline derivatives against A549(human lung adenocarcinoma). For this purpose, we used the multiple linear regressions (MLR) for the construction of a model to predict drug activity and Stepwise (SW) and genetic algorithm (GA) methods used to build the model. The data were selected from 31 molecules with specific pharmacological activity. They were divided into two sets train and test data. The resulting model was tested using statistical methods such as external test set and cross-validation to ensure its authenticity. The results showed that GA-MLR approach had good predictive power and higher data rates compared with SW-MLR (Q2LOO = 0.877, R2Train =0.933). The results obtained in this study can be used to design drugs with higher performance and pharmacological activity to treat lung cancer.