QSAR study of antiproliferative drug against A549 by GA-MLR and SW-MLR methods

Document Type : Research Article


1 Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

2 Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran

3 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.

Graphical Abstract

QSAR study of antiproliferative drug against A549 by GA-MLR and SW-MLR methods


[1] J.B. Vieira, F.S. Braga, C.C. Lobato, C.F. Santos, J.S. Costa, J.A.H. Bittencourt, D.S. Brasil, J.O. Silva, L.I. Hage-Melim, W.J.C. Macêdo, A QSAR, Pharmacokinetic and Toxicological Study of New Artemisinin Compounds with Anticancer Activity, Molecules., 19 (2014) 10670-10697.
[2] M.H. Bohari, H.K. Srivastava, G.N. Sastry, Analogue-based approaches in anti-cancer compound modelling: the relevance of QSAR models, Org. Med. Chem. Lett., 1 (2011) 1-12.
[3] J. Yan, Y. Pang, J. Sheng, Y. Wang, J. Chen, J. Hu, L. Huang, X. Li, A novel synthetic compound exerts effective anti-tumour activity in vivo via the inhibition of tubulin polymerisation in A549 cells, Biochem. Pharmacol., 97 (2015) 51-61.
[4] G.M. Cragg, P.G. Grothaus, D.J. Newman, Impact of Natural Products on Developing New Anti-Cancer Agents†, Chem. Revi., 109 (2009) 3012-3043.
[5] W. Liao, G. Hu, Z. Guo, D. Sun, L. Zhang, Y. Bu, Y. Li, Y. Liu, P. Gong, Design and biological evaluation of novel 4-(2-fluorophenoxy) quinoline derivatives bearing an imidazolone moiety as c-Met kinase inhibitors, Bioorg. Med. Chem., 23 (2015) 4410-4422.
[6] T. Fujita, QSAR and drug design, Elsevier, 1995.
[7] J.C. Madden, M.T. Cronin, Structure-based methods for the prediction of drug metabolism, (2006).
[8] E. Pourbasheer, R. Aalizadeh, M.R. Ganjali, P. Norouzi, J. Shadmanesh, QSAR study of ACK1 inhibitors by genetic algorithm–multiple linear regression (GA–MLR), J. Saudi. Chem. Soc., 18 (2014) 681-688.
[9] W. Li, Y. Tang, Y.-L. Zheng, Z.-B. Qiu, Molecular modeling and 3D-QSAR studies of indolomorphinan derivatives as kappa opioid antagonists, Bioorg. Med. Chem., 14 (2006) 601-610.
[10] A. Habibi-Yangjeh, E. Pourbasheer, M. Danandeh-Jenagharad, Prediction of basicity constants of various pyridines in aqueous solution using a principal component-genetic algorithm-artificial neural network, Monatsh. Chem., 139 (2008) 1423-1431.
[11] A. Habibi-Yangjeh, E. Pourbasheer, M. Danandeh-Jenagharad, Application of principal component-genetic algorithm-artificial neural network for prediction acidity constant of various nitrogen-containing compounds in water, Monatsh. Chem., 140 (2009) 15-27.
[12] R.R. Hocking, A Biometrics invited paper. The analysis and selection of variables in linear regression, Biometrics., 32 (1976) 1-49.
[13] Q. Shen, Q.-Z. Lü, J.-H. Jiang, G.-L. Shen, R.-Q. Yu, Quantitative structure–activity relationships (QSAR): studies of inhibitors of tyrosine kinase, European journal of pharmaceutical sciences, 20 (2003) 63-71.
[14] E. Pourbasheer, S. Riahi, M.R. Ganjali, P. Norouzi, QSAR study on melanocortin-4 receptors by support vector machine, Eur. J. Med. Chem., 45 (2010) 1087-1093.
[15] V. Vapnik, Statistical learning theory. 1998, in, Wiley, New York, 1998.
[16] H. Khajehsharifi, M. Sadeghi, E. Pourbasheer, Spectrophotometric simultaneous determination of ceratine, creatinine, and uric acid in real samples by orthogonal signal correction–partial least squares regression, Monatsh. Chem., 140 (2009) 685-691.
[17] P. Gramatica, P. Pilutti, E. Papa, Validated QSAR prediction of OH tropospheric degradation of VOCs: splitting into training-test sets and consensus modeling, J. Chem. Inf. Comput. Sci., 44 (2004) 1794-1802.
[18] R. Todeschini, V. Consonni, A. Mauri, M. Pavan, DRAGON-Software for the calculation of molecular descriptors, Web version, 3 (2003).
[19] R. Leardi, R. Boggia, M. Terrile, Genetic algorithms as a strategy for feature selection, J. Chemom., 6 (1992) 267-281.
[20] I. MathWorks, Genetic Algorithm and Direct Search Toolbox for Use with MATLAB: User’s Guide, in, MathWorks, 2005.
[21] P. Pargolghasemi, M.S. Hoseininezhad-Namin, A. Parchehbaf Jadid, Prediction of Activities of BRAF (V600E) Inhibitors by SW-MLR and GA-MLR Methods, Curr. Comput. Aided. Drug. Des., 13 (2017) 249-261.