Exploring QSARs of Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) Tyrosine Kinase Inhibitors by MLR, PLS and PC-ANN

Omar      Deeb; Sana      Jawabreh; Mohammad      Goodarzi

Abstract

Quantitative structure-activity relationship study was performed to understand the inhibitory activity of a set of 192 vascular endothelial growth factor receptor-2 (VEGFR-2) compounds. QSAR models were developed using multiple linear regression (MLR) and partial least squares (PLS) as linear methods. While principal component - artificial neural networks (PC-ANN) modeling method with application of eigenvalue ranking factor selection procedure was used as nonlinear method. The results obtained offer good regression models having good prediction ability. The results obtained by MLR and PLS are close and better than those obtained by principal component- artificial neural network. The best model was obtained with a correlation coefficient of 0.87. The strength and the predictive performance of the proposed models was verified using both internal (cross-validation and Y-scrambling) and external statistical validations.

Keywords: Vascular endothelial growth factor receptor-2 (VEGFR-2), quantitative structure-activity relationship, Principal component artificial neural network (PC-ANN), Multiple linear regression (MLR) and Partial least square (PLS), prediction ability, correlation coefficient, cross-validation, Y-scrambling