Inhibitory Activity Of 4-Anilinoquinazoline And Derivatives Against Egfr Kinase Using DFT-QSAR Analysis

A. Idrissi Taourati, M. Ghamali, S. Chtita, H. Zaki, F. Guenoun, T. Lakhlifi, M. Bouachrine

Abstract


The goal of this research is to promote the growth of predictive QSAR models of 4-anilinoquinazoline and derivatives against EGFR kinase using several statistical techniques especially principal components analysis (PCA), multiple linear regression (MLR) and nonlinear regression (RNLM) calculations. We choose the validation method to examine the execution and the equilibrium of this model. We accordingly propose a quantitative model (non-linear and linear QSAR models), and we interpret the activity of the compounds relying on the multivariate statistical analysis. The topological descriptors were computed, respectively, with ACD/ChemSketch and MarvinSketch programs. The electronic descriptors were computed with Gaussian 09. This study demonstrates that multiple regression and multiple non-linear regression analyses predict well the studied activity. Statistically speaking, a very important outcome was given by this model and a good stability to data variation in leave-one-out cross-validation was shown.

Keywords


QSAR ; DFT ; 4-Anilinoquinazoline ; MLR ; MNLR

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Online ISSN: 2605-6895

Authors attest that their work is original and unpublished, and is not under consideration for publication elsewhere. In addition, authors confirm that their paper has not been copied or plagiarized, in whole or in part, from other works.