Catastrophic Collision Between Obesity and COVID-19 Have Evoked the Computational Chemistry for Research in Silico Design of New CaMKKII Inhibitors Against Obesity by Using 3D-QSAR, Molecular Docking, and ADMET

Abstract

The purpose of the paper is to discuss the various methods and computational approaches, which are used in computer-aided drug design. For this reason, pyrimidine and azaindole derivatives have been used to study the inhibitory activity of CaMKKII. It is an enzyme that enters the brain to greatly reduce food from regulating the production of Ghrelin that is synthesized by the stomach and acts on the hypothalamus. The obtained results from different techniques such as the 3D-QSAR, molecular docking, and ADMET were applied to study series of new CaMKKII inhibitors of 23 molecules based on pyrimidine and azaindole derivatives. The CoMFA and CoMSIA models were used in 19 molecules in the training set that give high values of determination coefficient R-2 0.970 and 0.902 respectively, and significant values of Leave-One-Out cross-validation coefficient Q(2) 0.614 and 0.583 respectively. The predictive capacity of this model was examined by external validation though using a test set of four compounds with a predicted determination coefficient test R-ext(2) of 0.778 and 0.972 successively. The method of alignment adapted with the appropriate parameters gave credible models. The CoMFA and CoMSIA models produce the contour maps which were used to define a 3D-QSAR mode.