Identification of Novel CDK2 Inhibitors by QSAR and Virtual Screening Procedures

Abstract

AbstractQuantitative Structure–Activity Relationship (QSAR) studies were carried out on a set of 46 imidazo[1,2‐a]pyridines, imidazo[1,2‐b]pyridazines and 2,4‐bis anilino pyrimidines, and nitroso‐6‐aminopyrimidine and 2,6‐diaminopyrimidine inhibitors of CDK2 (Cyclin‐dependent Kinase2) using a multiple regression procedure. The activity contributions of these compounds were determined from regression equation and the validation procedures such as external set cross‐validation r2, (R2cv,ext) and the regression of observed activities against predicted activities and vice versa for validation set were described to analyze the predictive ability of the QSAR model. An accurate and reliable QSAR model involving five descriptors was chosen based on the FIT Kubinyi function which defines the statistical quality of the model. The proposed model due to its high predictive ability was utilized to screen similar repertoire of compounds reported in the literature, and the biological activities are estimated. The screening study clearly demonstrated that the strategy presented shall be used as an alternative to the time‐consuming experiments as the model tolerated a variety of structural modifications signifying its potential for drug design studies.