Quantitative 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 r 2 , (R 2 cv,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.
CDK2 (Cyclin Dependent Kinase 2) acts as a potential therapeutic target in cancer and several efforts have been made to find more specific, potent and selective ATP competitive CDK2 inhibitors. In this paper, we report a virtual screening approach that resulted in 54,558 Lipinski compliant hits from ZINC database based on the features exhibited by four compounds from our previous study. Docking and scoring of all compounds using GOLD (Genetic Optimisation for Ligand Docking) software, to evaluate the affinity of binding towards CDK2 enzyme 2UZO resulted in dock scores between 41.71 -82.33 kcal/mol. The resultant dataset of 392 hits were filtered based on the specificity between CDK2 and GSK-3 (Glycogen Synthase Kinase-3) to obtain 17 compounds that are more specific towards CDK2. Further, re-scoring of 17 best docked poses followed by a consensus scoring approach tested with five different scoring functions such as GOLD score, CHEM score implemented in GOLD 3.1, eHiTS_score (electronic High Throughput Screening), MolDock score of Molegro software and X-Score retrieved top hits. Finally, the top ten compounds were examined for anti-proliferative effects against human lung adenocarcinoma epithelial cell line, A549 using MTT assay.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.