Molecular modeling studies of Pyrazolopyrimidine Derivatives as potent Cyclin Dependent Kinase-2 inhibitors

Abstract

Abstract This study addresses the pressing need for innovative cancer treatments in the face of global challenges posed by the widespread occurrence of cancer and increasing treatment resistance. The study looks at cyclin-dependent kinase-2 (CDK2) and uses a methodical computer approach to find possible anticancer compounds with pyrazole and pyrimidine structures. (QSAR) quantitative structure-activity relationship has become crucial in lead optimization over the last three decades. A set of 45 pyrazolopyrimidine derivatives with known IC50 values were used to create and test models using QSARINS software. Model 4, with its high predictive performance (R2 = 0.9100, R2adj = 0.8900, LOF = 0.0394), emerges as the most reliable. The resulting QSAR model proves stable, predictive, and robust, effectively representing the original dataset. Active molecular descriptors are identified for predicting the structure-activity relationship. We used SAR analysis and model equation parameters to create sixty compounds and tested them for their predicted bioactivity using Model 4. These compounds are a series with pyrazolopyrimidine-fused piperidine and hybrid moieties, such as methanethione (20), ethenone (20), and benzamide (20). Among the designed series, 16 compounds exhibited pIC50 values exceeding 7, indicating that they were hit molecules represented as C1-C16. These obtained hit molecules undergo further screening with ADMET, molecular docking, and molecular dynamics simulations. C3 and C7, revealed in docking studies with low-energy conformations and sustained binding during simulations, consistently align their binding modes with the standard drug roscovitine. These compounds emerge as promising leads for targeting CDK2 in the development of groundbreaking cancer therapies.