Aim/Background: Quantitative structure activity relationship studies are important ligandbased methods used in drug discovery process. The development of Quantum mechanics and its application in the study of smaller and macromolecules have accelerated their applications in the field of drug discovery. Extensive developments in QSAR studies (either 2D or 3D) could be the result of progressive applications of Quantum mechanics. Latest drug discovery modules depends solely on the advanced computational methods such as 3D QSAR and molecular docking approach. It is believed that a few drug discovery groups from industries still depend on the 2-dimensional QSAR techniques as a part of the lead optimization. Our study aims on learning the application of five important binary fingerprinting techniques based on the quantum mechanics involving 2D QSAR studies for designing novel pyrazolo quinazolines as selective inhibitors of CDK2/CyclinA. Materials and Methods: A dataset of 47 analogues of pyrazolo quinazolines were selected with their inhibitory activity on CDK2/Cyclin A. The derivatives were divided into training and test sets. The Kernel based partial regression was run using five important binary fingerprints and statistical significance of each fingerprint was analysed. Results: Out of the five fingerprints selected, the fingerprint linear arrived at the optimized 2D QSAR model through the kernel-based regression analysis. The final developed model expressed the importance of the presence of Carboxamide groups on the pyrazole ring that could positively contributes to the inhibitory activity. Conclusion: The developed model could be of use to design better analogues with enhanced selectivity and specificity as inhibitors of CDK2/Cyclin A that would provide a clear insight amongst the researchers for the development of novel and potent clinically useful anticancer agents.