Combined Computational Techniques for Discovery of Novel Pyrazolo[3,4‐ <i>d</i> ]pyrimidine Derivatives as PAK1 Inhibitors

Abstract P21‐activated kinase 1 (PAK1) is a protein kinase involved in various cancers, making it an attractive target for therapeutic intervention. This study employed a comprehensive computational approach, including pharmacophore modeling, three dimensional‐quantitative structure‐activity relationship (3D‐QSAR) analysis, virtual screening, and molecular dynamics (MD) simulations, to identify novel PAK1 inhibitors. A total of 46 pyrazolo[3,4‐ d ]pyrimidine derivatives were used as a dataset to generate pharmacophore and 3D‐QSAR models. The pharmacophore model DHRRR_1 exhibited the highest survival score of 5.80 and a site score of 0.92. The 3D‐QSAR analysis yielded robust models with high predictive power, including an atom‐based QSAR model with R 2 = 0.7209 and Q 2 = 0.6649 and a field‐based QSAR model with R 2 = 0.9072 and Q 2 = 0.8464. These models guided the screening of 40,000 novel derivatives through R‐group enumeration. The compounds 1a, 1b and 1c was screened as the potetial compounds through R‐group enumeration study. Additionally, compounds from the ZINC database showed strong docking results, such as ZINC93921464 , ZINC92210618 , ZINC40387740 , and ZINC90059146 . The novel compounds were compared with the original QSAR dataset and the ZINC‐screened compounds. MD simulations provided insights into the dynamic behavior of PAK1‐ligand complexes, emphasizing the importance of interactions with Leu347 and Arg299. The screened compounds of the study may be used for further development of novel compounds as anticancer agents against PAK1 kinase.