Identifying the Novel Pyrimidine-Based CDK2 Inhibitors as Anticancer Agents Using Text-Mining and Combined Molecular Modeling Approaches

Abstract

The cycline-dependent kinase (CDK) protein is a vital target used in anti-cancer drugdesigning studies. Many FDA-approved drugs, including anti-cancer drugs, use pyrimidine as acrucial fragment. In the current study, a small molecule database (Specs SC) that text miningstudies include more than 210.000 compounds, and we filtered 6668 molecules that carry“pyrimidine” fragments. We then screened these compounds at the binding pocket of CDK-2 targetusing molecular docking and molecular dynamics (MD) simulations approaches. Besides, wecompared the binding free energies of screened compounds with pyrimidine-based FDA-approvedanti-cancer drug Abemaciclib which targets CDK. Based on the comparison of docking scores ofscreened compounds, we used top-7 hits in 100 ns MD simulations. We also applied the same MDsimulations protocol (100 ns) to the Abemaciclib-bound CDK-2 complex structure. We thencalculated the average Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) energies. OurMM/GBSA results showed that 6 out of 7 compounds have better MM/GBSA scores than FDAapproved compound Abemaciclib. Thus, together with a combination of text mining and integratedmolecular modeling approaches, we identified novel pyrimidine-based hits against CDK.