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 an important target used in anti-cancer drug designing studies. Pyrimidine is a crucial fragment which is used in many FDA-approved drugs including anti-cancer drugs. In the current study, a small molecule database (Specs SC) that includes more than 210.000 compounds were used in text mining studies and 6668 molecules that carry “pyrimidine” fragments were filtered. These compounds were then screened at the binding pocket of CDK-2 target using molecular docking and molecular dynamics (MD) simulations approaches. Binding free energies of screened compounds were also compared with pyrimidine-based FDA approved anti-cancer drug Abemaciclib which targets CDK. Based on comparison of docking scores of screened compounds, top-7 hits were used in 100 ns MD simulations. Same MD simulations protocol (100 ns) were also applied to Abemaciclib-bound CDK-2 complex structure. Average Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) energies were then calculated. Our MM/GBSA results showed that 6 out of 7 compounds have better MM/GBSA scores than FDA approved compound Abemaciclib. Thus, together with combination of text mining and integrated molecular modeling approaches, we identified novel pyrimidine-based hits against CDK.