Identification of potential extracellular signal-regulated protein kinase 2 inhibitors based on multiple virtual screening strategies

The integration of multiple virtual screening strategies facilitates the balance of computational efficiency and prediction accuracy. In this study, we constructed an efficient and reliable "multi-stage virtual screening-in vitro biological validation" system to identify potential inhibitors targeting extracellular signal-regulated protein kinase 2 (ERK2). Firstly, we rapidly obtained 10 candidate ERK2 inhibitors with desirable pharmacokinetic characteristics from thousands of named Natural Products in ZINC database based on machine learning classification models and ADME/T prediction. The structure-based molecular docking approach was then used to obtain four further hits with lower binding free energy compared to the positive control molecule Magnolipin. Subsequently, the two compounds were purchased for in vitro biological validation considering commercial availability and economic cost, and the results showed that Dodoviscin A exhibited acceptable inhibitory activity on ERK2 (IC₅₀ = 10.79 μm). Finally, the mechanism of action and binding stability of this natural product inhibitor were investigated by binding mode analysis and molecular dynamics simulation.

extracellular signal-regulated protein kinase 2; inhibitors; machine learning; natural products; virtual screening.