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.

中文翻译：

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基于多种虚拟筛选策略鉴定潜在的细胞外信号调节蛋白激酶 2 抑制剂

多种虚拟筛选策略的集成促进了计算效率和预测准确性的平衡。在本研究中，我们构建了一个高效可靠的“多阶段虚拟筛选-体外生物学验证”系统，以鉴定靶向细胞外信号调节蛋白激酶 2 (ERK2) 的潜在抑制剂。首先，我们基于机器学习分类模型和 ADME/T 预测，从 ZINC 数据库中的数千种命名天然产物中快速获得 10 种具有理想药代动力学特征的候选 ERK2 抑制剂。然后使用基于结构的分子对接方法获得与阳性对照分子 Magnolipin 相比具有较低结合自由能的四个进一步命中。随后，购买了这两种化合物体外考虑到商业可用性和经济成本的生物学验证，结果表明 Dodoviscin A 对 ERK2 表现出可接受的抑制活性（IC50= 10.79 微米）。最后，通过结合模式分析和分子动力学模拟研究了这种天然产物抑制剂的作用机制和结合稳定性。