Discovery of CDK2 Inhibitors Based on Machine Learning and Molecular Dynamics Simulations

文献类型： 外文期刊

第一作者： Tan, Yingjia

作者： Tan, Yingjia;Liu, Yulin;Zhao, Xi;Chen, Liang;Na, Risong

作者机构：

关键词： Cyclin-dependent kinase 2(CDK2) inhibitor; Machine learning; Molecular dynamics; Binding free energy

期刊名称：CHEMICAL JOURNAL OF CHINESE UNIVERSITIES-CHINESE （ 影响因子：0.6； 五年影响因子：0.5 ）

ISSN： 0251-0790

年卷期： 2025 年 46 卷 3 期

页码：

收录情况： SCI

摘要： Four potential cyclin-dependent kinase 2(CDK2) inhibitors were discovered through machine learning and molecular dynamics simulation methods. First, a classification model for CDK2 inhibitors was established using existing large-scale activity databases and machine learning algorithms. The extreme gradient boosting(XGBoost) model with extended-connectivity fingerprints(ECFP6) was used to screen the Enamine database, identifying 1152 novel compounds. These potential compounds were then ranked based on their affinity for CDK2 using molecular docking and scoring functions. The compounds were clustered into four categories using fingerprint clustering methods, and one compound with a high docking score was selected from each category. Subsequently, the four selected compounds underwent drug-likeness analysis and molecular dynamics simulations. The four potential CDK2 inhibitors(Z1766368563, Z363564868, Z1891240670 and Z2701273053) demonstrated good drug-likeness properties and high binding free energy in molecular dynamics simulation results. The findings suggest that these four compounds can serve as lead compounds for subsequent modification and optimization as CDK2 inhibitors.

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