a Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University , Nanjing , China.
SAR QSAR Environ Res. 2013 Oct;24(10):795-817. doi: 10.1080/1062936X.2013.815655. Epub 2013 Aug 13.
Cyclin-dependent kinase 2 (CDK2) has been identified as an important target for developing novel anticancer agents. Molecular docking, three-dimensional quantitative structure-activity relationship (3D-QSAR) and pharmacophore modelling were combined with the ultimate goal of studying the structure-activity relationship of CDK2 inhibitors. The comparative molecular similarity indices analysis (CoMSIA) model constructed based on a set of 3-aminopyrazole derivatives as CDK2 inhibitors gave statistically significant results (q (2) = 0.700; r (2) = 0.982). A HypoGen pharmacophore model, constructed using diverse CDK2 inhibitors, also showed significant statistics ([Formula: see text]Cost = 61.483; RMSD = 0.53; Correlation coefficient = 0.98). The small residues and error values between the estimated and experimental activities of the training and test set compounds proved their strong capability of activity prediction. The structural insights obtained from these two models were consistent with each other. The pharmacophore model summarized the important pharmacophoric features required for protein-ligand binding. The 3D contour maps in combination with the comprehensive pharmacophoric features helped to better interpret the structure-activity relationship. The results will be beneficial for the discovery and design of novel CDK2 inhibitors. The simplicity of this approach provides expansion to its applicability in optimizing other classes of small molecular CDK2 inhibitors.
细胞周期蛋白依赖性激酶 2(CDK2)已被确定为开发新型抗癌药物的重要靶标。本文采用分子对接、三维定量构效关系(3D-QSAR)和药效团模型相结合的方法,旨在研究 CDK2 抑制剂的构效关系。基于一组 3-氨基吡唑衍生物作为 CDK2 抑制剂构建的比较分子相似性指数分析(CoMSIA)模型,得出了统计学意义显著的结果(q 2 = 0.700;r 2 = 0.982)。基于多种 CDK2 抑制剂构建的 HypoGen 药效团模型也表现出显著的统计学意义([Formula: see text]Cost = 61.483;RMSD = 0.53;Correlation coefficient = 0.98)。训练集和测试集化合物的预测活性与实验活性之间的小残基和误差值表明了该模型具有较强的活性预测能力。这两个模型得到的结构见解是一致的。药效团模型总结了与蛋白-配体结合相关的重要药效特征。结合综合药效特征的三维等高线图有助于更好地解释构效关系。这些结果将有助于发现和设计新型 CDK2 抑制剂。该方法的简单性为其在优化其他类型小分子 CDK2 抑制剂方面的应用提供了扩展。