Department of Chemistry, Lanzhou University, Lanzhou, China.
Chem Biol Drug Des. 2011 Apr;77(4):248-54. doi: 10.1111/j.1747-0285.2010.01068.x. Epub 2011 Jan 28.
The c-Jun N-terminal kinases are attractive targets because of their involvement in several diseases. In this work, a combined molecular modeling study for a set of isoquinolones as inhibitors of JNK1 was performed by molecular docking, genetic algorithm-multiple linear regression and comparative molecular field analysis to rationalize the structural requirements responsible for the inhibitory activity of these compounds. Molecular docking study was employed to explore the binding mode of the active compound at the active site of JNK1. Based on the docked conformations, highly predictive 2D, 3D quantitative structure-activity relationship models were developed. The best 2D quantitative structure-activity relationship model was established using genetic algorithm-multiple linear regression method containing four molecular descriptors. The best comparative molecular field analysis model was obtained with a cross-validated coefficient q(2) of 0.562, non-cross-validated r(2) values of 0.994. The information from quantitative structure-activity relationship models and molecular docking is useful for the design of novel JNK1 inhibitors with improved activities.
c-Jun N-末端激酶因其参与多种疾病而成为有吸引力的靶标。在这项工作中,通过分子对接、遗传算法-多元线性回归和比较分子场分析,对一组异喹啉类化合物作为 JNK1 抑制剂进行了综合分子建模研究,以合理化这些化合物的抑制活性所负责的结构要求。分子对接研究用于探索活性化合物在 JNK1 活性部位的结合模式。基于对接构象,开发了高度可预测的 2D、3D 定量构效关系模型。使用遗传算法-多元线性回归方法建立了包含四个分子描述符的最佳 2D 定量构效关系模型。最佳比较分子场分析模型的交叉验证系数 q(2)为 0.562,非交叉验证 r(2)值为 0.994。定量构效关系模型和分子对接的信息可用于设计具有改善活性的新型 JNK1 抑制剂。