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首页> 外文期刊>QSAR & combinatorial science >3D-QSAR Studies on a Class of IKK-2 Inhibitors with GALAHAD Used to Develop Molecular Alignment Models
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3D-QSAR Studies on a Class of IKK-2 Inhibitors with GALAHAD Used to Develop Molecular Alignment Models

机译:一类ISK-2抑制剂与GALAHAD的3D-QSAR研究,用于开发分子比对模型

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摘要

Three-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) studies were preformed on a class of Ikb Kinase-2 (IKK-2) inhibitors, which contained thiophene-carboxamideseries and their analogs. Before 3D-QSAR, a pharmacophore-based molecular alignment model was built by using Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Datasets (GALAHAD). At the same time, we constructed another molecular model by application of classical common structure alignment method as a comparative study. The results of the Comparative Molecule Field Analysis (CoMFA), the main 3D-QSAR method applied in this paper, suggested that GALAHAD was a better tool for molecular alignment in this study. Based on the GALAHAD molecular alignment model, 3D-QSAR analysis, including CoMFA and Comparative Molecule Similarity Indices Analysis (CoMSIA), was carried out. The leave-one-out cross-validation correlation coefficient (q_(CoMFA)~2 = 0.642; q_(CoMSIA)~2 = 0.675) and the good correlation between the predicted and experimental activities of excluded test compounds revealed that CoMFA and CoMSIA models were robust. These models are useful tools for predicting the inhibitory activities of the new compounds against IKK-2. Suggestions of structural modification were then derived from the analysis of the 3D-QSAR, and these will probably lead to the more active IKK-2 inhibitors.
机译:对一类Ikb激酶2(IKK-2)抑制剂进行了三维定量构效关系(3D-QSAR)研究,该抑制剂含有噻吩-羧酰胺系列及其类似物。在3D-QSAR之前,通过使用遗传算法对数据集的超分子比对进行线性分配,建立了基于药效团的分子比对模型。同时,我们通过应用经典通用结构比对方法构建了另一个分子模型作为比较研究。本文应用的主要3D-QSAR方法比较分子场分析(CoMFA)的结果表明,GALAHAD是这项研究中进行分子比对的更好工具。基于GALAHAD分子比对模型,进行了3​​D-QSAR分析,包括CoMFA和比较分子相似性指标分析(CoMSIA)。留一法交叉验证相关系数(q_(CoMFA)〜2 = 0.642; q_(CoMSIA)〜2 = 0.675)和排除的测试化合物的预测活性与实验活性之间的良好相关性表明CoMFA和CoMSIA模型健壮。这些模型是预测新化合物对IKK-2抑制活性的有用工具。然后从3D-QSAR分析中得出结构修饰的建议,这些建议可能会导致活性更高的IKK-2抑制剂。

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