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Insight into the Structural Determinants of Imidazole Scaffold-Based Derivatives as TNF-α Release Inhibitors by in Silico Explorations

机译:通过Silico Explorations深入了解以咪唑支架为基础的衍生物作为TNF-α释放抑制剂的结构决定因素

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摘要

Presently, 151 widely-diverse pyridinylimidazole-based compounds that show inhibitory activities at the TNF-α release were investigated. By using the distance comparison technique (DISCOtech), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) methods, the pharmacophore models and the three-dimensional quantitative structure-activity relationships (3D-QSAR) of the compounds were explored. The proposed pharmacophore model, including two hydrophobic sites, two aromatic centers, two H-bond donor atoms, two H-bond acceptor atoms, and two H-bond donor sites characterizes the necessary structural features of TNF-α release inhibitors. Both the resultant CoMFA and CoMSIA models exhibited satisfactory predictability (with Q2 (cross-validated correlation coefficient) = 0.557, R2ncv (non-cross-validated correlation coefficient) = 0.740, R2pre (predicted correlation coefficient) = 0.749 and Q2 = 0.598, R2ncv = 0.767, R2pre = 0.860, respectively). Good consistency was observed between the 3D-QSAR models and the pharmacophore model that the hydrophobic interaction and hydrogen bonds play crucial roles in the mechanism of actions. The corresponding contour maps generated by these models provide more diverse information about the key intermolecular interactions of inhibitors with the surrounding environment. All these models have extended the understanding of imidazole-based compounds in the structure-activity relationship, and are useful for rational design and screening of novel 2-thioimidazole-based TNF-α release inhibitors.
机译:目前,研究了对TNF-α释放具有抑制活性的151种广泛的吡啶基咪唑基化合物。通过使用距离比较技术(DISCOtech),比较分子场分析(CoMFA)和比较分子相似性指标分析(CoMSIA)方法,化合物的药效基团模型和三维定量构效关系(3D-QSAR)被探索了。拟议的药效团模型包括两个疏水位点,两个芳族中心,两个H键供体原子,两个H键受体原子和两个H键供体位点,表征了TNF-α释放抑制剂的必要结构特征。所得的CoMFA模型和CoMSIA模型均表现出令人满意的可预测性(Q 2 (交叉验证的相关系数)= 0.557,R 2 ncv(非交叉验证的相关系数) = 0.740,R 2 pre(预测的相关系数)= 0.749,Q 2 = 0.598,R 2 ncv = 0.767,R 2 pre = 0.860)。在3D-QSAR模型和药效团模型之间观察到良好的一致性,疏水相互作用和氢键在作用机理中起关键作用。这些模型生成的相应轮廓图提供了有关抑制剂与周围环境的关键分子间相互作用的更多信息。所有这些模型在结构-活性关系上扩展了对基于咪唑的化合物的理解,可用于合理设计和筛选新型的基于2-硫咪唑的TNF-α释放抑制剂。

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