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通过 CoMFA 和 CoMSIA 三维定量构效关系研究对邻氨基苯甲酰胺衍生物的结构研究揭示了它们对糖原磷酸化酶抑制作用的结构新见解。

Structural investigations of anthranilimide derivatives by CoMFA and CoMSIA 3D-QSAR studies reveal novel insight into their structures toward glycogen phosphorylase inhibition.

机构信息

Molecular and Structural Biology Division, Central Drug Research Institute, Lucknow, India.

出版信息

SAR QSAR Environ Res. 2011 Jul-Sep;22(5-6):411-49. doi: 10.1080/1062936X.2011.569898. Epub 2011 May 27.

DOI:10.1080/1062936X.2011.569898
PMID:21607894
Abstract

In the present work, three-dimensional quantitative structure-activity relationship (3-D QSAR) studies on a set of 70 anthranilimide compounds has been performed using docking-based as well as substructure-based molecular alignments. This resulted in the selection of more statistically relevant substructure-based alignment for further studies. Further, molecular models with good predictive power were derived using CoMFA (r² = 0.997; Q² = 0.578) and CoMSIA (r² = 0.976; Q² = 0.506), for predicting the biological activity of new compounds. The so-developed contour plots identified several key features of the compounds explaining wide activity ranges. Based on the information derived from the CoMFA contour maps, novel leads were proposed which showed better predicted activity with respect to the already reported systems. Thus, the present study not only offers a highly significant predictive QSAR model for anthranilimide derivatives as glycogen phosphorylase (GP) inhibitors which can eventually assist and complement the rational drug-design attempts, but also proposes a highly predictive pharmacophore model as a guide for further development of selective and more potent GP inhibitors as anti-diabetic agents.

摘要

在本工作中,对一组 70 种邻氨甲酰苯甲酸类化合物进行了基于对接和基于子结构的三维定量构效关系(3-D QSAR)研究。这导致选择了更具统计学意义的基于子结构的排列进行进一步研究。此外,使用 CoMFA(r²=0.997;Q²=0.578)和 CoMSIA(r²=0.976;Q²=0.506)得出了具有良好预测能力的分子模型,用于预测新化合物的生物活性。如此开发的等高线图确定了化合物的几个关键特征,解释了广泛的活性范围。基于 CoMFA 等高线图得出的信息,提出了一些新的先导化合物,它们的预测活性相对于已经报道的系统有所提高。因此,本研究不仅提供了一个对糖原磷酸化酶(GP)抑制剂具有高度重要预测性的 QSAR 模型,该模型最终可以辅助和补充合理的药物设计尝试,而且还提出了一个具有高度预测性的药效团模型,作为进一步开发选择性和更有效的 GP 抑制剂作为抗糖尿病药物的指导。

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