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基于 QSAR 模型和分子对接研究的自闭症新型先导化合物的设计与筛选。

Design and Screening of New Lead Compounds for Autism Based on QSAR Model and Molecular Docking Studies.

机构信息

School of Public Health, Qingdao University, Qingdao 266071, China.

School of Basic Medicine, Ningxia Medical University, Yinchuan 750004, China.

出版信息

Molecules. 2022 Oct 26;27(21):7285. doi: 10.3390/molecules27217285.

Abstract

The purpose of the present study aims to develop a satisfactory model for predicting pro-social and pro-cognitive effects on azinesulfonamides of cyclic amine derivatives as potential antipsychotics. The three dimensional-quantitative structure affinity relationship (3D-QSAR) study was performed on a series of azinesulfonamides of cyclic amine derivative using comparative molecular similarity indices analysis (CoMSIA). The best statistical model of CoMSIA q2, r2, SEE and F values are 0.664, 0.973, 0.087, and 82.344, respectively. Based on the model contour maps and the highest activity structure of the 43rd compound, serial new structures were designed and the 43k1 compound was selected as the best structure. The dock results showed a good binding of 43k1 with the protein (PDB ID: 6A93). The QSAR model analysis of the contour maps can help us to provide guidelines for finding novel potential antipsychotics.

摘要

本研究旨在开发一种令人满意的模型,以预测环状胺衍生物的吖嗪磺酰胺的亲社会和认知促进作用,作为潜在的抗精神病药物。使用比较分子相似性指数分析(CoMSIA)对一系列环状胺衍生物的吖嗪磺酰胺进行了三维定量构效关系(3D-QSAR)研究。CoMSIA 的最佳统计模型 q2、r2、SEE 和 F 值分别为 0.664、0.973、0.087 和 82.344。基于模型轮廓图和第 43 个化合物的最高活性结构,设计了一系列新结构,并选择 43k1 化合物作为最佳结构。对接结果表明,43k1 与蛋白质(PDB ID:6A93)具有良好的结合。轮廓图的 QSAR 模型分析可以帮助我们提供寻找新型潜在抗精神病药物的指导方针。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e245/9657114/332dd6a2aee9/molecules-27-07285-g001.jpg

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