关于N-芳基衍生物对阿尔茨海默病活性的定量构效关系研究。

QSAR studies on N-aryl derivative activity towards Alzheimer's disease.

作者信息

Solomon Kamalakaran Anand, Sundararajan Srinivasan, Abirami Veluchamy

机构信息

Department of Bioinformatics, Sri Ramachandra University, Porur, Chennai, India.

出版信息

Molecules. 2009 Apr 7;14(4):1448-55. doi: 10.3390/molecules14041448.

DOI:10.3390/molecules14041448

PMID:19384276

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6254147/

Abstract

A Quantitative Structure Activity Relationship (QSAR) study has been an attempted on a series of 88 N-aryl derivatives which display varied inhibitory activity towards both acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), targets in Alzheimer's drug discovery. QSAR models were derived for 53 and 61 compounds for each target, respectively, with the aid of genetic function approximation (GFA) technique using topological, molecular shape, electronic and structural descriptors. The predictive ability of the QSAR model was evaluated using a test set of 26 compounds for AChE (r(2)(pred) = 0.857), (q(2)= 0.803) and 20 compounds for BChE (r(2)(pred)= 0.882), (q(2)= 0.857). The QSAR models point out that AlogP98, Wiener, Kappa-1-AM, Dipole-Mag, and CHI-1 are the important descriptors effectively describing the bioactivity of the compounds.

摘要

对一系列88种N-芳基衍生物进行了定量构效关系（QSAR）研究，这些衍生物对乙酰胆碱酯酶（AChE）和丁酰胆碱酯酶（BChE）均表现出不同的抑制活性，而这两种酶是阿尔茨海默病药物研发中的靶点。借助遗传函数近似（GFA）技术，分别使用拓扑、分子形状、电子和结构描述符，为每个靶点的53种和61种化合物推导了QSAR模型。使用26种化合物的测试集评估了AChE的QSAR模型的预测能力（r(2)(pred) = 0.857），（q(2)= 0.803），以及20种化合物的测试集评估了BChE的QSAR模型的预测能力（r(2)(pred)= 0.882），（q(2)= 0.857）。QSAR模型指出，AlogP98、维纳指数、卡帕-1-AM、偶极矩和CHI-1是有效描述化合物生物活性的重要描述符。