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A QSAR Study for the Prediction of Inhibitory Activity of Coumarin Derivatives for the Treatment of Alzheimer’s Disease
Arabian Journal for Science and Engineering ( IF 2.9 ) Pub Date : 2020-11-10 , DOI: 10.1007/s13369-020-05064-7
Sarah Ghanei-Nasab , Farzin Hadizadeh , Alireza Foroumadi , Azam Marjani

The inhibition of acetylcholinesterase (AChE) enzyme has been used as a successful therapeutic strategy for the symptomatic treatment of Alzheimer’s disease and its progression. It is also known that Coumarins, a group of naturally occurring substances in many plants, exhibit a wide range of biological activities such as AChE inhibition. In this study, we present a quantitative structure–activity relationship (QSAR) analysis to predict the inhibitory activity (\({\mathrm{IC}}_{50}\)) of Coumarins derivatives using several statistical regression and machine learning models based on various molecular descriptors of 94 different compounds extracted by the popular Dragon software. The models include multiple linear regression (MLR), partial least squares (PLS), random forests, artificial neural networks, and support vector machine (SVM). Also, a genetic algorithm (GA) was used in combination with MLR, PLS, SVM, and ANN to find a smaller subset of the utilized descriptors. The results indicated that the GA-ANN model achieves the best \({\mathrm{IC}}_{50}\) prediction accuracy.



中文翻译:

预测香豆素衍生物抑制阿尔茨海默氏病活性的QSAR研究

抑制乙酰胆碱酯酶(AChE)酶已被用作对症治疗阿尔茨海默氏病及其进展的成功治疗策略。还已知香豆素是许多植物中的一组天然存在的物质,它们表现出广泛的生物学活性,例如抑制AChE。在这项研究中,我们提出了一种定量构效关系(QSAR)分析来预测抑制活性(\({\ mathrm {IC}} _​​ {50} \)香豆素衍生物,使用几种统计回归和机器学习模型,这些模型基于流行的Dragon软件提取的94种不同化合物的各种分子描述符。这些模型包括多元线性回归(MLR),偏最小二乘(PLS),随机森林,人工神经网络和支持向量机(SVM)。同样,遗传算法(GA)与MLR,PLS,SVM和ANN结合使用,以找到所利用描述符的较小子集。结果表明,GA-ANN模型获得了最佳的\({\ mathrm {IC}} _​​ {50} \)预测精度。

更新日期:2020-11-12
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