Virtual Screening of Acetylcholinesterase Inhibitors Based on Machine Learning Combined with Molecule Docking Methods

Author(s): Jinyu Yan, Weiguang Huang, Chi Zhang*, Haizhong Huo*, Fuxue Chen*

Journal Name: Current Bioinformatics

Volume 16 , Issue 7 , 2021

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Graphical Abstract:


Objective: The aim of this study was to screen for compounds with relatively high inhibitory activity on acetylcholinesterase.

Methods: Classification models for acetylcholinesterase inhibitors based on KNN (1-nearest neighbors), and a quantitative prediction model based on support vector machine regression were used. The interaction of the compounds and receptors was analyzed using the molecular simulation method.

Results: The radial basis kernel function was selected as the kernel function for support vector machine regression, and a total of 19 descriptors were selected to construct the quantitative prediction model.

Keywords: Alzheimer's disease, acetylcholinesterase inhibitor, non-acetylcholinesterase inhibitor, QSAR model, molecular simulation, SVM.

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Article Details

Year: 2021
Published on: 19 July, 2020
Page: [963 - 971]
Pages: 9
DOI: 10.2174/1574893615999200719234045
Price: $65

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