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


Jinyu Yan, Weiguang Huang, Chi Zhang*, Haizhong Huo and Fuxue Chen   Pages 1 - 9 ( 9 )


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 receptor were 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.


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


School of Life Sciences, Shanghai University, Shanghai, School of Life Sciences, Shanghai University, Shanghai, University of Auckland, Auckland, Department of General Surgery, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, School of Life Sciences, Shanghai University, Shanghai

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