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

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

Volume: 16 Issue: 7

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

Affiliation:

• Huaxia Eye Hospital of Foshan, Huaxia Eye Hospital Group, Foshan, Guangdong 528000,China

• Department of General Surgery, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai,China

• School of Life Sciences, Shanghai University, Shanghai,China

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

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.

Cite this article as:

Yan Jinyu, Huang Weiguang , Zhang Chi *, Huo Haizhong *, Chen Fuxue *, Virtual Screening of Acetylcholinesterase Inhibitors Based on Machine Learning Combined with Molecule Docking Methods, Current Bioinformatics 2021; 16 (7) . https://dx.doi.org/10.2174/1574893615999200719234045