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.