| Literature DB >> 28275924 |
Bing Niu1, Manman Zhao2, Qiang Su2, Mengying Zhang2, Wei Lv3, Qin Chen2, Fuxue Chen2, Dechang Chu4, Dongshu Du5,6, Yuhui Zhang7.
Abstract
Alzheimer's disease (AD) accounts for almost three quarters of dementia patients and interferes people's normal life. Great progress has been made recently in the study of Acetylcholinesterase (AChE), known as one of AD's biomarkers. In this study, acetylcholinesterase inhibitors (AChEI) were collected to build a two-dimensional structure-activity relationship (2D-SAR) model and three-dimensional quantitative structure-activity relationship (3D-QSAR) model based on feature selection method combined with random forest. After calculation, the prediction accuracy of the 2D-SAR model was 89.63% by using the tenfold cross-validation test and 87.27% for the independent test set. Three cutting ways were employed to build 3D-QSAR models. A model with the highest [Formula: see text] (cross-validated correlation coefficient) and [Formula: see text](non-cross-validated correlation coefficient) was obtained to predict AChEI activity. The mean absolute error (MAE) of the training set and the test set was 0.0689 and 0.5273, respectively. In addition, molecular docking was also employed to reveal that the ionization state of the compounds had an impact upon their interaction with AChE. Molecular docking results indicate that Ser124 might be one of the active site residues.Entities:
Keywords: 2D-SAR; 3D-QSAR; AChE; Acetylcholinesterase inhibitors; Alzheimer’s disease; Molecular docking
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Year: 2017 PMID: 28275924 DOI: 10.1007/s11030-017-9732-0
Source DB: PubMed Journal: Mol Divers ISSN: 1381-1991 Impact factor: 2.943