Literature DB >> 28275924

2D-SAR and 3D-QSAR analyses for acetylcholinesterase inhibitors.

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

Mesh:

Substances:

Year:  2017        PMID: 28275924     DOI: 10.1007/s11030-017-9732-0

Source DB:  PubMed          Journal:  Mol Divers        ISSN: 1381-1991            Impact factor:   2.943


  43 in total

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2.  Mutagenesis of essential functional residues in acetylcholinesterase.

Authors:  G Gibney; S Camp; M Dionne; K MacPhee-Quigley; P Taylor
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Journal:  Proteins       Date:  2001-02-01

4.  Cholinesterase inhibitors: SAR and enzyme inhibitory activity of 3-[omega-(benzylmethylamino)alkoxy]xanthen-9-ones.

Authors:  Lorna Piazzi; Federica Belluti; Alessandra Bisi; Silvia Gobbi; Stefano Rizzo; Manuela Bartolini; Vincenza Andrisano; Maurizio Recanatini; Angela Rampa
Journal:  Bioorg Med Chem       Date:  2006-09-27       Impact factor: 3.641

5.  Pharmacophore mapping-based virtual screening followed by molecular docking studies in search of potential acetylcholinesterase inhibitors as anti-Alzheimer's agents.

Authors:  Pravin Ambure; Supratik Kar; Kunal Roy
Journal:  Biosystems       Date:  2013-12-08       Impact factor: 1.973

Review 6.  Current status on Alzheimer disease molecular genetics: from past, to present, to future.

Authors:  Karolien Bettens; Kristel Sleegers; Christine Van Broeckhoven
Journal:  Hum Mol Genet       Date:  2010-04-13       Impact factor: 6.150

Review 7.  The amyloid hypothesis of Alzheimer's disease: progress and problems on the road to therapeutics.

Authors:  John Hardy; Dennis J Selkoe
Journal:  Science       Date:  2002-07-19       Impact factor: 47.728

8.  Cytisine basicity, solvation, logP, and logD theoretical determination as tool for bioavailability prediction.

Authors:  Tomasz Pieńko; Monika Grudzień; Przemysław Paweł Taciak; Aleksander Paweł Mazurek
Journal:  J Mol Graph Model       Date:  2015-11-12       Impact factor: 2.518

9.  Bias in error estimation when using cross-validation for model selection.

Authors:  Sudhir Varma; Richard Simon
Journal:  BMC Bioinformatics       Date:  2006-02-23       Impact factor: 3.169

10.  Tau protein phosphorylated at threonine 181 in CSF as a neurochemical biomarker in Alzheimer's disease: original data and review of the literature.

Authors:  Piotr Lewczuk; Hermann Esselmann; Mirko Bibl; Georg Beck; Juan Manuel Maler; Markus Otto; Johannes Kornhuber; Jens Wiltfang
Journal:  J Mol Neurosci       Date:  2004       Impact factor: 2.866

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Journal:  Molecules       Date:  2021-04-11       Impact factor: 4.411

2.  Use of connectivity index and simple topological parameters for estimating the inhibition potency of acetylcholinesterase.

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3.  Machine learning models for predicting the activity of AChE and BACE1 dual inhibitors for the treatment of Alzheimer's disease.

Authors:  G Dhamodharan; C Gopi Mohan
Journal:  Mol Divers       Date:  2021-07-29       Impact factor: 2.943

4.  Vesicular stomatitis forecasting based on Google Trends.

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Journal:  PLoS One       Date:  2018-01-31       Impact factor: 3.240

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