ArticleMolecular diversity2017
2D-SAR and 3D-QSAR analyses for acetylcholinesterase inhibitors.
Article in Molecular diversity, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- The Pharmacokinetic Properties of Veterinary Antiparasitic Drugs in the Context of Human Pregnancy-An In Silico Study.International journal of molecular sciences · 2026Article
- MAISNet: a multi-species integrated graph neural network for acetylcholinesterase inhibitor screening.Bioinformatics (Oxford, England) · 2026Article
- Integrating multi-structure covalent docking with machine-learning consensus scoring enhances potency ranking of human acetylcholinesterase inhibitors.Briefings in bioinformatics · 2026Article
- Identification and Analysis of Anticancer Therapeutic Targets from the Polysaccharide Krestin (PSK) and Polysaccharopeptide (PSP) Using Inverse Docking.Molecules (Basel, Switzerland) · 2024Article
- QSAR analysis of VEGFR-2 inhibitors based on machine learning, Topomer CoMFA and molecule docking.BMC chemistry · 2024Article
- Bioinformatics and machine learning driven key genes screening for hepatocellular carcinoma.Biochemistry and biophysics reports · 2024Article
- Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species.Chemical research in toxicology · 2023Article
- Machine learning models for predicting the activity of AChE and BACE1 dual inhibitors for the treatment of Alzheimer's disease.Molecular diversity · 2022Article
- Use of connectivity index and simple topological parameters for estimating the inhibition potency of acetylcholinesterase.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2022Article
- Recent Developments in New Therapeutic Agents against Alzheimer and Parkinson Diseases: In-Silico Approaches.Molecules (Basel, Switzerland) · 2021Review
- Vesicular stomatitis forecasting based on Google Trends.PloS one · 2018Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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