ArticleFrontiers in neuroscience2022
Discovery of novel acetylcholinesterase inhibitors through integration of machine learning with genetic algorithm based
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- MAISNet: a multi-species integrated graph neural network for acetylcholinesterase inhibitor screening.Bioinformatics (Oxford, England) · 2026Article
- Novel Sulfonylhydrazones With Sulfonate Ester Framework: Promising Dual Inhibitors of AChE and hCAs.Biotechnology and applied biochemistry · 2025Article
- Molecular Modeling and Analysis of Cannabinoid and Cannabinoid-like Molecules Combining K-Means Clustering with Pearson Correlation and PCA.International journal of molecular sciences · 2025Article
- Discovery of Novel Anti-Acetylcholinesterase Peptides Using a Machine Learning and Molecular Docking Approach.Drug design, development and therapy · 2025Article
- Combining Deep Learning and Structural Modeling to Identify Potential Acetylcholinesterase Inhibitors fromACS omega · 2024Article
- Structural and functional neural substrates underlying the concreteness effect.Brain structure & function · 2023Article
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Authors and funding
9 authors.
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Abstract
Introduction: Alzheimer's disease (AD) is the most studied progressive eurodegenerative disorder, affecting 40-50 million of the global population. This progressive neurodegenerative disease is marked by gradual and irreversible declines in cognitive functions. The unavailability of therapeutic drug candidates restricting/reversing the progression of this dementia has severed the existing challenge. The development of acetylcholinesterase (AChE) inhibitors retains a great research focus for the discovery of an anti-Alzheimer drug. Materials and methods: This study focused on finding AChE inhibitors by applying the machine learning (ML) predictive modeling approach, which is an integral part of the current drug discovery process. In this study, we have extensively utilized ML and other Result and discussion: The output of this study helped us to identify some promising AChE inhibitors. The selected compounds performed well at different levels of analysis and may provide a possible pathway for the future design of potent AChE inhibitors.
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