Discovery of novel acetylcholinesterase inhibitors through integration of machine learning with genetic algorithm based in silico screening approaches

Aug 6, 2026

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Alzheimer’s disease is a progressive neurodegenerative disorder with no available therapy to halt or reverse disease progression, motivating continued discovery of effective drug candidates. This study integrates machine learning predictive modeling with genetic algorithm–driven in silico screening to search for acetylcholinesterase (AChE) inhibitors as potential anti-Alzheimer agents. The workflow combines ML and complementary computational methods to prioritize candidate compounds for AChE inhibition. Results identify promising AChE inhibitors that show favorable performance across multiple levels of in silico analysis, supporting future optimization.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

Background on Alzheimer’s disease and current therapies

Rationale for AChE inhibitors and the cholinergic hypothesis

Materials and methods

ML predictive modeling for AChE inhibitor discovery

In silico screening and computational workflow

Result and discussion

Identification of promising AChE inhibitors

TYPE Original Research PUBLISHED 03 March 2023 DOI 10.3389/fnins.2022.1007389 OPEN ACCESS EDITED BY Anshul Tiwari, Harvard Medical School, United States REVIEWED BY Mirza Masroor Ali Beg, International Atatürk-Alatoo University, Kyrgyzstan Zhiqiang Guo, Lanzhou Petrochemical General Hospital, China *CORRESPONDENCE Jae-June Dong [s82tonight@yuhs.ac](s82tonight@yuhs.ac)[ ](s82tonight@yuhs.ac)Mohammad Hassan Baig [mhbaig@yonsei.ac.kr](mhbaig@yonsei.ac.kr) †These authors have contributed equally to this work SPECIALTY SECTION This article was submitted to Neurogenomics, a section of the journal Frontiers in Neuroscience RECEIVED 30 July 2022 ACCEPTED 08 November 2022 PUBLISHED 03 March 2023 CITATION Khan MI, Taehwan P, Cho Y, Scotti M, Priscila Barros de Menezes R, Husain FM, Alomar SY, Baig MH and Dong J-J (2023) Discovery of novel acetylcholinesterase inhibitors through integration of machine learning with genetic algorithm based in silico screening approaches. Front. Neurosci. 16:1007389 . doi: 10.3389/fnins.2022.1007389 COPYRIGHT © 2023 Khan, Taehwan, Cho, Scotti, Priscila Barros de Menezes, Husain, Alomar, Baig and Dong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Discovery of novel acetylcholinesterase inhibitors through integration of machine learning with genetic algorithm based in silico screening approaches Mohd Imran Khan1†, Park Taehwan1†, Yunseong Cho1 , Marcus Scotti2 , Renata Priscila Barros de Menezes2 , Fohad Mabood Husain3 , Suliman Yousef Alomar4 , Mohammad Hassan Baig1* and Jae-June Dong1* 1 Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea, 2 Postgraduate Program in Bioactive Natural and Synthetic Products, Federal University of Paraíba, João Pessoa, Brazil, 3 Department of Food Science and Nutrition, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia, 4 Department of Zoology, College of Science, King Saud University, Riyadh, Saudi Arabia 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 isan integral part of the current drug discovery process. In this study, we have extensively utilized ML and other in silico approaches to search for an effective lead molecule against AChE. 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. KEYWORDS Alzheimer’s disease, machine learning (ML), virtual screening, molecular dynamics (MD), acetylcholinesterase (AChE) Frontiers in Neuroscience 01 [frontiersin.org](frontiersin.org) Introduction Alzheimer’s disease (AD), a most common neurodegenerative brain disorder, has aﬀected more than 40–50 million worldwide (Prince et al., 2013, 2015; Wu et al., 2017; GBD 2016 Dementia Collaborators, 2019) .

FAQs

Why does the study focus on acetylcholinesterase (AChE) inhibitors for Alzheimer’s disease?

AChE is involved in hydrolyzing acetylcholine in the brain, and the cholinergic hypothesis links cholinergic neuron degeneration to Alzheimer’s pathology. Enhancing cholinergic neurotransmission through AChE inhibition remains a key research direction.

What computational strategy does the study use to discover candidate molecules?

The study applies machine learning predictive modeling as part of a drug discovery pipeline, integrating ML with genetic algorithm–based in silico screening and additional computational approaches.

What do the study’s results indicate about the identified compounds?

The selected compounds show promising performance across different levels of analysis in the in silico workflow, suggesting potential pathways for designing more potent AChE inhibitors in future work.

Aug 6, 2026

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