[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128493-en":3,"doc-seo-128493-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128493,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Discovery of novel acetylcholinesterase inhibitors through integration of machine learning with genetic algorithm based in silico screening approaches","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.","TYPE Original Research PUBLISHED 03 March 2023  \nDOI 10.3389/fnins.2022.1007389  \nOPEN ACCESS  \nEDITED BY  \nAnshul Tiwari,  \nHarvard Medical School, United States  \nREVIEWED BY  \nMirza Masroor Ali Beg,  \nInternational Atatürk-Alatoo University, Kyrgyzstan  \nZhiqiang Guo,  \nLanzhou Petrochemical General Hospital, China  \n*CORRESPONDENCE  \nJae-June Dong [s82tonight@yuhs.ac](s82tonight@yuhs.ac)[ ](s82tonight@yuhs.ac)Mohammad Hassan Baig [mhbaig@yonsei.ac.kr](mhbaig@yonsei.ac.kr)  \n†These authors have contributed equally to this work  \nSPECIALTY SECTION  \nThis article was submitted to Neurogenomics,  \na section of the journal Frontiers in Neuroscience  \nRECEIVED 30 July 2022  \nACCEPTED 08 November 2022  \nPUBLISHED 03 March 2023  \nCITATION  \nKhan 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.  \nFront. Neurosci. 16:1007389 .  \ndoi: 10.3389/fnins.2022.1007389  \nCOPYRIGHT  \n© 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.  \nDiscovery of novel acetylcholinesterase inhibitors through integration of machine learning with genetic algorithm based in silico screening approaches  \nMohd 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*  \n1 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  \nIntroduction: 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.  \nMaterials 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.  \nResult 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.  \nKEYWORDS  \nAlzheimer’s disease, machine learning (ML), virtual screening, molecular dynamics (MD), acetylcholinesterase (AChE)  \nFrontiers in Neuroscience 01 [frontiersin.org](frontiersin.org)  \nIntroduction  \nAlzheimer’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) .","cbCaijPyC2YLPGaH","https://ap.wps.com/l/cbCaijPyC2YLPGaH","pdf",2722344,1,10,"English","en",105,"# Introduction\n## Background on Alzheimer’s disease and current therapies\n## Rationale for AChE inhibitors and the cholinergic hypothesis\n# Materials and methods\n## ML predictive modeling for AChE inhibitor discovery\n## In silico screening and computational workflow\n# Result and discussion\n## Identification of promising AChE inhibitors","[{\"question\":\"Why does the study focus on acetylcholinesterase (AChE) inhibitors for Alzheimer’s disease?\",\"answer\":\"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.\"},{\"question\":\"What computational strategy does the study use to discover candidate molecules?\",\"answer\":\"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.\"},{\"question\":\"What do the study’s results indicate about the identified compounds?\",\"answer\":\"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.\"}]","Discovery of novel acetylcholinesterase inhibitors through integration of machine learning with genetic algorithm based in silico screening approaches | PDF",1786001372,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"discovery-of-novel-acetylcholinesterase-inhibitors-through-integration-of-machine-learning-with-genetic-algorithm-based-in-silico-screening-approaches","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/discovery-of-novel-acetylcholinesterase-inhibitors-through-integration-of-machine-learning-with-genetic-algorithm-based-in-silico-screening-approaches/128493/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why does the study focus on acetylcholinesterase (AChE) inhibitors for Alzheimer’s disease?","Question",{"text":76,"@type":77},"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.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What computational strategy does the study use to discover candidate molecules?",{"text":81,"@type":77},"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.",{"name":83,"@type":74,"acceptedAnswer":84},"What do the study’s results indicate about the identified compounds?",{"text":85,"@type":77},"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.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":21,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]