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The work addresses the lack of deep learning methods for discovering MGO scavengers for AD therapeutics by introducing DeepMGO, a model trained on large-scale MGO scavenging assays. DeepMGO predicts activity, prioritizes novel compounds, and validates TP-41 in a mouse memory impairment model, improving depression and memory deficits.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/deep-learning-identifies-tp-41-for-methylglyoxal-scavenging-in-alzheimers-treatment/407177/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/deep-learning-identifies-tp-41-for-methylglyoxal-scavenging-in-alzheimers-treatment/407177.png","ImageObject",300,407,{"name":92,"@type":93},"Finn","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-28",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What clinical problem does the study target in Alzheimer’s disease?","Question",{"text":112,"@type":113},"It targets Alzheimer’s disease mechanisms involving increased advanced glycation end products (AGEs), driven by methylglyoxal (MGO). 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TP-41 is validated in a mouse model of memory impairment and ameliorates depression symptoms and memory deficits.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},407177,1790815378,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":46,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":31,"preview_page_count":4},34359740700684,"https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487","Theranostics 2026, Vol. 16, Issue 3 1103  \nIvyspring  \nInternational Publisher  \nTheranostics  \n2026; 16(3): 1103-1122. doi: 10.7150/thno.111550  \nResearch Paper  \nDeep learning identifies TP-41 for methylglyoxal scavenging in Alzheimer’s treatment  \nAron Park1,*, Seong-Min Hong2,*, Yeeun Lee3, Jungeun Lee4, Seunggyu Jeon4, Seung-Yong Seo4, Jinhyuk Lee5, Seon-Hyeok Kim4, Eun Ji Ko4, Hae Ran Lee4, Sang Heon Jung4, Munhyung Bae4, Min Cheol Kang6, Myoung Gyu Park6, Seungyoon Nam1,3,7􀀍, and Sun Yeou Kim4􀀍  \n1. Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon, 21999, Korea.  \n2. Division of Food Science and Technology, Gyeongsang National University, Jinju, Gyeongnam 52828, Korea.  \n3. Department of Genome Medicine and Science, Gachon Institute of Genome Medicine and Science, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, 21565, Korea.  \n4. College of Pharmacy and Gachon Institute of Pharmaceutical Science, Gachon University, 191, Hambakmoe-ro, Yeonsu-gu, Incheon 21936, Korea  \n5. Genome Editing Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Korea.  \n6. MetaCen Therapeutics Company, Changnyong-daero 256 Beon-gil, Yeongtong-gu, Suwon-si, Gyeonggi-do 16229, Korea.  \n7. Department of Translational-Clinical Medicine, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon, 21999, Korea.  \n*Equally contributed to the work.  \n􀀍 Corresponding authors: SN ([nams@gachon.ac.kr](nams@gachon.ac.kr)) and SYK ([sunnykim@gachon.ac.kr](sunnykim@gachon.ac.kr)) . Seungyoon Nam, Ph.D., Gachon University College of Medicine, \\#729, 38-13, Dokjeom-ro 3beongil, Namdong-gu, Incheon, 21565, Republic of Korea, Tel: +82-32-458-2737, Fax: +82-32-458-2735. Sun Yeou Kim, Ph.D., College of Pharmacy, Gachon University, \\#191, Hambakmoero, Yeonsu-gu, Incheon 21936, Tel: +82-32-820-4931, Fax +82-32-820-4957.  \n© The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)). See [https://ivyspring.com/terms](https://ivyspring.com/terms) for full terms and conditions.  \nReceived: 2025.02.05; Accepted: 2025.10.10; Published: 2026.01.01  \nAbstract  \nRationale: Increased levels of advanced glycation end products (AGEs) have been observed in the brain tissues of patients with Alzheimer's disease (AD) . Methylglyoxal (MGO) is a potent precursor of AGEs. To date, there have been no reports of utilizing deep learning (DL) technologies to target MGO scavengers for the development of AD therapeutics. Therefore, DL-driven approaches may play a crucial role in identifying potential MGO scavengers and candidates for Alzheimer's treatment.  \nMethods: We developed \"DeepMGO,\" a novel DL-based MGO scavenging activity prediction model, trained on 2,262 MGO scavenging activity assays from 660 compounds. Using this approach, we identified and validated TP-41 as a potential MGO scavenger in a mouse model of memory impairment.  \nResults: DeepMGO demonstrated robust predictive performance and identified novel compounds with high MGO scavenging activity. TP-41 ameliorated depression symptoms and memory deficits in mouse models.  \nConclusions: Using DeepMGO, we identified TP-41 as a potential therapeutic agent for AD.  \nKeywords: methylglyoxal, Alzheimer’s disease, deep learning, memory impairment, drug discovery  \nIntroduction  \nAlzheimer's disease (AD) is a neurodegenerative disorder that poses significant global health challenges, characterized by progressive cognitive decline and memory impairment impacting millions worldwide [1] . Despite extensive research efforts into AD pathogenesis, effective treatments remain elusive and current therapies only provide limited symptomatic relief and fail to halt disease progre","cbCaisvmnpgS5sRz","https://ap.wps.com/l/cbCaisvmnpgS5sRz","pdf",8898377,"English","# Abstract\n## Rationale\n## Methods\n## Results\n## Conclusions\n# Keywords\n# Introduction","[{\"question\":\"What clinical problem does the study target in Alzheimer’s disease?\",\"answer\":\"It targets Alzheimer’s disease mechanisms involving increased advanced glycation end products (AGEs), driven by methylglyoxal (MGO). The study aims to identify MGO scavengers to support AD therapeutic development.\"},{\"question\":\"How does DeepMGO work and what data was used for training?\",\"answer\":\"The study develops DeepMGO, a deep learning model that predicts MGO scavenging activity. It is trained on 2,262 MGO scavenging activity assays from 660 compounds.\"},{\"question\":\"What are the main findings regarding TP-41?\",\"answer\":\"DeepMGO shows robust predictive performance and identifies compounds with high MGO scavenging activity. TP-41 is validated in a mouse model of memory impairment and ameliorates depression symptoms and memory deficits.\"}]","Deep learning identifies TP-41 for methylglyoxal scavenging in Alzheimer’s treatment | PDF",1790571214]