[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125362-en":3,"doc-seo-125362-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},125362,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Machine Learning and Artificial Intelligence in Nanomedicine - Review article","Nanomedicine leverages nanoscale lipid, polymeric, and inorganic particles to deliver diagnostic or therapeutic agents for cancer, infectious diseases, and neurological disorders, yet clinical translation of promising designs remains difficult. Key determinants—including particle size, surface chemistry, and payload interactions—must be optimized, while preclinical outcomes often do not forecast human efficacy. This review explains how AI and machine learning accelerate nanomedicine development through high-throughput screening, structure–function extraction, biodistribution prediction, and improved protein corona modeling. It also examines persistent issues in data standardization, model generalizability, and the absence of dedicated FDA guidance at the AI–nanomedicine interface.","UC Riverside  \nUC Riverside Previously Published Works  \nTitle  \nMachine Learning and Artificial Intelligence in Nanomedicine  \nPermalink  \n[https://escholarship.org/uc/item/15f7q3fx](https://escholarship.org/uc/item/15f7q3fx)  \nJournal  \nWiley Interdisciplinary Reviews Nanomedicine and Nanobiotechnology, 17(4)  \nISSN  \n1939-5116  \nAuthors  \nChou, Wei‐Chun  \nCanchola, Alexa Zhang, Fan et al.  \nPublication Date  \n2025-07-01  \nDOI  \n10.1002/wnan.70027  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nWiley Interdisciplinary Reviews: Nanomedicine and Nanobiotechnology  \nADVANCED REVIEW  OPEN ACCESS   \nMachine Learning and Artificial Intelligence  \nin Nanomedicine  \nWei-Chun Chou1,2 | Alexa Canchola1  | Fan Zhang3 | Zhoumeng Lin4,5   \n1Department of Environmental Sciences, University of California, Riverside, California, USA | 2Environmental Toxicology Graduate Program, University of California, Riverside, California, USA | 3Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, Florida, USA | 4Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, Florida,  \nUSA | 5Center for Environmental and Human Toxicology, University of Florida, Gainesville, Florida, USA Correspondence: Wei-Chun Chou ([weichun.chou@ucr.edu](weichun.chou@ucr.edu)) | Zhoumeng Lin ([linzhoumeng@ufl.edu](linzhoumeng@ufl.edu))  \nReceived: 5 April 2025 | Revised: 24 June 2025 | Accepted: 22 July 2025  \nEditor-in-Chief: Fabiana Quaglia | Executive Editor: Nancy Ann Monteiro-Riviere  \nFunding: The work was supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health (NIH)  \n(Grant nos: R03EB035643 and R01EB031022) .  \nKeywords: artificial intelligence | drug delivery | machine learning | nanomedicine | pharmacokinetics  \nABSTRACT  \nNanomedicine harnesses nanoscale materials, such as lipid, polymeric, and inorganic nanoparticles, to deliver diagnostic or therapeutic agents for cancer, infectious disease, and neurological disorders, among others. However, translating promising nanoparticle designs into clinically approved products remains a challenge. Factors such as particle size, surface chemistry, and payload interactions must be optimized, and preclinical results often fail to predict human efficacy. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools to address these hurdles at every stage of nanomedicine development. By rapidly screening extensive libraries and extracting structure–function relationships, AI-driven models can rationalize nanoparticle formulation, predict biodistribution, and guide optimal design. Techniques like high-throughput DNA barcoding and automated liquid handling facilitate robust, large-scale data collection, feeding into computational pipelines that expedite discovery while reducing reliance on resource-intensive trial-and-error experiments. AI-based platforms also enable improved modeling of protein corona formation, which profoundly affects nanoparticle immunogenicity and cellular uptake. Despite these advances, challenges persist in data standardization, model generalizability, and establishing a clear regulatory framework since no dedicated U.S. Food and Drug Administration (FDA) guidance addresses the intersection of AI and nanomedicine. Overcoming these limitations requires harmonized data sharing, rigorous in vivo validation, and clear ethical and regulatory guidelines. This review summarizes the rapidly evolving landscape of AI in nanomedicine, highlighting key successes in design and preclinical prediction, as well ","cbCaipTsmqBJ2HMd","https://ap.wps.com/l/cbCaipTsmqBJ2HMd","pdf",1602454,1,18,"English","en",105,"# Introduction\n## Nanomedicine and translation challenges\n# AI and machine learning in nanomedicine\n## High-throughput data generation and structure–function learning\n## Predicting biodistribution and guiding formulation\n## Protein corona modeling and biological interactions\n# Remaining challenges and regulatory landscape\n## Data standardization and generalizability\n## In vivo validation, ethics, and FDA guidance gaps","[{\"question\":\"Why is translating nanoparticle designs into approved nanomedicine products difficult?\",\"answer\":\"Clinical translation is hindered by the need to optimize particle size, surface chemistry, and payload interactions, while preclinical results often fail to predict human efficacy.\"},{\"question\":\"How do AI and machine learning help across stages of nanomedicine development?\",\"answer\":\"They support rapid screening of large libraries, extraction of structure–function relationships, prediction of biodistribution, and guidance for optimal nanoparticle design.\"},{\"question\":\"What challenges remain even with recent AI advances in nanomedicine?\",\"answer\":\"Issues persist in data standardization, model generalizability, and the lack of a clear regulatory framework, including the absence of dedicated FDA guidance for the AI–nanomedicine intersection.\"}]","Machine Learning and Artificial Intelligence in Nanomedicine - Review article | PDF",1785898423,45,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-and-artificial-intelligence-in-nanomedicine-review-article","",{"@graph":36,"@context":85},[37,54,68],{"@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/machine-learning-and-artificial-intelligence-in-nanomedicine-review-article/125362/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is translating nanoparticle designs into approved nanomedicine products difficult?","Question",{"text":75,"@type":76},"Clinical translation is hindered by the need to optimize particle size, surface chemistry, and payload interactions, while preclinical results often fail to predict human efficacy.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do AI and machine learning help across stages of nanomedicine development?",{"text":80,"@type":76},"They support rapid screening of large libraries, extraction of structure–function relationships, prediction of biodistribution, and guidance for optimal nanoparticle design.",{"name":82,"@type":73,"acceptedAnswer":83},"What challenges remain even with recent AI advances in nanomedicine?",{"text":84,"@type":76},"Issues persist in data standardization, model generalizability, and the lack of a clear regulatory framework, including the absence of dedicated FDA guidance for the AI–nanomedicine intersection.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]