[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118925-en":3,"doc-seo-118925-105":30,"detail-sidebar-cat-0-en-105":83},{"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},118925,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","A Machine Learning Approach for PLGA Nanoparticles in Antiviral Drug Delivery","Nanoparticles are extensively studied in laboratory settings, yet only a limited number of discoveries reach clinical translation because optimizing drug formulations often relies on slow, costly trial-and-error experimentation. This work proposes a machine-learning paradigm to streamline antiviral drug development using data extracted from research reports on PLGA nanoparticle size, polydispersity index, drug loading capacity, and encapsulation efficiency. A Gaussian Process model generates prediction graphs to estimate drug loading and encapsulation for target nanoparticle characteristics, reducing experimental iterations and saving laboratory time while improving efficiency.","pharmaceutics  \nArticle  \nA Machine Learning Approach for PLGA Nanoparticles in Antiviral Drug Delivery  \nLabiba Noorain 1, Vu Nguyen 2, *, Hae-Won Kim 3,4,5 and Linh T. B. Nguyen 3,6, *  \nCitation: Noorain, L.; Nguyen, V.; Kim, H.-W.; Nguyen, L.T.B. A Machine Learning Approach for PLGA Nanoparticles in Antiviral Drug Delivery. Pharmaceutics 2023, 15, 495. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)pharmaceutics15020495  \nAcademic Editors: Prabir Patra and Ziyad S. Haidar  \nReceived: 17 November 2022  \nRevised: 25 January 2023  \nAccepted: 31 January 2023  \nPublished: 2 February 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Pharmaceutics, School of Pharmacy, University College London, London WC1N 1AX, UK  \n2 Machine Learning Research Group, Eagle House, University of Oxford, Oxford OX2 6ED, UK  \n3 UCL Eastman-Korea Dental Medicine Innovation Centre, Dankook University, Cheonan 31116, Republic of Korea  \n4 Institute of Tissue Regeneration Engineering, Dankook University, Cheonan 31116, Republic of Korea  \n5 BK21 NBM Global Research Centre for Regenerative Medicine, Dankook University, Cheonan 31116, Republic of Korea  \n6 Eastman Dental Institute, University College London, Royal Free Hospital, Rowland Hill Street, London NW3 2PF, UK  \n* [Correspondence: vu@ieee.org](Correspondence: vu@ieee.org) (V.N.); [l.nguyen@ucl.ac.uk](l.nguyen@ucl.ac.uk) (L.T.B.N.)  \nAbstract: In recent years, nanoparticles have been highly investigated in the laboratory. However, only a few laboratory discoveries have been translated into clinical practice. These findings in the laboratory are limited by trial-and-error methods to determine the optimum formulation for successful drug delivery. A new paradigm is required to ease the translation of lab discoveries to clinical practice. Due to their previous success in antiviral activity, it is vital to accelerate the discovery of novel drugs to treat and manage viruses. Machine learning is a subfield of artificial intelligence and consists of computer algorithms which are improved through experience. It can generate predictions from data inputs via an algorithm which includes a method built from inputsand outputs. Combining nanotherapeutics and well-established machine-learning algorithms can simplify antiviral-drug development systems by automating the analysis. Other relationships in biopharmaceutical networks would eventually aid in reaching a complex goal very easily. From previous laboratory experiments, data can be extracted and input into machine learning algorithms to generate predictions. In this study, poly (lactic-co-glycolic acid) (PLGA) nanoparticles were investigated in antiviral drug delivery. Data was extracted from research articles on nanoparticle size, polydispersity index, drug loading capacity and encapsulation efficiency. The Gaussian Process, a form of machine learning algorithm, could be applied to this data to generate graphs with predictions of the datasets. The Gaussian Process is a probabilistic machine learning model which defines a prior over function. The mean and variance of the data can be calculated via matrix multiplications, leading to the formation of prediction graphs—the graphs generated in this study which could be used for the discovery of novel antiviral drugs. The drug load and encapsulation efficiency of a nanoparticle with a specific size can be predicted using these graphs. This could eliminate the trial-and-error discovery method and save laboratory time and ease efficiency.  \nKeywords: nanoparticles; machine learning; antiviral; PLGA  \n1. Introduction  \nTraditional pharmaceutical drug development proc","cbCaiqE0p9z62Aql","https://ap.wps.com/l/cbCaiqE0p9z62Aql","pdf",4860258,1,17,"English","en",105,"# Introduction\n## Limitations of traditional trial-and-error drug development\n# Methods\n## Data extraction from nanoparticle research articles\n## Gaussian Process for property prediction\n# Results and Applications\n## Prediction graphs for drug loading and encapsulation efficiency","[{\"question\":\"Which PLGA nanoparticle properties are targeted for prediction?\",\"answer\":\"Predictions focus on drug loading capacity and encapsulation efficiency, using extracted research data such as nanoparticle size and polydispersity index.\"}]","A Machine Learning Approach for PLGA Nanoparticles in Antiviral Drug Delivery | PDF",1785720978,43,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"a-machine-learning-approach-for-plga-nanoparticles-in-antiviral-drug-delivery","",{"@graph":36,"@context":77},[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/a-machine-learning-approach-for-plga-nanoparticles-in-antiviral-drug-delivery/118925/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"Which PLGA nanoparticle properties are targeted for prediction?","Question",{"text":75,"@type":76},"Predictions focus on drug loading capacity and encapsulation efficiency, using extracted research data such as nanoparticle size and polydispersity index.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]