[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123444-en":3,"doc-seo-123444-105":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},123444,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Machine learning in antiviral drug design - Review article","Viral infections constitute a major worldwide health risk, while effective antivirals remain limited due to viral mutation, reliance on host machinery, and the persistent challenge of selective toxicity. Traditional antiviral drug discovery is slow and costly, intensifying the need for faster strategies. Since the COVID-19 pandemic, machine learning and broader AI methods have advanced antiviral drug discovery by enabling earlier identification of promising hit compounds. This review focuses on ML applications and highlights studies where model-guided candidates show experimentally confirmed activity in biological assays.","Bioorg. Med. Chem. 132 (2026) 118426  \nContents lists available at ScienceDirect  \nBioorganic & Medicinal Chemistry  \njournal [homepage: www.elsevier.com/locate/bmc](homepage: www.elsevier.com/locate/bmc)  \n| Review Article\u003Cbr>Machine learning in antiviral drug design Anja Kolariˇc a, Marko Jukiˇc a,b,*, Urban Bren a,b,c,*\u003Cbr>a Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova ulica 17, SI-2000 Maribor, Slovenia\u003Cbr>b Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaˇska ulica 8, SI-6000 Koper, Slovenia c Institute of Environmental Protection and Sensors, Beloruska ulica 7, SI-2000 Maribor, Slovenia |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Machine learning Artificial intelligence\u003Cbr>Antiviral compounds Biological activity |  | Viral infections pose a significant health threat worldwide. Due to the high mutation rates of many viruses and their reliance on host cellular machinery, the development of effective antiviral therapies is particularly difficult. As a result, only a limited number of antiviral agents is currently available. In parallel to modern vaccines, traditional antiviral drug development is both time-consuming and costly, underscoring the need for faster, more efficient approaches. In recent years, particularly since the beginning of the COVID-19 pandemic, machine learning (ML) together with broader artificial intelligence (AI), have emerged as powerful methodologies for drug discovery and offer the potential to accelerate the identification and development of antiviral agents. This review examines the application of ML in the early stages of antiviral drug discovery, with a particular focus on recent studies where ML methods have successfully identified hit compounds with experimentally demonstrated activity in biological assays. By highlighting these successful case studies, the review illustrates the growing impact of ML in advancing the discovery of urgently needed novel antivirals. |\n\n1. Introduction  \nViral infections represent a major global public health challenge. Although hundreds of pathogenic viruses pose threats to human health, effective antiviral drugs are available for only a limited number of them.1 Developing antivirals is particularly difficult because viruses depend on the host cellular machinery for replication, making it challenging to identify drug targets that can selectively inhibit the virus without damaging host cells. This lack of selective toxicity remains a significant obstacle in the antiviral drug development.2 A further complication in the development of antiviral drugs represents the high mutation rate of viruses, which can lead to treatments becoming less effective or even completely ineffective. Traditional antiviral drug discovery and development is both time-consuming and expensive, factors that become especially critical when facing rapidly mutating viruses oremerging pathogens for which immediate therapeutic options are needed.3 This issue has been underscored by the outbreaks of highly pathogenic viruses such as Zika, Ebola, and severe acute respiratory syndrome (SARS, SARS-CoV-2) virus, which have caused substantial morbidity and mortality, yet are still lacking in the number of approved, effective antiviral treatments.1 These limitations highlight the urgent  \nneed for faster, more efficient approaches to antiviral drug development. The latter has been clearly demonstrated by the COVID-19 pandemic that started in late 2019.  \nIn recent years, artificial intelligence (AI) has emerged as a transformative technology in drug discovery, becoming an integral component of modern pharmaceutical research. Machine learning (ML) in particular has attracted great interest due to its potential to accelerate drug discovery and reduce costs. ML can be used for various tasks, such as identifying ","cbCaitj2pbrzRcVD","https://ap.wps.com/l/cbCaitj2pbrzRcVD","pdf",11409266,1,36,"English","en",105,"# Introduction\n## Challenges in antiviral drug development\n## Role of AI and ML in drug discovery\n# Machine learning applications in early antiviral discovery\n## Case studies with experimentally validated hit compounds","[{\"question\":\"Why is developing antiviral therapies especially difficult?\",\"answer\":\"Viruses depend on host cellular machinery for replication, making it hard to find targets that inhibit the virus without harming host cells. High viral mutation rates also reduce or eliminate treatment effectiveness over time.\"},{\"question\":\"How does machine learning help in antiviral drug design?\",\"answer\":\"ML models process large datasets to predict biological activity, off-target effects, and interactions with specific protein targets. This can accelerate tasks such as candidate identification, drug repurposing, and compound design.\"},{\"question\":\"What does this review emphasize about successful ML outcomes?\",\"answer\":\"It concentrates on early-stage antiviral drug discovery studies where ML methods successfully identified hit compounds that were validated with experimentally demonstrated activity in biological assays.\"}]","Machine learning in antiviral drug design - Review article | PDF",1785816549,91,{"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-in-antiviral-drug-design-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-in-antiviral-drug-design-review-article/123444/",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","2026-08-04",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 developing antiviral therapies especially difficult?","Question",{"text":75,"@type":76},"Viruses depend on host cellular machinery for replication, making it hard to find targets that inhibit the virus without harming host cells. High viral mutation rates also reduce or eliminate treatment effectiveness over time.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does machine learning help in antiviral drug design?",{"text":80,"@type":76},"ML models process large datasets to predict biological activity, off-target effects, and interactions with specific protein targets. This can accelerate tasks such as candidate identification, drug repurposing, and compound design.",{"name":82,"@type":73,"acceptedAnswer":83},"What does this review emphasize about successful ML outcomes?",{"text":84,"@type":76},"It concentrates on early-stage antiviral drug discovery studies where ML methods successfully identified hit compounds that were validated with experimentally demonstrated activity in biological assays.","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"]