[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121513-en":3,"doc-seo-121513-105":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":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},121513,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Quantum Machine Learning for Drug Discovery - Taxonomy, Research Challenges, and the Road Ahead","Quantum Machine Learning for Drug Discovery - Taxonomy, Research Challenges, and the Road Ahead examines how machine learning supports modern drug discovery while reducing time and cost pressures. The work contrasts advanced quantum machine learning with classical machine learning across drug design, virtual screening, and ADMET (absorption, distribution, metabolism, excretion) and toxicity prediction. It also surveys QML methods applied to real-world clinical and drug discovery data, highlighting the research opportunities and challenges shaped by epidemic-driven demand and complex discovery pipelines.","Accepted:13 October 2025  \nRevised:26September 2025  \nSURVEY  \nReceived:06September 2024  \nQuantum Machine Learning for Drug Discovery:Taxonomy,ResearchChallenges,and the Road Ahead  \nCitation in BibTeX format  \nHOANG PHI DUONG,Memorial University of Newfoundland,St John's,NL,CanadaSYED MUHAMMAD RIZVI,Kyung Hee University,Seoul,South KoreaBRAD MCNIVEN,Memorial University of Newfoundland,St John's,NL,CanadaTHANH TUAN NGUYEN,University of Greenwich,London,U.K.HYUNDONG SHIN,Kyung Hee University,Seoul,South Korea  \nOCTAVIA A DOBRE,Memorial University of Newfoundland,St John's,NL,CanadaView all  \nOpen Access Support provided by:Memorial University of NewfoundlandUniversity of GreenwichKyung Hee University  \nQuantum Machine Learning for Drug Discovery:Taxonomy,ResearchChallenges,and the Road Ahead  \nHOANG PHI YEN DUONG,Electrical and Computer Engineering,Memorial University,St.John's,CanadaSYED MUHAMMAD ABUZAR RIZVI,Kyung Hee University,Dongdaemun-gu,Korea(the Republic of)BRAD MCNIVEN,Memorial University,St.John's,Canada  \nTHANH TUAN NGUYEN,University of Greenwich,London,United Kingdom of Great Britain and NorthernIreland  \nHYUNDONG SHIN,Kyung Hee University,Dongdaemun-gu,Korea(the Republic of)  \nOCTAVIA DOBRE,Memorial University,St.John's,Canada  \nTRUNG Q.DUONG*,Memorial University,St.John's,Canada,Queen's University Belfast,Belfast,UnitedKingdom of Great Britain and Northern Ireland,and Kyung Hee University,Suwon,Republic of Korea  \nThe recent pandemic outbreak has posed significant challenges for medical research,particularly in drug discovery.Machinelearning(ML)has become increasingly prevalent in various stages of drug discovery,aiming to support the advancementof new drug research while reducing time and cost investments.Furthermore,the emergence of quantum computing andquantum machine learning(QML)represents a significant advancement in this field,offering the ability to tackle the complexprocesses involved in drug discovery.This review provides a comprehensive perspective,comparing advanced QML toclassical ML in drug discovery applications including drug design,virtual screening,and ADMET(absorption,distribution,metabolism,excretion)and toxicity prediction.Additionally,we summarize the current applications of QML algorithms toreal-world data sets utilized in clinical research and drug discovery.  \nAdditional Key Words and Phrases:Quantum Machine Learning,Drug Discovery and Development,Medical Research  \n# 1 Introduction\n\nIn recent years,the emergence of the COVID-19 pandemic has indicated the significant role of drug discovery,attracting substantial attention from scientists in various disciplines.Drug discovery is the process of identifyinga disease target such as protein,DNA,RNA and receptors to find an appropriate drug that is capable of preventingthe disease and improving the lives of patients [111].As shown in Fig 1,drug discovery involves five mainstages:identifying target and validation,lead optimization,pre-clinical testing,clinical trials,and Federal DrugAdministration(FDA)approval[31].From 2009-2018,the FDA has approved over 350 new drugs,approximately35 drugs per year from this period [184].From 2019 to the present,the FDA has approved 259 new drugs,nearly  \nCorresponding authors are Trung Q.Duong and Hyundong Shin.  \nAuthors'Contact Information:Hoang Phi Yen Duong,Electrical and Computer Engineering,Memorial University,St.John's,Newfoundlandand Labrador,Canada;e-mail:yhpduong@mun.ca;Syed Muhammad Abuzar Rizvi,Kyung Hee University,Dongdaemun-gu,Seoul,Korea(the Republic of);e-mail:smabuzarrizvi@khuackr;Brad McNiven,Memorial University,St.John's,Newfoundland and Labrador,Canada;e-mail:bm2570@mun.ca;Thanh Tuan Nguyen,University of Greenwich,London,United Kingdom of Great Britain and Northern Ireland;e-mail:tuan.nguyen@greenwichac.uk;Hyundong Shin,Kyung Hee University,Dongdaemun-gu,Seoul,Korea(the Republic of);e-mail:hshin@khu.ac.kr;Octavia Dobre,Memorial University,St.John's,Newfoundland and Labrador,Canada;e-mail:od","cbCaigUDdyCKYFuQ","https://ap.wps.com/l/cbCaigUDdyCKYFuQ","pdf",2578501,1,36,"English","en",105,"# 1 Introduction\n## Drug discovery stages and challenges\n## Machine learning and quantum machine learning overview\n## Applications in drug design, screening, and ADMET/toxicity","[{\"question\":\"Why is drug discovery considered time-consuming and costly?\",\"answer\":\"The document explains that the process requires huge investment and multiple stages, with significant risk of failure. Examples include long timelines for FDA approval and high failure rates in later stages and lead optimization.\"},{\"question\":\"What role does machine learning play in drug discovery?\",\"answer\":\"Machine learning is used in different stages to support new drug research and reduce time and cost. It is applied to tasks such as drug design and virtual screening, and to predict properties like ADMET and toxicity.\"},{\"question\":\"How does quantum machine learning differ from classical machine learning in this context?\",\"answer\":\"The document positions quantum machine learning as a significant advancement enabled by quantum computing, aiming to address complex processes in drug discovery. It further reviews QML approaches and compares them with classical ML for the same application areas.\"}]","Quantum Machine Learning for Drug Discovery - Taxonomy, Research Challenges, and the Road Ahead | PDF",1785736034,91,{"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},"quantum-machine-learning-for-drug-discovery-taxonomy-research-challenges-and-the-road-ahead","",{"@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/quantum-machine-learning-for-drug-discovery-taxonomy-research-challenges-and-the-road-ahead/121513/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is drug discovery considered time-consuming and costly?","Question",{"text":75,"@type":76},"The document explains that the process requires huge investment and multiple stages, with significant risk of failure. Examples include long timelines for FDA approval and high failure rates in later stages and lead optimization.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role does machine learning play in drug discovery?",{"text":80,"@type":76},"Machine learning is used in different stages to support new drug research and reduce time and cost. It is applied to tasks such as drug design and virtual screening, and to predict properties like ADMET and toxicity.",{"name":82,"@type":73,"acceptedAnswer":83},"How does quantum machine learning differ from classical machine learning in this context?",{"text":84,"@type":76},"The document positions quantum machine learning as a significant advancement enabled by quantum computing, aiming to address complex processes in drug discovery. 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