[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86578-en":3,"doc-seo-86578-105":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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},86578,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Q2 SAR: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning","Quantitative Structure-Activity Relationship (QSAR) modeling is central to early-stage drug discovery, predicting compound toxicity, bioavailability, and therapeutic potential from molecular descriptors. Classical approaches often struggle with highly complex, non-linear, and high-dimensional molecular interactions, limiting predictive accuracy and increasing costly late-stage failures. This paper introduces a Quantum Multiple Kernel Learning (QMKL) framework using Quantum Support Vector Machines (QSVMs), encoding descriptors into exponentially large quantum Hilbert spaces for richer non-linear modeling and improved performance.","Q 2 SAR: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning  \nMariano Caruso   \nUGR, Granada, Spain UNIR, La Rioja, Spain FIDESOL, Granada, Spain [mcaruso@fidesol.org](mcaruso@fidesol.org)  \nDaniel Ruiz   \nQNOW Technologies  \nDelaware, USA  \n[daniel@qnow.tech](daniel@qnow.tech)  \nAlejandro Giraldo  QNOW Technologies  \nDelaware, USA  \n[alejandro@qnow.tech](alejandro@qnow.tech)  \nGuido Bellomo  CONICET -UBA  \nICC, Argentina  \n[gbellomo@icc.fcen.uba.ar](gbellomo@icc.fcen.uba.ar)  \narXiv :2607 . 11701v1 [ quant-ph] 13 Jul 2026  \nAbstract—Quantitative Structure-Activity Relationship (QSAR) modeling is a foundational computational methodology in earlystage drug discovery, heavily relied upon for predicting compound toxicity, bioavailability, and therapeutic potential. However, classical methods often struggle to effectively map the highly complex, non-linear, and high-dimensional interactions inherent in molecular data, leading to reduced predictive accuracy and costly late-stage clinical failures. In this paper, we present a Quantum Multiple Kernel Learning (QMKL) framework—dubbed Next-Gen Q2 SAR—that leverages Quantum Support Vector Machines (QSVMs) to overcome these classical limitations. By encoding molecular descriptors into exponentially large quantum Hilbert spaces, our approach substantially enhances the expressiveness of non-linear modeling. Benchmarking our quantumenhanced framework on a dataset targeting the DYRK1A kinase (a critical target for Alzheimer’s disease), the QMKL-SVM achievesan impressive Area Under the Curve (AUC) score of 0.8750, significantly outperforming classical state-of-the-art Gradient Boosting models (AUC = 0 .8037). Furthermore, we establish a theoretical and empirical pathway toward resolving classical data bottlenecks through projected quantum kernels (PQK) and measurement accelerators. As quantum computing architecture matures, this framework paves the way for autonomous cognitive architectures and self-improving drug discovery pipelines, promising to unlock deeper insights across vast chemical spacesand to accelerate the development of life-saving therapeutics.  \nIndex Terms—Quantum Machine Learning, QSAR, Drug Discovery, Quantum Multiple Kernel Learning, Support Vector Machines  \nI. INTRODUCTION  \nThe accurate prediction of biological activity for novel, uncharacterized chemical compounds is a defining challenge in modern computational chemistry and pharmacology. This foundational task forms the core of Quantitative StructureActivity Relationship (QSAR) modeling, a critical methodology that seeks to infer the biological and toxicological effects of molecules by analyzing intricate patterns between their molecular descriptors and known experimental outcomes [1] . Traditional QSAR models play an essential role in predicting drug candidates’ bioavailability, off-target effects, and overall therapeutic potential, ultimately attempting to reduce the astronomical costs and long timelines associated with high attrition rates in late-stage drug development.  \nDespite their widespread industrial adoption, classical QSAR approaches face profound limitations. Chemical and biolog-  \nical data are intrinsically high-dimensional and highly nonlinear. Classical algorithms typically rely on aggressive dimensionality reduction techniques (such as Principal Component Analysis) or shallow heuristic representations that, while mathematically tractable, often discard crucial structural and semantic relational information. Consequently, models based on classical paradigms like Random Forests or standard MultiLayer Perceptrons often encounter an intrinsic performance ceiling, suffering from reduced generalizability and impaired predictive accuracy when confronted with small or highly imbalanced datasets.  \nTo overcome these barriers, we introduce a quantumenhanced modeling framework that integrates Quantum Support Vector Machines (QSVMs) and Quantum Multiple Kernel Learning (QMKL)","cbCaisX3yMemkYfG","https://ap.wps.com/l/cbCaisX3yMemkYfG","pdf",272347,4,1,6,"English","en",105,"# Abstract\n# Introduction\n## Quantum-enhanced QSAR and Hilbert space feature mapping\n## Hybrid evaluation on DYRK1A kinase inhibitors\n## Addressing NISQ data access and circuit overhead","[{\"question\":\"What problem does the paper address in QSAR modeling for drug discovery?\",\"answer\":\"Classical QSAR methods have difficulty capturing highly non-linear, high-dimensional molecular relationships, which can reduce predictive accuracy and lead to costly late-stage clinical failures.\"},{\"question\":\"How does Quantum Multiple Kernel Learning (QMKL) improve QSAR performance?\",\"answer\":\"The approach leverages Quantum Support Vector Machines (QSVMs) and quantum feature maps to encode molecular descriptors into exponentially large quantum Hilbert spaces, enabling more expressive non-linear modeling.\"},{\"question\":\"Which benchmarking target and performance metric are reported in the abstract?\",\"answer\":\"The framework is benchmarked on DYRK1A kinase inhibitors, achieving an AUC of 0.8750, outperforming classical Gradient Boosting models with an AUC of 0.8037.\"}]",1784212751,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"q2-sar-overcoming-classical-bottlenecks-in-drug-discovery-via-quantum-multiple-kernel-learning","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"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":20},"https://docshare.wps.com/document/q2-sar-overcoming-classical-bottlenecks-in-drug-discovery-via-quantum-multiple-kernel-learning/86578/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-27","2026-07-16",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},"What problem does the paper address in QSAR modeling for drug discovery?","Question",{"text":75,"@type":76},"Classical QSAR methods have difficulty capturing highly non-linear, high-dimensional molecular relationships, which can reduce predictive accuracy and lead to costly late-stage clinical failures.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does Quantum Multiple Kernel Learning (QMKL) improve QSAR performance?",{"text":80,"@type":76},"The approach leverages Quantum Support Vector Machines (QSVMs) and quantum feature maps to encode molecular descriptors into exponentially large quantum Hilbert spaces, enabling more expressive non-linear modeling.",{"name":82,"@type":73,"acceptedAnswer":83},"Which benchmarking target and performance metric are reported in the abstract?",{"text":84,"@type":76},"The framework is benchmarked on DYRK1A kinase inhibitors, achieving an AUC of 0.8750, outperforming classical Gradient Boosting models with an AUC of 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