QCS-ADME: Quantum Circuit Search for Drug Property Prediction with Imbalanced Data and Regression Adaptation

QCS-ADME: Quantum Circuit Search for Drug Property Prediction with Imbalanced Data and Regression Adaptation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article QCS-ADME: Quantum Circuit Search for Drug Property Prediction with Imbalanced Data and Regression Adaptation Kangyu Zheng, Tianfan Fu, Zhiding Liang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7263066/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Mar, 2026 Read the published version in Quantum Machine Intelligence → Version 1 posted 5 You are reading this latest preprint version Abstract The biomedical field is beginning to explore the use of quantum machine learning (QML) for tasks traditionally handled by classical machine learning, especially in predicting ADME (absorption, distribution, metabolism, and excretion) properties, which are essential in drug evaluation. However, ADME tasks pose unique challenges for existing quantum computing systems (QCS) frameworks, as they involve both classification with unbalanced dataset and regression problems. These dual requirements make it necessary to adapt and refine current QCS frameworks to effectively address the complexities of ADME predictions.\\We propose a novel training-free scoring mechanism to evaluate QML circuit performance on imbalanced classification and regression tasks. Our mechanism demonstrates significant correlation between scoring metrics and test performance on imbalanced classification tasks. Additionally, we develop methods to quantify continuous similarity relationships between quantum states, enabling performance prediction for regression tasks. This represents the first comprehensive approach to searching and evaluating QCS circuits specifically for regression applications. \\Validation on representative ADME tasks—eight imbalanced classification and four regression—demonstrates moderate correlation between our scoring metrics and circuit performance, significantly outperforming baseline scoring methods that show negligible correlation. Quantum machine learning Quantum circuit search DrugProperty Prediction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 09 Mar, 2026 Read the published version in Quantum Machine Intelligence → Version 1 posted Reviewers agreed at journal 04 Aug, 2025 Reviewers invited by journal 04 Aug, 2025 Editor assigned by journal 04 Aug, 2025 Submission checks completed at journal 03 Aug, 2025 First submitted to journal 31 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7263066","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495662082,"identity":"5f3776f1-2260-44c6-912c-020137269fbf","order_by":0,"name":"Kangyu Zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYDACCRBx4J8cAwNjAwMDG/FaDhiTriWxAcwjRov87OZjD7+cuZO+fXZzA8OHssOEtTDOOZZuLHPjWe6cOwcbGGecI0ILs0SOmbTEB+bcGRKJDcy8bURoYZPI/wbSki4B0vKXGC08Ejlskh9uHE4Aa2EkRouERJqZNMOZNMMZMgcbDvacSyesRX5G8jPJH8ds5CWk2x8++FFmTVgLCDDzgO0DRg9x6oGA8QdUyygYBaNgFIwCrAAA+I89hJQUMXIAAAAASUVORK5CYII=","orcid":"","institution":"Rensselaer Polytechnic Institute","correspondingAuthor":true,"prefix":"","firstName":"Kangyu","middleName":"","lastName":"Zheng","suffix":""},{"id":495662083,"identity":"474641de-0120-433e-9344-eae103c05c83","order_by":1,"name":"Tianfan Fu","email":"","orcid":"","institution":"Rensselaer Polytechnic Institute","correspondingAuthor":false,"prefix":"","firstName":"Tianfan","middleName":"","lastName":"Fu","suffix":""},{"id":495662084,"identity":"42b435ff-22f3-46cb-9323-5c737914728b","order_by":2,"name":"Zhiding Liang","email":"","orcid":"","institution":"Rensselaer Polytechnic Institute","correspondingAuthor":false,"prefix":"","firstName":"Zhiding","middleName":"","lastName":"Liang","suffix":""}],"badges":[],"createdAt":"2025-07-31 14:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7263066/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7263066/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s42484-026-00369-6","type":"published","date":"2026-03-09T15:59:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":104740880,"identity":"93ede8e5-4fec-461d-b4e8-6857788c5e04","added_by":"auto","created_at":"2026-03-16 16:19:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2409613,"visible":true,"origin":"","legend":"","description":"","filename":"ccgxbhjqhzrfzjnbjxgmspzwqrfxwdpc.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7263066/v1_covered_06c51ee2-6a04-48d7-986c-aa514e138837.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"QCS-ADME: Quantum Circuit Search for Drug Property Prediction with Imbalanced Data and Regression Adaptation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"quantum-machine-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"qumi","sideBox":"Learn more about [Quantum Machine Intelligence](http://link.springer.com/journal/42484)","snPcode":"42484","submissionUrl":"https://submission.nature.com/new-submission/42484/3","title":"Quantum Machine Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Quantum machine learning, Quantum circuit search, DrugProperty Prediction","lastPublishedDoi":"10.21203/rs.3.rs-7263066/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7263066/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe biomedical field is beginning to explore the use of quantum machine learning (QML) for tasks traditionally handled by classical machine learning, especially in predicting ADME (absorption, distribution, metabolism, and excretion) properties, which are essential in drug evaluation. 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