{"paper_id":"062393e7-b4df-446f-ad97-5df05f6f3237","body_text":"Binding affinity predictions with hybrid quantum-classical convolutional neural networks | 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 Article Binding affinity predictions with hybrid quantum-classical convolutional neural networks Laia Domingo, Marko Djukic, Christine Johnson, Florentino Borondo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3240806/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Oct, 2023 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Central in drug design is the identification of biomolecules that uniquely and robustly bind to a target protein, while minimizing their interactions with others. Accordingly, precise binding affinity prediction, enabling the accurate selection of suitable candidates from an extensive pool of potential compounds, can greatly reduce the expenses associated to practical experimental protocols. In this respect, recent advances revealed that deep learning methods show superior performance compared to other traditional computational methods, especially with the advent of large datasets. These methods, however, are complex and very time-intensive, thus representing an important clear bottleneck for their development and practical application. In this context, the emerging realm of quantum machine learning holds promise for enhancing numerous classical machine learning algorithms. In this work, we take one step forward and present a hybrid quantum-classical convolutional neural network, which is able to reduce by 20% the complexity of the classical counterpart while still maintaining optimal performance in the predictions. Additionally, this results in a significant cost and time savings of up to 40% in the training stage, which means a substantial speed-up of the drug design process. Biological sciences/Drug discovery/Target validation Physical sciences/Physics/Quantum physics Physical sciences/Physics/Quantum physics/Quantum information Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Oct, 2023 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Major revision 13 Sep, 2023 Reviews received at journal 04 Sep, 2023 Reviewers agreed at journal 14 Aug, 2023 Reviewers invited by journal 14 Aug, 2023 Editor assigned by journal 14 Aug, 2023 Editor invited by journal 11 Aug, 2023 Submission checks completed at journal 11 Aug, 2023 First submitted to journal 07 Aug, 2023 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. 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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-3240806\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":225842869,\"identity\":\"07d1f4c4-ca0a-463d-8af3-fa39a39aacb1\",\"order_by\":0,\"name\":\"Laia Domingo\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYBACPgbGBoYPBiAmY8MBBjYitLABVTLOIFELAwMzDwqXoBb2w42fbQru2PVLNzceYCizSWwgqIUnsVk6x+BZ8sw5B4EOO5dGhBaGxAaglsPJBjcSGw4wth02Juww/ofNvy2AWuwhWv4ToUUisU2aweCwnYEEWMsBOSK0PGyz7DE4nCABsiXhXDJhLfz86Y9v/Phz2J5/RvrjDx/K7HgIaoEBSEAlEK0eCOxJUTwKRsEoGAUjDAAAIEU+FSTM2rsAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Ingenii\",\"correspondingAuthor\":true,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Laia\",\"middleName\":\"\",\"lastName\":\"Domingo\",\"suffix\":\"\"},{\"id\":225842870,\"identity\":\"c5fd0270-e806-409e-8046-e6afc99334f2\",\"order_by\":1,\"name\":\"Marko Djukic\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Ingenii\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Marko\",\"middleName\":\"\",\"lastName\":\"Djukic\",\"suffix\":\"\"},{\"id\":225842871,\"identity\":\"fa6fd727-2ebd-4650-8f85-8645d888ee52\",\"order_by\":2,\"name\":\"Christine Johnson\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Ingenii\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Christine\",\"middleName\":\"\",\"lastName\":\"Johnson\",\"suffix\":\"\"},{\"id\":225842872,\"identity\":\"883f0830-ddb5-4acf-98a7-994cb78a69c4\",\"order_by\":3,\"name\":\"Florentino Borondo\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Universidad Autonoma de Madrid\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Florentino\",\"middleName\":\"\",\"lastName\":\"Borondo\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2023-08-07 06:14:24\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-3240806/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-3240806/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1038/s41598-023-45269-y\",\"type\":\"published\",\"date\":\"2023-10-20T15:01:32+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":45091042,\"identity\":\"a304d2f6-84d7-4ca2-9215-38c0ebaad00f\",\"added_by\":\"auto\",\"created_at\":\"2023-10-23 15:08:12\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1189782,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Files.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3240806/v1_covered_cbfb77a6-4b9c-4e5b-aa92-169f0d74d182.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Binding affinity predictions with hybrid quantum-classical convolutional neural networks\",\"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\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-3240806/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-3240806/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"Central in drug design is the identification of biomolecules that uniquely and robustly bind to a target protein, while minimizing their interactions with others. 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