SamplingDesign: RNA Design via Continuous Optimization with Coupled Variables and Monte-Carlo Sampling

SamplingDesign: RNA Design via Continuous Optimization with Coupled Variables and Monte-Carlo Sampling | 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 SamplingDesign: RNA Design via Continuous Optimization with Coupled Variables and Monte-Carlo Sampling Liang Huang, Wei Yu Tang, Ning Dai, Tianshuo Zhou, David Mathews This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6693856/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Feb, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract RNA design aims to find an RNA sequence that can fold into a given target structure, which enables the creation of artificial RNA molecules with specific function, and has numerous applications in medicine. Computationally, it is particularly challenging due to two levels of combinatorial explosion: the exponentially large design space and the exponentially many competing structures for each design. As a result, heuristic methods such as local search have been popular for this task, but they cannot keep up with combinatorial explosion. We instead borrow two techniques from machine learning, continuous relaxation and Monte-Carlo sampling, to the RNA design problem. We formulate RNA design as continuous optimization, which starts with a distribution over all valid candidate sequences, and uses gradient descent to improve the expectation of an arbitrary objective function. We define novel sequence distributions using coupled variables to model the correlation between nucleotides. To make it universally applicable to any objective function, we use sampling to approximate the expected objective function, to estimate the gradient, and to select the final candidate. Compared to the state-of-the-art methods, our work consistently outperforms them in key metrics such as Boltzmann probability, ensemble defect, and energy gap, especially on long and hard-to-design puzzles in the Eterna100 benchmark. Our code is at http://github.com/weiyutang1010/ncrna_design . Biological sciences/Computational biology and bioinformatics Physical sciences/Mathematics and computing/Computer science Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Published Journal Publication published 20 Feb, 2026 Read the published version in Nature Communications → Version 1 posted 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. 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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-6693856","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":463226615,"identity":"1919da22-2fd5-4e8d-8139-a0dc24c7cd6b","order_by":0,"name":"Liang Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYBACPmYwdQDCY2ywAZGNB/BpYUPTkgYm8WthQNVyGJmLQws787OHXxjuJPbPbr/A+HPHebu17YeBttTYRON2GJu5sQzDs8QZd84UMPOeuZ287UwiUMuxtNwG3H4xk5ZgOJzYcCMngZmx7Xay2QGgFqAL8Whh/wbWMh+ohfFn27lks/MPCWnhMZP8ANSy4Ub6AQbetgN2ZjcI2sJTJs1gcNh4440chsO8bckJZjeAtiTg8Qs///Ftkj8qDsvOu5H+8OHPNjt7s/PpDx98qLHBqQUEmHkMQBSPwQEgmQhWmYBHOQgw/gBT7A9ApD0BxaNgFIyCUTACAQCDEWQI15fUbgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-6444-7045","institution":"Oregon State University","correspondingAuthor":true,"prefix":"","firstName":"Liang","middleName":"","lastName":"Huang","suffix":""},{"id":463226616,"identity":"e8f9314a-d181-4ac6-85c6-32f400090970","order_by":1,"name":"Wei Yu Tang","email":"","orcid":"","institution":"University of Southern California","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"Yu","lastName":"Tang","suffix":""},{"id":463226617,"identity":"d85d7e1b-a9d7-4a0e-bea3-ae021d1ad4ac","order_by":2,"name":"Ning Dai","email":"","orcid":"","institution":"Oregon State University","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Dai","suffix":""},{"id":463226618,"identity":"840aece4-604e-4f3d-a926-ae57530aadb8","order_by":3,"name":"Tianshuo Zhou","email":"","orcid":"https://orcid.org/0009-0008-4804-0825","institution":"Oregon State University","correspondingAuthor":false,"prefix":"","firstName":"Tianshuo","middleName":"","lastName":"Zhou","suffix":""},{"id":463226619,"identity":"1bdfbe46-d526-4190-8819-aa70f3ce1d63","order_by":4,"name":"David Mathews","email":"","orcid":"https://orcid.org/0000-0002-2907-6557","institution":"University of Rochester Medical Center","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Mathews","suffix":""}],"badges":[],"createdAt":"2025-05-18 23:15:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6693856/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6693856/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-025-67901-3","type":"published","date":"2026-02-20T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":105618260,"identity":"2c5abc62-a11f-474f-a5e2-3385025a2256","added_by":"auto","created_at":"2026-03-28 07:11:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3618315,"visible":true,"origin":"","legend":"Article File","description":"","filename":"main.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6693856/v1_covered_ef2ff1b0-359f-4d84-9d7d-03d785730595.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"SamplingDesign: RNA Design via Continuous Optimization with Coupled Variables and Monte-Carlo Sampling","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6693856/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6693856/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"RNA design aims to find an RNA sequence that can fold into a given target structure, which enables the creation of artificial RNA molecules with specific function, and has numerous applications in medicine. 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