BravizineDSE: A Multi-Input Deep Transformer for Receptor-Context-Aware GPCR Signaling Bias Prediction and Derivation of a Six-Feature Molecular Selectivity Rule

BravizineDSE: A Multi-Input Deep Transformer for Receptor-Context-Aware GPCR Signaling Bias Prediction and Derivation of a Six-Feature Molecular Selectivity Rule | 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 BravizineDSE: A Multi-Input Deep Transformer for Receptor-Context-Aware GPCR Signaling Bias Prediction and Derivation of a Six-Feature Molecular Selectivity Rule Aaryan Senthilvanan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9100474/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract G protein-coupled receptors (GPCRs) transduce signals through at least two structurally and functionally distinct pathways—G-protein activation and β-arrestin recruitment—whose selective engagement determines both therapeutic efficacy and side-effect liability. Despite the clinical importance of pathway-selective (“biased”) agonism, no publicly available computational tool has integrated receptor context as an architecturally mandatory input to bias prediction. Here we present BravizineDSE, a multiinput deep transformer trained on 126 million molecule–receptor pairs spanning 100 human GPCRs. The model simultaneously encodes a 160-token SMILES string, a 256-bit circular fingerprint, ten physicochemical descriptors, and a 48-dimensional receptor sequence feature vector through a cross-attention fusion head that renders receptor identity non-optional at inference time. On a scaffold-holdout evaluation the model achieves AUROC = 0.978 and balanced accuracy (BA) = 0.909; generalization degrades gracefully to BA = 0.751 under leaveone- publication-out (LOPO) and BA = 0.627 under leave-one-receptor-out (LORO) protocols, isolating distinct failure modes. Mechanistic interpretability analysis via SHAP across 1412 high-confidence molecule–receptor pairs spanning all five GPCR families yields a transferable, human-readable selectivity rule—Aaryan’s Rule of 6—specifying six molecular thresholds whose satisfaction score predicts DRD2 β-arrestin bias with called-only BA = 0.702 and 83.8% corpus coverage. Surrogate circularity is quantified via descriptor-only, fingerprint-only, and permutedlabel control models; the permuted-label AUROC collapses to 0.513, confirming genuine label-associated signal. The rule identifies sp3 carbon fraction, stereocenter count, aromatic ring count, hydrogen bond acceptor count, topological polar surface area, and molecular weight as jointly sufficient to stratify DRD2 signaling selectivity, and further proposes that nitrogen substitution within an aromatic ring system constitutes a transferable selectivity-encoding motif at protein–ligand interfaces beyond the GPCR superfamily. Clinical Pharmacology Molecular Biology Beta arrestin Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted 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. 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-9100474","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604826352,"identity":"a88e2e29-ccba-4c41-9af3-dde02c740c68","order_by":0,"name":"Aaryan Senthilvanan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYPACGyBmbjhAipY0IGYkTcthsBbi1PK3n30m8XPHeXuD4wcbD/5gsMmXdyCgReJMuplk75nbiRvOJDYc5mFIs9xI0HkH0tgkeNtuJxgcAGoButDAkJD75M8/Y5P823bO3uD8wwagw4jQYnAjjU2at+0A44YbiQ0HeIBa5Am5y/DGM2Zr2bbkxJk3HgL9YpBmYEBIi9z5NMabb9vs7PnOJx/++KPCxkCekMOAgEUCRCocALsTiA4Q1sL8AUTCDSfGllEwCkbBKBhZAADNMUaqgGHlwwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0005-7930-2224","institution":"S.Y.A.L.I.S Labs","correspondingAuthor":true,"prefix":"","firstName":"Aaryan","middleName":"","lastName":"Senthilvanan","suffix":""}],"badges":[],"createdAt":"2026-03-12 05:44:35","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9100474/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9100474/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104584717,"identity":"1fc76b42-505e-487b-89a7-9f232ad4b663","added_by":"auto","created_at":"2026-03-13 15:37:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":387677,"visible":true,"origin":"","legend":"","description":"","filename":"BravizineDSEManuscriptSenthilvanan.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9100474/v1_covered_ff03c88e-2a9e-4879-abf8-857a7fadc541.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eBravizineDSE: A Multi-Input Deep Transformer for Receptor-Context-Aware GPCR Signaling Bias Prediction and Derivation of a Six-Feature Molecular Selectivity Rule\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"S.Y.A.L.I.S Labs","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Beta arrestin","lastPublishedDoi":"10.21203/rs.3.rs-9100474/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9100474/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eG protein-coupled receptors (GPCRs) transduce signals through at least two structurally and functionally distinct pathways—G-protein activation and β-arrestin recruitment—whose selective engagement determines both therapeutic efficacy and side-effect liability. 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