Leveraging Molecular Descriptors and Explainable Machine Learning for Monomer Conversion Prediction in Photoinduced Electron Transfer-Reversible Addition- Fragmentation Chain Transfer Polymerization

Leveraging Molecular Descriptors and Explainable Machine Learning for Monomer Conversion Prediction in Photoinduced Electron Transfer-Reversible Addition- Fragmentation Chain Transfer Polymerization | 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 Leveraging Molecular Descriptors and Explainable Machine Learning for Monomer Conversion Prediction in Photoinduced Electron Transfer-Reversible Addition- Fragmentation Chain Transfer Polymerization Berna Alemdag, Azra Kocaarslan, Gözde Kabay This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7549965/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract This study introduces a molecular descriptor-based machine learning (ML) architecture for predicting monomer conversion in photoinduced electron transfer-reversible addition-fragmentation chain transfer (PET-RAFT) polymerization systems. Unlike conventional polymer informatics approaches that treat polymers as single entities or rely on one-hot encoding of reaction components, we decompose the PET-RAFT system into its molecular building blocks: monomer, RAFT agent, and photocatalyst, encoding each component separately through SMILES-derived descriptors supplemented with thermodynamic parameters. Using a 152 PET-RAFT reactions dataset, we systematically trained (with five-fold cross-validation) and tested 10 ML algorithms. CatBoost demonstrated superior stability across CV folds and was identified as the top-performer for monomer conversion prediction (R 2 = 0.84; RMSE = 10.04 pps; MAE = 8.16 pps). Through SHapley Additive exPlanations (SHAP) analysis, mechanistically interpretable structure-property relationships were revealed that monomer topological complexity, electronic polarization, and molecular weight collectively account for over 60% of the model’s predictive power. External validation demonstrated CatBoost’s generalization ability to unseen (meth)acrylates/ (meth)acrylamides, achieving a mean absolute error (MAE) of 8.03, suggesting improved performance compared to training (9.62 ± 1.71 pps). Furthermore, this explainable, descriptor-based ML approach bridges mechanistic understanding with predictive modelling, providing experimentally actionable hypotheses for rational polymer design while maintaining the interpretability for scientific insights. Physical sciences/Chemistry Physical sciences/Materials science Physical sciences/Mathematics and computing artificial intelligence explainable AI polymer informatics RAFT chemical interpretability Full Text Additional Declarations No competing interests reported. Supplementary Files SUPPORTINGDOCUMENTSR.docx Cite Share Download PDF Status: Published Journal Publication published 09 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 28 Oct, 2025 Reviews received at journal 06 Oct, 2025 Reviews received at journal 26 Sep, 2025 Reviewers agreed at journal 18 Sep, 2025 Reviewers agreed at journal 18 Sep, 2025 Reviewers invited by journal 18 Sep, 2025 Editor invited by journal 18 Sep, 2025 Editor assigned by journal 09 Sep, 2025 Submission checks completed at journal 08 Sep, 2025 First submitted to journal 06 Sep, 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. 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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-7549965","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":511915183,"identity":"d6685867-05f7-44e7-bd94-d4e8855eadec","order_by":0,"name":"Berna Alemdag","email":"","orcid":"","institution":"Karlsruhe Institute of Technology (KIT)","correspondingAuthor":false,"prefix":"","firstName":"Berna","middleName":"","lastName":"Alemdag","suffix":""},{"id":511915184,"identity":"5a94ee6c-1511-419e-bd65-b73a298bcdaf","order_by":1,"name":"Azra Kocaarslan","email":"","orcid":"","institution":"Karlsruhe Institute of Technology (KIT)","correspondingAuthor":false,"prefix":"","firstName":"Azra","middleName":"","lastName":"Kocaarslan","suffix":""},{"id":511915185,"identity":"e050a35d-1c00-4f1c-afda-8f8a7c015de8","order_by":2,"name":"Gözde Kabay","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYJACCQaGBAYDBgbGhw0GIH4C8VqYDRsMDEjTwibZwECEFnn39oc3PjCkyZuznzGrnFHwh4GfPccArxbDMweSLWcw5Bju7Mkxu7kB6DDJnjcEtMxIOCbNw1DBuOEAUMsDoBaDG4RsmZHYJv2HocJ+w/k3ZoUgLfaEtMhLJLNJMzDkJG64kWPGCHKYgQQBLQY8x5gtewzSknfOeFYsOcPAmEfizLMC/La0A0PsR0Wy7Xb+5I0fe/7IyfG3J2/Ab8sBMAkiOMAkD17lYFsa4Ez2BwRVj4JRMApGwcgEAPb0Rp6bzFrUAAAAAElFTkSuQmCC","orcid":"","institution":"Karlsruhe Institute of Technology (KIT)","correspondingAuthor":true,"prefix":"","firstName":"Gözde","middleName":"","lastName":"Kabay","suffix":""}],"badges":[],"createdAt":"2025-09-06 09:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7549965/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7549965/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-33553-y","type":"published","date":"2026-02-09T15:59:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102785444,"identity":"a7cabbaa-e02a-40bb-af9a-082a042b5fc5","added_by":"auto","created_at":"2026-02-16 16:06:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1222296,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptSciR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7549965/v1_covered_d183f29f-dbc7-4eb2-8a4e-be014b5bbfcc.pdf"},{"id":90886511,"identity":"b43c0d43-70fc-4c99-83f0-f2e8ade7a531","added_by":"auto","created_at":"2025-09-09 10:09:36","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2738627,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPORTINGDOCUMENTSR.docx","url":"https://assets-eu.researchsquare.com/files/rs-7549965/v1/6a91f689e9cc7c8e5b05e1ce.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Leveraging Molecular Descriptors and Explainable Machine Learning for Monomer Conversion Prediction in Photoinduced Electron Transfer-Reversible Addition- Fragmentation Chain Transfer Polymerization","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":"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":"artificial intelligence, explainable AI, polymer informatics, RAFT, chemical interpretability","lastPublishedDoi":"10.21203/rs.3.rs-7549965/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7549965/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study introduces a molecular descriptor-based machine learning (ML) architecture for predicting monomer conversion in photoinduced electron transfer-reversible addition-fragmentation chain transfer (PET-RAFT) polymerization systems. Unlike conventional polymer informatics approaches that treat polymers as single entities or rely on one-hot encoding of reaction components, we decompose the PET-RAFT system into its molecular building blocks: monomer, RAFT agent, and photocatalyst, encoding each component separately through SMILES-derived descriptors supplemented with thermodynamic parameters. Using a 152 PET-RAFT reactions dataset, we systematically trained (with five-fold cross-validation) and tested 10 ML algorithms. CatBoost demonstrated superior stability across CV folds and was identified as the top-performer for monomer conversion prediction (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.84; RMSE\u0026thinsp;=\u0026thinsp;10.04 pps; MAE\u0026thinsp;=\u0026thinsp;8.16 pps). Through SHapley Additive exPlanations (SHAP) analysis, mechanistically interpretable structure-property relationships were revealed that monomer topological complexity, electronic polarization, and molecular weight collectively account for over 60% of the model\u0026rsquo;s predictive power. External validation demonstrated CatBoost\u0026rsquo;s generalization ability to unseen (meth)acrylates/ (meth)acrylamides, achieving a mean absolute error (MAE) of 8.03, suggesting improved performance compared to training (9.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71 pps). Furthermore, this explainable, descriptor-based ML approach bridges mechanistic understanding with predictive modelling, providing experimentally actionable hypotheses for rational polymer design while maintaining the interpretability for scientific insights.\u003c/p\u003e","manuscriptTitle":"Leveraging Molecular Descriptors and Explainable Machine Learning for Monomer Conversion Prediction in Photoinduced Electron Transfer-Reversible Addition- Fragmentation Chain Transfer Polymerization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 10:09:31","doi":"10.21203/rs.3.rs-7549965/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-28T11:04:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-06T20:04:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-26T05:52:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66070643427789048778518151008466904472","date":"2025-09-18T15:57:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149070976141853759775744711162117752041","date":"2025-09-18T15:56:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-18T15:52:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-18T15:32:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-09T06:15:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-08T12:37:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-06T09:37:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","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}}],"origin":"","ownerIdentity":"3126259b-f1a3-4f50-bc9c-dc5f9fcc2b44","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":54369031,"name":"Physical sciences/Chemistry"},{"id":54369032,"name":"Physical sciences/Materials science"},{"id":54369033,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-02-16T16:04:06+00:00","versionOfRecord":{"articleIdentity":"rs-7549965","link":"https://doi.org/10.1038/s41598-025-33553-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-02-09 15:59:25","publishedOnDateReadable":"February 9th, 2026"},"versionCreatedAt":"2025-09-09 10:09:31","video":"","vorDoi":"10.1038/s41598-025-33553-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-33553-y","workflowStages":[]},"version":"v1","identity":"rs-7549965","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7549965","identity":"rs-7549965","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}