Deep Learning-Enhanced QSAR Modeling for Predicting Developmental Neurotoxicity Based on Molecular Initiating Events from Adverse Outcome Pathways

Deep Learning-Enhanced QSAR Modeling for Predicting Developmental Neurotoxicity Based on Molecular Initiating Events from Adverse Outcome Pathways | 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 Deep Learning-Enhanced QSAR Modeling for Predicting Developmental Neurotoxicity Based on Molecular Initiating Events from Adverse Outcome Pathways Eufrásia de Sousa Pereira, Vinícius Alexandre Fiaia Costa, Eder Soares Almeida Santos, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7162409/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Jan, 2026 Read the published version in Molecular Diversity → Version 1 posted 11 You are reading this latest preprint version Abstract Developmental neurotoxicity (DNT) is linked to chemical exposure that disrupts the nervous system in humans or animals. Traditional methods for assessing chemical toxicity are valuable but often time-consuming, costly, and involve significant animal use, making it impractical to meet growing demands. To address this, we developed a deep learning-enhanced QSAR modeling framework aimed at predicting binding affinities towards molecular initiating events (MIEs) and key events (KEs) within the Adverse Outcome Pathway (AOP) asso ciated with exposure to cannabis contaminated with pesticides. Our model was trained on data from 24,476 compounds, sourced from the ChEMBL database, and tested against 4 MIE and 6 KE tasks. The DNNs showed superior performance, with an average correlation coefficient of 0.82 ± 0.05 and a root mean square error of 0.72 ± 0.08 for the test set. To enhance interpretability, we used SHAP values to explain the model’s predictions clearly. Furthermore, ECFP4 feature contributions were mapped onto known neurotoxic compounds to highlight regions likely responsible for MIEs visually. Our results confirm that developed models accurately predict DNT and effectively identify the correct MIEs and KEs for several neurotoxicants. Developmental neurotoxicity predictive modeling deep learning cheminformatics explainability Full Text Additional Declarations No competing interests reported. Supplementary Files SupportingInformationESP3.docx Cite Share Download PDF Status: Published Journal Publication published 23 Jan, 2026 Read the published version in Molecular Diversity → Version 1 posted Editorial decision: Revision requested 01 Nov, 2025 Reviews received at journal 30 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviews received at journal 17 Aug, 2025 Reviewers agreed at journal 16 Aug, 2025 Reviewers agreed at journal 14 Aug, 2025 Reviewers invited by journal 14 Aug, 2025 Editor assigned by journal 19 Jul, 2025 Submission checks completed at journal 19 Jul, 2025 First submitted to journal 19 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-7162409","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502688009,"identity":"d3ef24b7-0336-41ed-865d-ace03effa611","order_by":0,"name":"Eufrásia de Sousa Pereira","email":"","orcid":"","institution":"Federal University of Goiás","correspondingAuthor":false,"prefix":"","firstName":"Eufrásia","middleName":"de Sousa","lastName":"Pereira","suffix":""},{"id":502688010,"identity":"b02bad78-54ef-426b-8f76-cb097c4aecd9","order_by":1,"name":"Vinícius Alexandre Fiaia Costa","email":"","orcid":"","institution":"Federal University of Goiás","correspondingAuthor":false,"prefix":"","firstName":"Vinícius","middleName":"Alexandre Fiaia","lastName":"Costa","suffix":""},{"id":502688011,"identity":"62ff45bf-6592-418c-97b1-0bea6c2a4c12","order_by":2,"name":"Eder Soares Almeida Santos","email":"","orcid":"","institution":"Federal University of Goiás","correspondingAuthor":false,"prefix":"","firstName":"Eder","middleName":"Soares Almeida","lastName":"Santos","suffix":""},{"id":502688012,"identity":"770c528c-bd50-4ef3-89d6-432918fe3b24","order_by":3,"name":"Bruno Junior Neves","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYBACAwST+QBDBQNDAila2BIYzpCohceAOC3m0mfMHnzcYZPYz3/mm8SBXwx55g0EtFj25ZgbzjyTljhzRu42iYN9DMUyBwg57AyPmTRv22Fjgxu826Q/9jAkziDkMKiW/8b25888kzhIgpYDcgYMOWwSB34QocWyh61McmZbspzEjTRji4MNEsUShLSY8zBvk/jYZsfD33/44Y0Df2zyCGpBBYxtJGoAgj8k6xgFo2AUjIIRAABk4j4eS4lUfgAAAABJRU5ErkJggg==","orcid":"","institution":"Federal University of Goiás","correspondingAuthor":true,"prefix":"","firstName":"Bruno","middleName":"Junior","lastName":"Neves","suffix":""}],"badges":[],"createdAt":"2025-07-19 06:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7162409/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7162409/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11030-025-11454-6","type":"published","date":"2026-01-23T15:59:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":101152494,"identity":"7b6c016a-6c62-4245-be54-69a23bf1f802","added_by":"auto","created_at":"2026-01-26 16:12:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1369991,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptESP3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7162409/v1_covered_c48c05f0-4843-4c7a-828f-93ab6852ad18.pdf"},{"id":89645150,"identity":"d6bdfc2f-00d9-477c-9a44-e16bd4ab34af","added_by":"auto","created_at":"2025-08-22 08:48:36","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3780865,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformationESP3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7162409/v1/d4ea07e677442012d9d17d9d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep Learning-Enhanced QSAR Modeling for Predicting Developmental Neurotoxicity Based on Molecular Initiating Events from Adverse Outcome Pathways","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":"molecular-diversity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"modi","sideBox":"Learn more about [Molecular Diversity](http://link.springer.com/journal/11030)","snPcode":"11030","submissionUrl":"https://submission.nature.com/new-submission/11030/3","title":"Molecular Diversity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Developmental neurotoxicity, predictive modeling, deep learning, cheminformatics, explainability","lastPublishedDoi":"10.21203/rs.3.rs-7162409/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7162409/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDevelopmental neurotoxicity (DNT) is linked to chemical exposure that disrupts the nervous system in humans or animals. Traditional methods for assessing chemical toxicity are valuable but often time-consuming, costly, and involve significant animal use, making it impractical to meet growing demands. To address this, we developed a deep learning-enhanced QSAR modeling framework aimed at predicting binding affinities towards molecular initiating events (MIEs) and key events (KEs) within the Adverse Outcome Pathway (AOP) asso ciated with exposure to cannabis contaminated with pesticides. Our model was trained on data from 24,476 compounds, sourced from the ChEMBL database, and tested against 4 MIE and 6 KE tasks. The DNNs showed superior performance, with an average correlation coefficient of 0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 and a root mean square error of 0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 for the test set. To enhance interpretability, we used SHAP values to explain the model\u0026rsquo;s predictions clearly. Furthermore, ECFP4 feature contributions were mapped onto known neurotoxic compounds to highlight regions likely responsible for MIEs visually. Our results confirm that developed models accurately predict DNT and effectively identify the correct MIEs and KEs for several neurotoxicants.\u003c/p\u003e","manuscriptTitle":"Deep Learning-Enhanced QSAR Modeling for Predicting Developmental Neurotoxicity Based on Molecular Initiating Events from Adverse Outcome Pathways","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-22 08:48:31","doi":"10.21203/rs.3.rs-7162409/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-01T20:11:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-30T11:06:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115550159294446885694230654656697591335","date":"2025-10-22T00:13:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20571252540194596174226207322800063393","date":"2025-10-16T04:37:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-17T15:08:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98475384467502518855993970459988375732","date":"2025-08-16T05:53:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111163747043300635720700027058241319120","date":"2025-08-14T17:02:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-14T15:55:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-19T14:15:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-19T07:52:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Diversity","date":"2025-07-19T06:39:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-diversity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"modi","sideBox":"Learn more about [Molecular Diversity](http://link.springer.com/journal/11030)","snPcode":"11030","submissionUrl":"https://submission.nature.com/new-submission/11030/3","title":"Molecular Diversity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a402b5b2-331f-4bd9-8303-aae3c1dd9446","owner":[],"postedDate":"August 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:09:16+00:00","versionOfRecord":{"articleIdentity":"rs-7162409","link":"https://doi.org/10.1007/s11030-025-11454-6","journal":{"identity":"molecular-diversity","isVorOnly":false,"title":"Molecular Diversity"},"publishedOn":"2026-01-23 15:59:13","publishedOnDateReadable":"January 23rd, 2026"},"versionCreatedAt":"2025-08-22 08:48:31","video":"","vorDoi":"10.1007/s11030-025-11454-6","vorDoiUrl":"https://doi.org/10.1007/s11030-025-11454-6","workflowStages":[]},"version":"v1","identity":"rs-7162409","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7162409","identity":"rs-7162409","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}