Research on a Prediction Model for the Bioconcentration Factor (BCF) of Polyhalogenated Organic Phosphates Based on QSPR

Research on a Prediction Model for the Bioconcentration Factor (BCF) of Polyhalogenated Organic Phosphates Based on QSPR | 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 Research on a Prediction Model for the Bioconcentration Factor (BCF) of Polyhalogenated Organic Phosphates Based on QSPR Xiongjun Yuan, Cheng Wang, Yongde Wei, Jingjie Shi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8471107/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 The bioconcentration factor (BCF) is a key parameter for evaluating the environmental behavior and ecological risks of organic pollutants, yet experimental determination of BCF values is time-consuming and unsuitable for large-scale screening. Polyhalogenated organophosphate esters, widely used as flame retardants, have raised increasing environmental concern due to their persistence and potential bioaccumulation. In this study, a quantitative structure–property relationship (QSPR) modeling framework was developed to predict the BCF of polyhalogenated organophosphate esters and support environmental risk prioritization. A dataset consisting of 160 compounds was compiled and divided into training (n = 130) and test (n = 30) sets. Ten informative molecular descriptors were selected from an initial pool of 766 candidates using a genetic algorithm. Three predictive models, including multiple linear regression (MLR), support vector machine (SVM), and backpropagation artificial neural network (BP-ANN), were constructed and systematically evaluated. Dataset partitioning was verified using Tanimoto similarity analysis and uniform manifold approximation and projection (UMAP) visualization to ensure structural independence and chemical-space representativeness. Model performance was assessed through a comprehensive validation scheme employing 11 statistical metrics covering internal validation, external predictivity, and robustness. The applicability domain was further defined using Williams plots to identify reliable prediction boundaries and outlier compounds. Among the three models, the BP-ANN exhibited the best overall predictive performance, achieving R ² train = 0.86 and Q ² F2 = 0.79, with low external prediction errors ( MAE test = 0.33 and RMSE test = 0.41). The results indicate that bioaccumulation behavior of polyhalogenated organophosphate esters is jointly governed by hydrophobicity, molecular topology, electronic distribution, and functional group composition. The proposed QSPR framework provides a reliable and efficient tool for screening the bioaccumulation potential of structurally related flame retardants and may assist in early-stage environmental risk assessment and sustainable chemical management. Backpropagation artificial neural network (BP-ANN) Bioconcentration factor (BCF) Molecular descriptors Polyhalogenated organophosphates Quantitative structure–property relationship (QSPR) Full Text Additional Declarations No competing interests reported. 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. 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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-8471107","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":586354624,"identity":"e841f1da-f1c2-4865-9f23-d125be0937e5","order_by":0,"name":"Xiongjun Yuan","email":"","orcid":"","institution":"Changzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xiongjun","middleName":"","lastName":"Yuan","suffix":""},{"id":586354625,"identity":"8fa1ad79-c7e7-42ec-b926-e5e69408dd79","order_by":1,"name":"Cheng Wang","email":"","orcid":"","institution":"Changzhou University","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Wang","suffix":""},{"id":586354626,"identity":"df2099b8-1edb-4567-be85-ea089b7ca85f","order_by":2,"name":"Yongde Wei","email":"","orcid":"","institution":"Changzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yongde","middleName":"","lastName":"Wei","suffix":""},{"id":586354627,"identity":"bb43809a-08d9-49d2-8efa-6585ab4276df","order_by":3,"name":"Jingjie Shi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYBAC+RmMDQYJDAzMbAzMB6BiCfi1GNyAa2GDKSWkRQLO5DEgUot0c0PBwx217HzsPR8//PhzmIGfPceA4ecO3Frk5xxsMEg8c5yZjefsZskensMMkj1vDBh7z+Cx5kYiUEvbMWY2idxtzAwSh4G+yzFgZmwjRov8m2fMDAaHGeyJ1FIDtIWHjZkhAWiLBAEtBhAtB4B+STOW7DmQziNx5lnBwV48WuRnpD8z/NlWlyzffvghMMSs5fjbkzc++InPYQwMbMD4OJwM4/GAiAN4NQAj/gEDQ50dAUWjYBSMglEwkgEARdZPeNrpQZwAAAAASUVORK5CYII=","orcid":"","institution":"Changzhou University","correspondingAuthor":true,"prefix":"","firstName":"Jingjie","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2025-12-29 08:53:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8471107/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8471107/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102295838,"identity":"383da465-b1ec-4180-8a41-ff2b72f22663","added_by":"auto","created_at":"2026-02-10 10:15:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":856697,"visible":true,"origin":"","legend":"","description":"","filename":"paperBCF.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8471107/v1_covered_b42aef88-cac0-4301-8408-633b800ad544.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on a Prediction Model for the Bioconcentration Factor (BCF) of Polyhalogenated Organic Phosphates Based on QSPR","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"Backpropagation artificial neural network (BP-ANN), Bioconcentration factor (BCF), Molecular descriptors, Polyhalogenated organophosphates, Quantitative structure–property relationship (QSPR)","lastPublishedDoi":"10.21203/rs.3.rs-8471107/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8471107/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe bioconcentration factor (BCF) is a key parameter for evaluating the environmental behavior and ecological risks of organic pollutants, yet experimental determination of BCF values is time-consuming and unsuitable for large-scale screening. Polyhalogenated organophosphate esters, widely used as flame retardants, have raised increasing environmental concern due to their persistence and potential bioaccumulation. In this study, a quantitative structure–property relationship (QSPR) modeling framework was developed to predict the BCF of polyhalogenated organophosphate esters and support environmental risk prioritization. A dataset consisting of 160 compounds was compiled and divided into training (n = 130) and test (n = 30) sets. Ten informative molecular descriptors were selected from an initial pool of 766 candidates using a genetic algorithm. Three predictive models, including multiple linear regression (MLR), support vector machine (SVM), and backpropagation artificial neural network (BP-ANN), were constructed and systematically evaluated. Dataset partitioning was verified using Tanimoto similarity analysis and uniform manifold approximation and projection (UMAP) visualization to ensure structural independence and chemical-space representativeness. Model performance was assessed through a comprehensive validation scheme employing 11 statistical metrics covering internal validation, external predictivity, and robustness. The applicability domain was further defined using Williams plots to identify reliable prediction boundaries and outlier compounds. Among the three models, the BP-ANN exhibited the best overall predictive performance, achieving \u003cem\u003eR\u003c/em\u003e²\u003csub\u003etrain\u003c/sub\u003e = 0.86 and \u003cem\u003eQ\u003c/em\u003e²\u003csub\u003eF2\u003c/sub\u003e = 0.79, with low external prediction errors (\u003cem\u003eMAE\u003c/em\u003e\u003csub\u003etest\u003c/sub\u003e = 0.33 and \u003cem\u003eRMSE\u003c/em\u003e\u003csub\u003etest\u003c/sub\u003e = 0.41). The results indicate that bioaccumulation behavior of polyhalogenated organophosphate esters is jointly governed by hydrophobicity, molecular topology, electronic distribution, and functional group composition. The proposed QSPR framework provides a reliable and efficient tool for screening the bioaccumulation potential of structurally related flame retardants and may assist in early-stage environmental risk assessment and sustainable chemical management.\u003c/p\u003e","manuscriptTitle":"Research on a Prediction Model for the Bioconcentration Factor (BCF) of Polyhalogenated Organic Phosphates Based on QSPR","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-06 18:43:54","doi":"10.21203/rs.3.rs-8471107/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"087aa93f-df5b-4b04-870f-c911d5b9c0f7","owner":[],"postedDate":"February 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-06T18:43:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-06 18:43:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8471107","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8471107","identity":"rs-8471107","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}