Transparent-ATC-DNN: Deep Neural Network Based ATC Drug Class Prediction Using 17 Molecular Properties and Transparency Analysis Using SHAP Explanation for Property Categorization.

Transparent-ATC-DNN: Deep Neural Network Based ATC Drug Class Prediction Using 17 Molecular Properties and Transparency Analysis Using SHAP Explanation for Property Categorization. | 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 Transparent-ATC-DNN: Deep Neural Network Based ATC Drug Class Prediction Using 17 Molecular Properties and Transparency Analysis Using SHAP Explanation for Property Categorization. Anushka Chaurasia, Deepak Kumar, Yogita a This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3917388/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 Anatomical Therapeutic Chemical (ATC) system assigns unique codes to drugs for tracking, aiding in prescription, public health policies, and research. Different classifiers with distinct properties have been used earlier to predict ATC drug classes. However, Limited research has been conducted on 17 molecular properties and classifier transparency, emphasising the need of categorising each feature to increase model explainability. In this paper,a Transparent-ATC-DNN (Transparent ATC drug class prediction using Deep Neural Network) deep neural network and SHAP based framework was proposed for ATC drug class prediction and feature categorisation based on their contribution towards prediction. The 17 molecular properties and ATC data were extracted from PubChem and SIDER databases and then mapped based on drug identifier. The resulting dataset contained information on 1117 drugs, including their molecular properties, and 14 ATC drug classes. The proposed framework was applied to this dataset utilizing MLSMOTE to manage multilabel class imbalance and compared with eight classifiers, namely the ETC, KNN, SVM, RF, DT, XGBoost, LSTM, and 1D-CNN. The results of multilabel DNN architecture demonstrated promising performance, achieving an average accuracy of 98.89 ± .002, a hamming loss of 1.50 ± .002, and an ROC-AUC of 98.47 ± .003. Features were evaluated and categorized into three distinct categories: highest, lowest, and none, showcasing the proposed framework's effectiveness and explanatory power in predicting ATC drug classes. Anatomical Therapeutic Chemical (ATC) Deep Neural Network 17 Molecular Properties SHAP Feature Importance Ranking 17 Molecular Properties 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. 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-3917388","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270644839,"identity":"8d278952-f185-47c3-a58c-5c06629b719d","order_by":0,"name":"Anushka Chaurasia","email":"data:image/png;base64,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","orcid":"","institution":"National Institute of Technology Meghalaya","correspondingAuthor":true,"prefix":"","firstName":"Anushka","middleName":"","lastName":"Chaurasia","suffix":""},{"id":270644840,"identity":"30809c48-4d91-4e7a-a308-d7ebf20348fc","order_by":1,"name":"Deepak Kumar","email":"","orcid":"","institution":"National Institute of Technology Meghalaya","correspondingAuthor":false,"prefix":"","firstName":"Deepak","middleName":"","lastName":"Kumar","suffix":""},{"id":270644841,"identity":"274c04e9-7f1f-4423-bf16-c1edec7a9817","order_by":2,"name":"Yogita a","email":"","orcid":"","institution":"National Institute of Technology Meghalaya","correspondingAuthor":false,"prefix":"","firstName":"Yogita","middleName":"","lastName":"a","suffix":""}],"badges":[],"createdAt":"2024-02-01 12:44:43","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-3917388/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3917388/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51334475,"identity":"288deef8-f8b4-475f-a015-541a66b39bdf","added_by":"auto","created_at":"2024-02-19 18:40:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3369458,"visible":true,"origin":"","legend":"","description":"","filename":"TransparentATCDNN.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3917388/v1_covered_5ceff3c3-31d6-455b-84f8-8fa8b4fe9d1b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transparent-ATC-DNN: Deep Neural Network Based ATC Drug Class Prediction Using 17 Molecular Properties and Transparency Analysis Using SHAP Explanation for Property Categorization.","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":true,"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":"Anatomical Therapeutic Chemical (ATC), Deep Neural Network, 17 Molecular Properties, SHAP, Feature Importance, Ranking 17 Molecular Properties","lastPublishedDoi":"10.21203/rs.3.rs-3917388/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3917388/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The Anatomical Therapeutic Chemical (ATC) system assigns unique codes to drugs for tracking, aiding in prescription, public health policies, and research. Different classifiers with distinct properties have been used earlier to predict ATC drug classes. However, Limited research has been conducted on 17 molecular properties and classifier transparency, emphasising the need of categorising each feature to increase model explainability. In this paper,a Transparent-ATC-DNN (Transparent ATC drug class prediction using Deep Neural Network) deep neural network and SHAP based framework was proposed for ATC drug class prediction and feature categorisation based on their contribution towards prediction. The 17 molecular properties and ATC data were extracted from PubChem and SIDER databases and then mapped based on drug identifier. The resulting dataset contained information on 1117 drugs, including their molecular properties, and 14 ATC drug classes. The proposed framework was applied to this dataset utilizing MLSMOTE to manage multilabel class imbalance and compared with eight classifiers, namely the ETC, KNN, SVM, RF, DT, XGBoost, LSTM, and 1D-CNN. The results of multilabel DNN architecture demonstrated promising performance, achieving an average accuracy of 98.89 ± .002, a hamming loss of 1.50 ± .002, and an ROC-AUC of 98.47 ± .003. 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