Machine Learning Framework for Multi-Label Antimicrobial Peptide Classification with Interpretable Feature Insights

Machine Learning Framework for Multi-Label Antimicrobial Peptide Classification with Interpretable Feature Insights | 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 Machine Learning Framework for Multi-Label Antimicrobial Peptide Classification with Interpretable Feature Insights Md Saidur Rahman Kohinoor, Md Sabir Hossain This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7623974/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 Antimicrobial peptides are promising alternatives to combat antibiotic resistance, but their multifunctionality poses significant classification challenges. This study introduces a multilabel classification framework using adaptation techniques in Extreme Gradient Boosting (XGB_multi), Random Forest (RF_multi), and Convolutional Neural Networks (CNN_multi) to predict AMP functionalities, including antibacterial, antifungal, antiviral, anticancer, and mammalian cell targeting. A dataset containing \((6,845)\) peptide sequences was curated from the dbAMPv2 database. Sequences were filtered for canonical amino acids and clustered with a \((90%)\) sequence identity threshold using CD-HIT (Cluster Database at High Identity with Tolerance), ensuring diversity and reducing redundancy. Features were extracted using the iFeatureOmega toolkit, combining sequence-based descriptors like Amino Acid Composition (AAC) and Pseudo-Amino Acid Composition (PAAC) with physicochemical properties like hydrophobicity, charge, and secondary structure. XGB_multi achieved the highest accuracy of \((0.919)\) , outperforming RF_multi and CNN_multi in other functional predictions. CNN_multi excelled in antibacterial, antifungal, and antiviral tasks with an AUC of \((0.892)\) , while RF_multi demonstrated high precision ( \((0.861)\) ) and subset accuracy ( \((0.689)\) ). These models outperformed existing tools like AMPfun, MultiPep, iAMPpred, and AMP_scanner v2, achieving up to \((7.9%)\) improvement in AUC for certain functionalities. Feature importance analysis identified charge, hydrophobicity, and structural attributes as critical contributors, with sequence-derived features like PAAC and hydrophobicity5 emerging as highly discriminative. These findings provide a robust foundation for designing innovative antimicrobial therapeutics with multifunctional capabilities to combat drug-resistant pathogens. Antimicrobial Peptides AMP Classification Machine Learning Functional Prediction Feature Importance Analysis Peptide Therapeutics 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-7623974","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":516576310,"identity":"c9cd6e8c-ec80-48e0-891f-c62655bd3213","order_by":0,"name":"Md Saidur Rahman Kohinoor","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIie3RsWrDMBCAYRmBvKj7ZalfQcEQMpW+RxaZgierFALGUEhlAspSyJotz1ACnWUE7SIyZ8gSApk6uHQqZKjcTg2oZOygfzE2fNxJRigU+o/Fdb1rK7hMMDao+vnG/ibUmBTsMO3P1A2yZxHIc7hQVYasTc8jyXXBoKeAR4vigzVqO0Lx9BnQ8cpL+toydreG2xjEijfqICR9KSFyW3pJ/cg4lDCOFmJl3pUREooBRBL7yZQyTQlkclPsddOR5G3gFnvwn4WQTFLliLWYfxOgjhDjJYxig8FCd8kp0+uDUDQfDzP16p+y3M0+22rS/co96HIr5rF52rTHe/8U/ftdI9I9uBe4KfKUhEKhUOi0Lx6eXdf0CQtsAAAAAElFTkSuQmCC","orcid":"","institution":"King Fahd University of Petroleum and Minerals","correspondingAuthor":true,"prefix":"","firstName":"Md","middleName":"Saidur Rahman","lastName":"Kohinoor","suffix":""},{"id":516576311,"identity":"7c71e275-38c5-40ca-9df5-d349211c8388","order_by":1,"name":"Md Sabir Hossain","email":"","orcid":"","institution":"King Fahd University of Petroleum and Minerals","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Sabir","lastName":"Hossain","suffix":""}],"badges":[],"createdAt":"2025-09-15 20:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7623974/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7623974/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91603091,"identity":"24601afc-022b-4446-be58-dc8c047d9fdb","added_by":"auto","created_at":"2025-09-18 08:57:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1343645,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerMultiAMPclassificationAnonymousCopy.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7623974/v1_covered_b887bad9-3649-4826-b8c0-b3b03b9af59a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine Learning Framework for Multi-Label Antimicrobial Peptide Classification with Interpretable Feature Insights","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"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":"Antimicrobial Peptides, AMP Classification, Machine Learning, Functional Prediction, Feature Importance Analysis, Peptide Therapeutics","lastPublishedDoi":"10.21203/rs.3.rs-7623974/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7623974/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAntimicrobial peptides are promising alternatives to combat antibiotic resistance, but their multifunctionality poses significant classification challenges. This study introduces a multilabel classification framework using adaptation techniques in Extreme Gradient Boosting (XGB_multi), Random Forest (RF_multi), and Convolutional Neural Networks (CNN_multi) to predict AMP functionalities, including antibacterial, antifungal, antiviral, anticancer, and mammalian cell targeting. A dataset containing \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((6,845)\\)\u003c/span\u003e\u003c/span\u003e peptide sequences was curated from the dbAMPv2 database. Sequences were filtered for canonical amino acids and clustered with a \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((90%)\\)\u003c/span\u003e\u003c/span\u003e sequence identity threshold using CD-HIT (Cluster Database at High Identity with Tolerance), ensuring diversity and reducing redundancy. Features were extracted using the iFeatureOmega toolkit, combining sequence-based descriptors like Amino Acid Composition (AAC) and Pseudo-Amino Acid Composition (PAAC) with physicochemical properties like hydrophobicity, charge, and secondary structure. XGB_multi achieved the highest accuracy of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((0.919)\\)\u003c/span\u003e\u003c/span\u003e, outperforming RF_multi and CNN_multi in other functional predictions. CNN_multi excelled in antibacterial, antifungal, and antiviral tasks with an AUC of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((0.892)\\)\u003c/span\u003e\u003c/span\u003e, while RF_multi demonstrated high precision (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((0.861)\\)\u003c/span\u003e\u003c/span\u003e) and subset accuracy (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((0.689)\\)\u003c/span\u003e\u003c/span\u003e). These models outperformed existing tools like AMPfun, MultiPep, iAMPpred, and AMP_scanner v2, achieving up to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((7.9%)\\)\u003c/span\u003e\u003c/span\u003e improvement in AUC for certain functionalities. Feature importance analysis identified charge, hydrophobicity, and structural attributes as critical contributors, with sequence-derived features like PAAC and hydrophobicity5 emerging as highly discriminative. These findings provide a robust foundation for designing innovative antimicrobial therapeutics with multifunctional capabilities to combat drug-resistant pathogens.\u003c/p\u003e","manuscriptTitle":"Machine Learning Framework for Multi-Label Antimicrobial Peptide Classification with Interpretable Feature Insights","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-18 08:41:27","doi":"10.21203/rs.3.rs-7623974/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":"bb85a6cd-da5c-42d2-852b-6eca013ce6b2","owner":[],"postedDate":"September 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-30T13:38:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-18 08:41:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7623974","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7623974","identity":"rs-7623974","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}