Designing broad-spectrum antimicrobial peptides based on conditional feedback generation adversarial network

Designing broad-spectrum antimicrobial peptides based on conditional feedback generation adversarial network | 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 Designing broad-spectrum antimicrobial peptides based on conditional feedback generation adversarial network Yannan Bin, Zhaoyang Li, Kanglin Wang, Yansen Su This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9128640/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Antimicrobial peptides (AMPs) offer promising alternatives to conventional antibiotics due to their broad-spectrum activity, low toxicity, and reduced risk of resistance development. However, the existing AMP design methods struggle to balance generation efficiency with pathogen-specific targeting, which limits their effectiveness against multi-pathogen infections. To address these challenges, we propose a novel AMP generation method termed CFGAN ( C onditional F eedback G enerative A dversarial N etwork), which integrates conditional information with a reinforcement feedback loop. Based on the GAN architecture, CFGAN incorporates two key innovations: (1) conditional information, encoding details about targeted pathogens, minimum inhibitory concentration level, and peptide length, which allows for precise control over antimicrobial specificity against ten representative pathogens, and (2) reinforcement feedback loop, composing a functional analysor and a dynamic reward mechanism that iteratively guides the generator to produce peptide with broad-spectrum targeting and high antimicrobial activity. To improve training stability and convergence speed, we implement a Brownian Motion Controller that dynamically adjusts the learning rates of both the generator and discriminator. Experimental results show that CFGAN outperforms existing AMP generation models in terms of consistency, stability, and antimicrobial properties. Additionally, molecular dynamics simulations confirm that the generated peptides demonstrate strong structural stability and effective membrane-binding capacity. These findings position CFGAN as an efficient framework for designing broad-spectrum AMPs, with significant potential for the development of next-generation anti-infective drugs. The data and Python codes of CFGAN are available at https://github.com/YannanBin/CFGAN6 . Antimicrobial peptides Generative adversarial network Reinforcement feedback loop Conditional information Molecular dynamics simulation Full Text Additional Declarations No competing interests reported. Supplementary Files CFGANSupplementaryMaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Mar, 2026 Reviews received at journal 19 Mar, 2026 Reviewers agreed at journal 19 Mar, 2026 Reviews received at journal 19 Mar, 2026 Reviewers agreed at journal 19 Mar, 2026 Reviewers invited by journal 19 Mar, 2026 Editor assigned by journal 19 Mar, 2026 Submission checks completed at journal 18 Mar, 2026 First submitted to journal 15 Mar, 2026 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-9128640","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":608674414,"identity":"91ffded7-423b-4b10-9fcb-c91a537b939f","order_by":0,"name":"Yannan Bin","email":"","orcid":"","institution":"Anhui University","correspondingAuthor":false,"prefix":"","firstName":"Yannan","middleName":"","lastName":"Bin","suffix":""},{"id":608674415,"identity":"55ad1513-e261-40c8-b03d-4648b9f48cfc","order_by":1,"name":"Zhaoyang Li","email":"","orcid":"","institution":"Anhui University","correspondingAuthor":false,"prefix":"","firstName":"Zhaoyang","middleName":"","lastName":"Li","suffix":""},{"id":608674420,"identity":"4cce7a0d-a671-4ae4-921f-c287b04206c2","order_by":2,"name":"Kanglin Wang","email":"","orcid":"","institution":"Knature Biopharmaceutical Co. 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However, the existing AMP design methods struggle to balance generation efficiency with pathogen-specific targeting, which limits their effectiveness against multi-pathogen infections. To address these challenges, we propose a novel AMP generation method termed CFGAN (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC\u003c/span\u003eonditional \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eF\u003c/span\u003eeedback \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eG\u003c/span\u003eenerative \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eA\u003c/span\u003edversarial \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eN\u003c/span\u003eetwork), which integrates conditional information with a reinforcement feedback loop. Based on the GAN architecture, CFGAN incorporates two key innovations: (1) conditional information, encoding details about targeted pathogens, minimum inhibitory concentration level, and peptide length, which allows for precise control over antimicrobial specificity against ten representative pathogens, and (2) reinforcement feedback loop, composing a functional analysor and a dynamic reward mechanism that iteratively guides the generator to produce peptide with broad-spectrum targeting and high antimicrobial activity. To improve training stability and convergence speed, we implement a Brownian Motion Controller that dynamically adjusts the learning rates of both the generator and discriminator. Experimental results show that CFGAN outperforms existing AMP generation models in terms of consistency, stability, and antimicrobial properties. Additionally, molecular dynamics simulations confirm that the generated peptides demonstrate strong structural stability and effective membrane-binding capacity. These findings position CFGAN as an efficient framework for designing broad-spectrum AMPs, with significant potential for the development of next-generation anti-infective drugs. The data and Python codes of CFGAN are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/YannanBin/CFGAN6\u003c/span\u003e\u003cspan address=\"https://github.com/YannanBin/CFGAN6\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e","manuscriptTitle":"Designing broad-spectrum antimicrobial peptides based on conditional feedback generation adversarial network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-23 03:55:06","doi":"10.21203/rs.3.rs-9128640/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-19T06:42:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-19T06:41:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41732796569502855200724814774949369244","date":"2026-03-19T06:33:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-19T05:25:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89906466203696061438538719495892505349","date":"2026-03-19T04:54:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-19T04:33:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-19T04:31:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-18T13:05:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Membrane Computing","date":"2026-03-15T12:21:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-membrane-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmec","sideBox":"Learn more about [Journal of Membrane Computing](https://www.springer.com/journal/41965)","snPcode":"41965","submissionUrl":"https://submission.nature.com/new-submission/41965/3","title":"Journal of Membrane Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"564a3901-07ef-445d-845f-4d980e2e5592","owner":[],"postedDate":"March 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-25T14:53:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-23 03:55:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9128640","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9128640","identity":"rs-9128640","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}