Methods for Continuous-Valued Training Data Generation from Genome-Scale Metabolic Models: Partial-Inhibition FBA with Mixed Essentiality Sampling, Applied to ESKAPE Drug Target Curation

Methods for Continuous-Valued Training Data Generation from Genome-Scale Metabolic Models: Partial-Inhibition FBA with Mixed Essentiality Sampling, Applied to ESKAPE Drug Target Curation | 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 Method Article Methods for Continuous-Valued Training Data Generation from Genome-Scale Metabolic Models: Partial-Inhibition FBA with Mixed Essentiality Sampling, Applied to ESKAPE Drug Target Curation Byeongsoo Kang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9374605/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 Background . Computational antimicrobial target discovery faces three methodological limitations: (i) knockout-only FBA yields binary phenotypes unsuitable for regression training, (ii) no experimentally labeled toxicity datasets exist at the gene-target level, and (iii) pipelines rarely report negative validation results. Methods . We describe a pipeline addressing each limitation. We introduce partial gene inhibition simulation (10-100% flux reduction) applied to mixed essential/non-essential gene sets (39 + 30 genes from 1,516 iML1515 genes), generating 945 continuous-valued FBA simulations as regression training targets (not independent drug response predictions). We describe two ANN architectures: a subsystem-structured ANN (61.5% parameter reduction over fully connected baselines) and a dual-head ANN for joint potency-toxicity regression. We propose an exploratory toxicity labeling heuristic (sequence homology 35%, pathway overlap 30%, conservation 20%, cross-reactivity 15%); weights are an initial proposal pending experimental calibration. These components are integrated with a Neo4j knowledge graph, local LLM literature mining (46% effective precision), and AlphaFold structural analysis. Results . Applied to three ESKAPE pathogen models (iML1515, iYS1720, iYL1228), the pipeline curates 29 targets lacking approved therapeutics from 39 literature-validated essential genes. Sequence-based audit reveals 11 of 21 targets lack detectable human homologs; folA shows 30% identity to human DHFR2, consistent with known trimethoprim cross-reactivity. Prospective-style temporal validation (2020 cutoff) shows the composite scoring heuristic did not exceed a random baseline (F1 = 0.519, z = -0.99), establishing the pipeline as a hypothesis generation tool rather than a predictive model. Double knockout of essential gene pairs produced indistinguishable lethal phenotypes, indicating partial inhibition grids are required for meaningful combination scoring. Conclusions . The methods -- partial inhibition FBA, two ANN architectures, multi-evidence toxicity labeling, and four-way integration -- are individually reusable. The complete pipeline (10-tab dashboard, 40 tests, all code) is released under MIT license at https://github.com/shoo99/ai-drug-target. Bioinformatics antimicrobial resistance flux balance analysis partial gene inhibition genome-scale metabolic model artificial neural network drug target discovery ESKAPE pathogens knowledge graph toxicity prediction open-source Full Text Additional Declarations The authors declare no competing interests. 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-9374605","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":620701543,"identity":"93732238-2be6-4aa0-a2db-482a571cc145","order_by":0,"name":"Byeongsoo Kang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYDACCSD+UGEDJBkbDxCthXHGmTSQlgbitTDzth0Gs4nTIj+7+eBj3rbzdmvbDwNtqbGJJqjF4M6xZMM5524nbzuTCNRyLC23gaAWiRwziTdlt5PNDgC1MDYcJqxFfgZQCw/buWSz8w+J1MJwI8dMkqftgJ3ZDWJtMbiRlmw440xygtkNoC0JxPhFfkbywQcfKuzszc6nP3zwocaGCIdBQSJYZQKxykHAnhTFo2AUjIJRMMIAANHaSZTsx3+MAAAAAElFTkSuQmCC","orcid":"","institution":"SYSOFT","correspondingAuthor":true,"prefix":"","firstName":"Byeongsoo","middleName":"","lastName":"Kang","suffix":""}],"badges":[],"createdAt":"2026-04-10 04:58:01","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-9374605/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9374605/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106960332,"identity":"bfb03752-7962-43b6-90fc-cc3365e99c34","added_by":"auto","created_at":"2026-04-15 09:20:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1920045,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptv252.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9374605/v1_covered_c3c180b7-37de-4250-ae36-d4c17d30c715.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eMethods for Continuous-Valued Training Data Generation from Genome-Scale Metabolic Models: Partial-Inhibition FBA with Mixed Essentiality Sampling, Applied to ESKAPE Drug Target Curation\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"SYSOFT","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":"antimicrobial resistance, flux balance analysis, partial gene inhibition, genome-scale metabolic model, artificial neural network, drug target discovery, ESKAPE pathogens, knowledge graph, toxicity prediction, open-source","lastPublishedDoi":"10.21203/rs.3.rs-9374605/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9374605/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e. Computational antimicrobial target discovery faces three methodological limitations: (i) knockout-only FBA yields binary phenotypes unsuitable for regression training, (ii) no experimentally labeled toxicity datasets exist at the gene-target level, and (iii) pipelines rarely report negative validation results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e. We describe a pipeline addressing each limitation. We introduce partial gene inhibition simulation (10-100% flux reduction) applied to mixed essential/non-essential gene sets (39 + 30 genes from 1,516 iML1515 genes), generating 945 continuous-valued FBA simulations as regression training targets (not independent drug response predictions). We describe two ANN architectures: a subsystem-structured ANN (61.5% parameter reduction over fully connected baselines) and a dual-head ANN for joint potency-toxicity regression. We propose an exploratory toxicity labeling heuristic (sequence homology 35%, pathway overlap 30%, conservation 20%, cross-reactivity 15%); weights are an initial proposal pending experimental calibration. These components are integrated with a Neo4j knowledge graph, local LLM literature mining (46% effective precision), and AlphaFold structural analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e. Applied to three ESKAPE pathogen models (iML1515, iYS1720, iYL1228), the pipeline curates 29 targets lacking approved therapeutics from 39 literature-validated essential genes. Sequence-based audit reveals 11 of 21 targets lack detectable human homologs; folA shows 30% identity to human DHFR2, consistent with known trimethoprim cross-reactivity. Prospective-style temporal validation (2020 cutoff) shows the composite scoring heuristic did not exceed a random baseline (F1 = 0.519, z = -0.99), establishing the pipeline as a hypothesis generation tool rather than a predictive model. Double knockout of essential gene pairs produced indistinguishable lethal phenotypes, indicating partial inhibition grids are required for meaningful combination scoring.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e. The methods -- partial inhibition FBA, two ANN architectures, multi-evidence toxicity labeling, and four-way integration -- are individually reusable. The complete pipeline (10-tab dashboard, 40 tests, all code) is released under MIT license at https://github.com/shoo99/ai-drug-target.\u003c/p\u003e","manuscriptTitle":"Methods for Continuous-Valued Training Data Generation from Genome-Scale Metabolic Models: Partial-Inhibition FBA with Mixed Essentiality Sampling, Applied to ESKAPE Drug Target Curation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-13 02:49:48","doi":"10.21203/rs.3.rs-9374605/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":"492e6774-0079-4904-981b-ea51c616051c","owner":[],"postedDate":"April 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66056732,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2026-04-13T02:49:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-13 02:49:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9374605","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9374605","identity":"rs-9374605","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}