Discovery of Novel Anticancer Agents and Influenza Potential Biomarkers Through a Mass Spectrometry Foundation Model

Discovery of Novel Anticancer Agents and Influenza Potential Biomarkers Through a Mass Spectrometry Foundation Model | 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 Article Discovery of Novel Anticancer Agents and Influenza Potential Biomarkers Through a Mass Spectrometry Foundation Model Fei Xu, Junxiao Wang, Beilun Wang, Kaihong Huang, Zhaoguo Li, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7883708/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 Metabolomics, using non-targeted tandem mass spectrometry, generates rich biological data, but novel metabolites with structures absent from databases are challenging to analyze. Existing algorithms predict isolated chemical features such as molecular fingerprints or structural classes but fail to integrate them into reliable structure-level predictions, particularly for complex metabolites or spectra with high noise and sparse fragments. Here we present ComFaceID, a foundation model that revolutionizes de novo structure profiling through generating 500-dimensional embeddings from MS² spectra, enabling parallel prediction of diverse structural descriptors. ComFaceID consistently outperforms state-of-the-art tools across key tasks, including library search, classification, and fingerprint prediction, even under challenging conditions such as complex metabolomic backgrounds with noisy and information-poor spectra. By integrating outputs from these multi-task predictions, we further developed a multi-parameter framework which significantly enhances prioritization accuracy over single-parameter approaches. When applied to 3,334 actinomycete extracts, ComFaceID uncovered 6 novel compounds across 3 structural classes, including a unique hexahydroindolizine scaffold. Two compounds showed potent, broad-spectrum cytotoxicity superior to doxorubicin, with lower off-target toxicity. In an H1N1 infection model, the ComFaceID pipeline identified over 40 unannotated likely biomarkers, revealing pulmonary inflammation-induced gut metabolic remodeling marked by increased saturated fatty acyl phospholipids and reduced bile acids. By bridging spectral interpretation and novel metabolite discovery, ComFaceID establishes a new workflow for structure-informed metabolomics. Biological sciences/Biological techniques/Mass spectrometry Biological sciences/Chemical biology/Metabolomics Biological sciences/Chemical biology/Natural products Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SIDiscoveryofNovelAnticancerAgentsandInfluenzaPotentialBiomarkersThroughaMassSpectrometryFoundationModel.docx Discovery of Novel Anticancer Agents and Influenza Potential Biomarkers Through a Mass Spectrometry Foundation Model 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-7883708","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":534624193,"identity":"93350006-9911-4b59-931f-dc57f24160be","order_by":0,"name":"Fei 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