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    Researchers created a large library of novel compounds to improve the identification of metabolites in untargeted metabolomics. This new resource significantly enhances the matching of mass spectrometry/mass spectrometry (MS/MS) spectra, expanding our understanding of biochemical landscapes.

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    Area of Science:

    • Metabolomics
    • Biochemistry
    • Computational Biology

    Background:

    • Untargeted metabolomics relies on MS/MS libraries for structural annotation.
    • Current libraries cover only a small fraction of public MS/MS spectra, leaving many features unannotated.

    Purpose of the Study:

    • To address the gap in MS/MS spectral annotation by creating a comprehensive, biologically relevant compound library.
    • To improve the identification of known and novel metabolites in complex biological samples.

    Main Methods:

    • Synthesized over 100,000 compounds using reactions mimicking biochemical transformations.
    • Developed computational infrastructure for large-scale MS/MS spectral comparisons across repositories.
    • Integrated the novel compound library into existing spectral matching workflows.

    Main Results:

    • 91% of synthesized compounds were not found in existing databases.
    • The new library increased the reference-based match rate by 17.4%.
    • Over 60 million new MS/MS matches were generated, raising the global annotation rate to 8.1%.

    Conclusions:

    • The biologically inspired MS/MS library significantly enhances metabolite identification in untargeted metabolomics.
    • This framework expands the detectable biochemical landscape across diverse organisms.
    • Facilitates the discovery of novel metabolites, including drug conjugates and co-metabolites.