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FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events

Author

Listed:
  • Yannek Nowatzky

    (Federal Institute for Materials Research and Testing (BAM)
    Freie Universität Berlin)

  • Francesco Friedrich Russo

    (Federal Institute for Materials Research and Testing (BAM)
    Freie Universität Berlin)

  • Jan Lisec

    (Federal Institute for Materials Research and Testing (BAM))

  • Alexander Kister

    (Federal Institute for Materials Research and Testing (BAM))

  • Knut Reinert

    (Freie Universität Berlin
    Max Planck Institute for Molecular Genetics)

  • Thilo Muth

    (Freie Universität Berlin
    Robert Koch Institute)

  • Philipp Benner

    (Federal Institute for Materials Research and Testing (BAM))

Abstract

Non-targeted metabolomics holds great promise for advancing precision medicine and biomarker discovery. However, identifying compounds from tandem mass spectra remains a challenging task due to the incomplete nature of spectral reference libraries. Augmenting these libraries with simulated mass spectra can provide the necessary references to resolve unmatched spectra, but generating high-quality data is difficult. In this study, we present FIORA, an open-source graph neural network designed to simulate tandem mass spectra. Our main contribution lies in utilizing the molecular neighborhood of bonds to learn breaking patterns and derive fragment ion probabilities. FIORA not only surpasses state-of-the-art fragmentation algorithms, ICEBERG and CFM-ID, in prediction quality, but also facilitates the prediction of additional features, such as retention time and collision cross section. Utilizing GPU acceleration, FIORA enables rapid validation of putative compound annotations and large-scale expansion of spectral reference libraries with high-quality predictions.

Suggested Citation

  • Yannek Nowatzky & Francesco Friedrich Russo & Jan Lisec & Alexander Kister & Knut Reinert & Thilo Muth & Philipp Benner, 2025. "FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events," Nature Communications, Nature, vol. 16(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-57422-4
    DOI: 10.1038/s41467-025-57422-4
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    References listed on IDEAS

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    1. Xavier Domingo-Almenara & Carlos Guijas & Elizabeth Billings & J. Rafael Montenegro-Burke & Winnie Uritboonthai & Aries E. Aisporna & Emily Chen & H. Paul Benton & Gary Siuzdak, 2019. "The METLIN small molecule dataset for machine learning-based retention time prediction," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
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