Self-supervised learning from small-molecule mass spectrometry data

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Metabolomics

Nature Biotechnology

(2025)Cite this article

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A self-supervised approach to learning from 24 million spectra generates a foundation model for detecting and characterizing small molecules by tandem mass spectrometry.

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Fig. 1: Two stages of training DreaMS.

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Authors and Affiliations

  1. Department of Computer Science, University of Antwerp, Antwerp, Belgium

    Wout Bittremieux

  2. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    William Stafford Noble

  3. Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA

    William Stafford Noble

Corresponding author

Correspondence to
William Stafford Noble.

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Competing interests

The authors declare no competing interests.

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Cite this article

Bittremieux, W., Noble, W.S. Self-supervised learning from small-molecule mass spectrometry data.
Nat Biotechnol (2025). https://doi.org/10.1038/s41587-025-02677-x

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  • DOI: https://doi.org/10.1038/s41587-025-02677-x

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