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Annual Review of Analytical Chemistry

Volume 18, 2025

Machine Learning in Small-Molecule Mass Spectrometry

  • Yuhui Hong1, Yuzhen Ye1 and Haixu Tang1
  • Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, Indiana, USA; email: [email protected]
  • Vol. 18:193-215 (Volume publication date May 2025)
  • First published as a Review in Advance on February 27, 2025
  • Copyright © 2025 by the author(s).
    This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See credit lines of images or other third-party material in this article for license information.

Abstract

Tandem mass spectrometry (MS/MS) is crucial for small-molecule analysis; however, traditional computational methods are limited by incomplete reference libraries and complex data processing. Machine learning (ML) is transforming small-molecule mass spectrometry in three key directions: () predicting MS/MS spectra and related physicochemical properties to expand reference libraries, () improving spectral matching through automated pattern extraction, and () predicting molecular structures of compounds directly from their MS/MS spectra. We review ML approaches for molecular representations [descriptors, simplified molecular-input line-entry (SMILE) strings, and graphs] and MS/MS spectra representations (using binned vectors and peak lists) along with recent advances in spectra prediction, retention time, collision cross sections, and spectral matching. Finally, we discuss ML-integrated workflows for chemical formula identification. By addressing the limitations of current methods for compound identification, these ML approaches can greatly enhance the understanding of biological processes and the development of diagnostic and therapeutic tools.

Keyword(s): molecular representation learning, retention time prediction, small-molecule identification, tandem mass spectra prediction
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2025-05-15
2025-06-16
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Machine Learning in Small-Molecule Mass Spectrometry
Annual Review of Analytical Chemistry 18, 193 (2025); https://doi.org/10.1146/annurev-anchem-071224-082157
/content/journals/10.1146/annurev-anchem-071224-082157
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