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arXiv Machine Learning By Paul Krzakala, Gabriel Melo, Camille Lan\c{c}on, Charlotte Laclau, R\'emi Flamary, Etienne Th\'evenot, Florence d'Alch\'e-Buc

MSAlign: Aligning Molecule and Mass Spectra representations for Metabolite Identification

Read the original on arXiv Machine Learning →

The paper introduces MSAlign, a lightweight model that aligns frozen foundation models for mass spectra (DreaMS) and molecules (MolDeBERTa) to improve metabolite identification from MS/MS spectra. It presents a unified framework for representation alignment and contrastive learning, demonstrates that a score fusion strategy further boosts performance at minimal cost, and addresses evaluation challenges by quantifying distribution shift in data splitting strategies. All resources, including datasets, splits, and code, are publicly released to promote reproducible research.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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