ArticleNature biotechnology2026
Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS.
Article in Nature biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Computational metabolomics at scale: from open data to insight.Current opinion in biotechnology · 2026Review
- Integrated metabolomics data analysis to generate mechanistic hypotheses with MetaProViz.Molecular systems biology · 2026Article
- Systematic mass-spectrometry-guided metabolic fingerprinting elucidates diversity of specialized metabolites across the Brassicaceae.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- X-Align: annotation-independent cross-platform alignment of untargeted metabolomics features.Chemical science · 2026Article
- DeepMASS v.2: An enhanced deep learning platform for large-scale discovery and structural annotation of unknown plant metabolites.Plant communications · 2026Article
- Foundation Models for Liquid Chromatography-High-Resolution Mass Spectrometry: A New Era beyond Labeled Datasets.Analytical chemistry · 2026Article
- Metabolism as the biochemical language for mechanistic biomedical AI.Nature metabolism · 2026Article
- Characterizing the effect of short wavelengths on the floral flavonoid metabolome of medicinal cannabis using a comparative computational metabolomics workflow.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Foundation model enables interpretable open and error-tolerant searching for mass spectrometry-based proteomics.Bioinformatics (Oxford, England) · 2026Article
- Benchmarking MS/MS Featurization Strategies for Machine Learning-Driven Metabolite Structure Annotation.Journal of the American Society for Mass Spectrometry · 2026Article
- Agentic AI for Structural Elucidation and Discovery of Drug Metabolites from Mass Spectrometry Data.bioRxiv : the preprint server for biology · 2026Article
- Advancing the Discovery of Emerging Contaminants: A Leap in Technology and Data.Environmental science & technology · 2026Review
- The Language of Elution: Autoregressive Prediction of the Next Feature in Untargeted LC-HRMS Lipidomics.ArXiv · 2026Article
- Why machine learning fails at mass spectrometry for small molecules.Nature metabolism · 2026Article
- Data-Driven Filter for Detector Oscillation Artifacts in Time-of-Flight Mass Spectrometry.Analytical chemistry · 2026Article
- Visible-Near-Infrared Hyperspectral Imaging Enables Nondestructive Identification of Bean Accessions via 1D Spectral Reflectance Analysis.ACS omega · 2026Article
- Peak2Patch: High-Fidelity Functional Group Identification through Attention-Based Fusion of Infrared and Mass Spectra.ACS omega · 2026Article
- Combating Antibacterial Resistance: The Integrative Role of Artificial Intelligence in Bio-Based Product Development.Antibiotics (Basel, Switzerland) · 2026Review
- Decoding the Oxylipin Chemical Space Using Ion Identity Molecular Networking.Analytical chemistry · 2026Article
- Persistence Assessment of Chemicals: Trajectories toward New Approach Methodologies (P-NAMs).Environmental science & technology · 2026Review
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Authors and funding
6 authors.
Funding
Abstract
Characterizing biological and environmental samples at a molecular level primarily uses tandem mass spectroscopy (MS/MS), yet the interpretation of tandem mass spectra from untargeted metabolomics experiments remains a challenge. Existing computational methods for predictions from mass spectra rely on limited spectral libraries and on hard-coded human expertise. Here we introduce a transformer-based neural network pre-trained in a self-supervised way on millions of unannotated tandem mass spectra from our GNPS Experimental Mass Spectra (GeMS) dataset mined from the MassIVE GNPS repository. We show that pre-training our model to predict masked spectral peaks and chromatographic retention orders leads to the emergence of rich representations of molecular structures, which we named Deep Representations Empowering the Annotation of Mass Spectra (DreaMS). Further fine-tuning the neural network yields state-of-the-art performance across a variety of tasks. We make our new dataset and model available to the community and release the DreaMS Atlas-a molecular network of 201 million MS/MS spectra constructed using DreaMS annotations.
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