IDSL_MINT: a deep learning framework to predict molecular fingerprints from mass spectra

Sadjad Fakouri Baygi, Dinesh Kumar Barupal

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


The majority of tandem mass spectrometry (MS/MS) spectra in untargeted metabolomics and exposomics studies lack any annotation. Our deep learning framework, Integrated Data Science Laboratory for Metabolomics and Exposomics—Mass INTerpreter (IDSL_MINT) can translate MS/MS spectra into molecular fingerprint descriptors. IDSL_MINT allows users to leverage the power of the transformer model for mass spectrometry data, similar to the large language models. Models are trained on user-provided reference MS/MS libraries via any customizable molecular fingerprint descriptors. IDSL_MINT was benchmarked using the LipidMaps database and improved the annotation rate of a test study for MS/MS spectra that were not originally annotated using existing mass spectral libraries. IDSL_MINT may improve the overall annotation rates in untargeted metabolomics and exposomics studies. The IDSL_MINT framework and tutorials are available in the GitHub repository at . Scientific contribution statement. Structural annotation of MS/MS spectra from untargeted metabolomics and exposomics datasets is a major bottleneck in gaining new biological insights. Machine learning models to convert spectra into molecular fingerprints can help in the annotation process. Here, we present IDSL_MINT, a new, easy-to-use and customizable deep-learning framework to train and utilize new models to predict molecular fingerprints from spectra for the compound annotation workflows.

Original languageEnglish
Article number8
JournalJournal of Cheminformatics
Issue number1
StatePublished - Dec 2024


  • Deep learning
  • LipidMaps
  • Lipidomics
  • Mass spectrometry
  • Metabolomics
  • Molecular fingerprint descriptor
  • PyTorch
  • Transformer


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