Mass spectrometry is an analytical technique used to determine the mass-to-charge ratio of ions. When combined with chromatography, it becomes a powerful tool for identifying molecules in chemical samples. Typically, the analysis of experimental spectra relies on comparing them to a well-maintained database of reference data. However, a significant challenge arises because existing spectral databases don't adequately cover the vast chemical space. To address this limitation, recent attention has shifted towards machine learning-based de-novo methods. These methods can directly derive the molecular structure from the mass spectrum. In this context, we introduce a novel approach that addresses a specific use case involving GC-EI-MS spectra. This case is particularly challenging because it lacks additional information from the initial stage of MS/MS experiments, which previous methods depend on.

Slides

TBA

Lecture recordings (2 parts)

Readings

1. https://doi.org/10.1021/acscentsci.9b00085  (NEIMS, forward model for data generation #1)

2. https://doi.org/10.1021/acs.analchem.2c02093  (RASSP, forward model for data generation #2)

3. https://doi.org/10.1101/2021.06.25.449969  (MassGenie, backward modelfo r spectra analysis #1)

4. https://doi.org/10.1038/s42004-023-00932-3  (Spec2Mol, backward model for spectra analysis #2)