Mass Spectra Prediction with Structural Motif-based Graph Neural
  Networks
Jiwon Park Jeonghee Jo Sungroh Yoon
Mass spectra, which are agglomerations of ionized fragments from targeted
molecules, play a crucial role across various fields for the identification of
molecular structures. A prevalent analysis method involves spectral library
searches,where unknown spectra are cross-referenced with a database. The
effectiveness of such search-based approaches, however, is restricted by the
scope of the existing mass spectra database, underscoring the need to expand
the database via mass spectra prediction. In this research, we propose the
Motif-based Mass Spectrum Prediction Network (MoMS-Net), a system that predicts
mass spectra using the information derived from structural motifs and the
implementation of Graph Neural Networks (GNNs). We have tested our model across
diverse mass spectra and have observed its superiority over other existing
models. MoMS-Net considers substructure at the graph level, which facilitates
the incorporation of long-range dependencies while using less memory compared
to the graph transformer model.