Park, Jiwon Jo, Jeonghee Yoon, Sungroh 2024-04-04T05:31:48Z 2024-04-04T05:31:48Z 2024-04-04 2024-01 https://pubs.kist.re.kr/handle/201004/149589 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 GNN-based architecture to predict the mass spectra pattern utilizing the structural motif information of the molecule. MoMS-Net considers both a molecule and its substructures as a graph form, which facilitates the incorporation of long-range dependencies while using less memory compared to the graph transformer model. We evaluated our model over various types of mass spectra and showed the validity and superiority over the conventional models. English Nature Publishing Group Mass spectra prediction with structural motif-based graph neural networks Article 10.1038/s41598-024-51760-x 1 Scientific Reports, v.14, no.1 Scientific Reports 14 1 Y scie scopus 001146669200021 2-s2.0-85182482600 Multidisciplinary Sciences Science & Technology - Other Topics Article PEPTIDES