dIon: Fragmentation-Based Invariance for Self-Supervised Learning of Tandem Mass Spectra

We introduce a novel invariance for peptide tandem mass spectrometry data, unlocking self-supervised representation learning that improves de novo sequencing of peptides. This invariance exploits the physical relationship between precursor properties (mass and charge) and fragment-ion evidence, with