De novo sequencing of proteomics tandem mass spectrometry data, in which observed fragmentation spectra are translated into corresponding peptide sequences, has been an open challenge for more than 40 years . Recently, as in many other areas of science, considerable progress toward solving this challenge has been made using deep learning, in which multi-layer neural networks with millions of parameters are trained to generate peptide sequences from observed spectra. The first such deep learning method, DeepNovo , has been followed by at least 22 additional publications (reviewed in ).