More recently, as in many other fields, de novo sequencing has seen rapid progress due to the introduction of deep learning methods. "Deep learning" refers to any machine learning algorithm that uses a multi-layer neural network . These methods typically possess a large number of trainable parameters and require a correspondingly large amount of training data. Deep learning has been successfully applied in various domains of mass spectrometry proteomics, including predicting fragment ion intensities , identifying peptide features in MS1 data , performing large-scale embedding and clustering of MS2 spectra , and predicting peptide properties . The first deep learning method for de novo sequencing, introduced in 2017, is DeepNovo , and it has since been followed by at least 25 additional deep learning methods (Table ). In addition to their superior performance, the rise of deep learning methods in mass spectrometry analysis can be attributed to three factors: the emergence of neural network architectures that are well suited for mass spectra and peptides, the development of hardware-including graphics processing units (GPUs)-that accelerate the parallel computations that comprise neural networks, and the availability of large-scale public data needed to train these models .