Improvements in prediction models could be implemented in the future to obtain more effective features. In the last couple of years, transformers have rapidly become the dominant neural network architecture, including several applications in bioinformatics. For example, a transformer, in combination with a pretraining strategy, outperformed Prosit's recurrent neural network in predicting fragment ion intensities . Another way to improve the model performance is to use transfer learning. This is a process by which a neural network is first trained on a large dataset, after which it is adapted to another related task or dataset by finetuning it on a smaller training dataset. In this way, model performance can be optimized for specific datasets, even when only a limited amount of training data is available. Transfer learning is used by several deep learning tools and may help to create models better suited for different scenarios, such as finetuning them for different fragmentation mechanisms, instrument platforms, and even lab-specific data properties .