In the past few years deep learning (DL) has revolutionized machine learning research, achieving tremendous increases in performance on a variety of problems, ranging from image recognition to natural language processing. In bioinformatics deep neural networks have already been used to solve important problems in genomics, but they have not seen a lot of use in biological mass spectrometry (MS) yet. Nevertheless, there is a huge opportunity to apply DL to MS research as well. Here we show how powerful DL models can usher in a shift from a model-driven approach, using hard-coded rules based on expert knowledge, for example such as complex fragmentation rules, to a data-driven one, where underlying rules are automatically inferred by advanced machine learning models.