Noticably, most PMF databases only leverage one dimension of the acquired mass spectra, since only the peak list is passed to the search engine. While additional parameters, such as the selected protease and a possible variety of physicochemical properties, can be utilised to increase prediction performance, the peak intensities, which constitute the second dimension of the recorded spectra, are rarely considered. This is a possible entry point for much more sophisticated models, which could harness the potential of deep learning and artificial intelligence to predict peak intensities in mass spectra. This approach was first tested in 2002, where Gay et. al. used standard datamining methods to predict peak intensities to enhance the potential of peptide mass fingerprinting. While the training set in this study was rather small (containing 157 proteins), the achieved correlation was a clear indicator that this technique has the potential to elevate the one-dimensional mass fingerprint into a two-dimensional spectral fingerprint. Apart from this and other upstream-methods, like advanced noise reduction algorithms, machine learning could enable a fundamentally more complex approach to peptide mass fingerprinting. While the 'traditional' workflow was comprised of comparing peak (or spectral) datasets to existing databases, a more contemporary approach could use convolutionary neural networks to detect reoccuring motifs and complex clustered information in large datasets that would otherwise remain hidden from human intuition. Perhaps the most crucial aspect of artificial intelligence is the human-friendly visualisation and interpretation of the predicted results. Here, the use of principal component analysis (PCA, ) and tdistributed stochastic neighbour embedding (t-SNE, ) could project complex mass spectra in lowdimensionality spaces to identify motifs and clusters. Such have the potential to help researchers to draw connections between peptide mass fingerprints and biological functions in unprecedented ways.