A challenge faced by the entire field of deep learning, including de novo sequencing, lies in interpreting these models once they have been trained. Traditional de novo sequencing methods employed algorithms, such as dynamic programming with respect to a spectrum graph , that associate amino acids with pairs of peaks with corresponding mass differences. Presumably, deep learning models are capable of learning these types of rules on the basis of the provided training data. However, explicitly revealing the logic behind any given prediction is non-trivial. A potentially significant step in this direction is the new π-xNovo model , which uses a multi-head attention mechanism that provides an after-the-fact explanation, in the form of an attention matrix, linking predicted amino acids to specific peaks in the spectrum.