The second solution, which is the most widely used, involves using database search procedures to assign peptides to observed spectra and then treating those assignments as ground truth. Critical to the success of this approach is the availability of statistically rigorous methods for controlling the false discovery rate (FDR) among a set of peptides detected via database search . Typically, datasets for training and validating de novo methods are generated using a 1% FDR threshold, applied at the PSM level. However, suboptimal settings during database searches can still lead to inaccurately labeled peptides. For instance, the nine-species benchmark data initially did not account for missassigned isotopic peaks , resulting in the incorrect identification of deamidated peptides from spectra for which the first isotopic peak rather than the mono-isotopic peak was used as the precursor m/z (Figure ). This error was only identified during the development of the Casanovo tool six years later , while other de novo tools had already been trained on this partially incorrect data in the meantime. Such inaccuracies in training data propagate the limitations of the database search tools used to generate these labels, emphasizing the importance of using the latest advances in spectrum annotation to produce the highest quality training data possible. Moreover, as de novo tools learn to approximate predictions of the database search tool used to generate the peptide labels, they inherit any limitations inherent to these tools. As discussed previously, a correct train/test split is crucial to prevent these tools from simply memorizing peptide sequences rather than learning to predict them accurately. Evaluating these tools on mass spectrometry data from heterogeneous sources, which have limited to no overlap in their proteomes, is one strategy to ensure robustness and generalizability.