However, because modified peptides can have different properties compared to their unmodified counterparts, predictions from deep learning tools trained on modified peptides are expected to perform better than simply shifting fragment ions and using the same retention time as for the unmodified peptide. With the use of transfer learning, the fragment ion intensity prediction tool pDeep2 was extended to include 22 PTMs. Notably, it is difficult to train good models for low-abundant PTMs, as unlike for common PTMs, it is more challenging to collect sufficient MS/MS spectra.