Peptides play important roles in plant and animal physiology targeting various proteins including growth factors, ion channels, receptors, and enzymes. Many peptides exhibit a broad range of biological activities, all valuable starting points to treat human disorders . The peptide space is vast; a peptide sequence of length N could lead to 20N possible mutations solely with canonical residues. Adding the post-translational modifications and the number of mutations becomes astronomical. It is not practical to synthesize all sequences or even to investigate all functionally interesting variants. A central goal for computational peptide design is to create novel sequences that carry the underlying properties of natural peptides with defined structural and functional properties. Multiple bioinformatic approaches have proven to be useful in accelerating peptide design learning either from their sequences or their tridimensional structures . In addition, the automation of peptide synthesis on solid support or the heterologous expression of proteins across biological systems has reduced production costs, making peptide space exploration accessible. These in silico methods are predominantly learning from primary sequences from medium-large datasets, rather than their structures due to the high costs associated with solving structures experimentally . However, current sequence-based approaches do not systematically study post-translational modifications (PTMs) that can significantly affect the physicochemical, chemical, or biological properties of peptides .