However, both fields use computational methods to analyze and manipulate large datasets to identify patterns and relationships in the data. The main historic difference is that chemoinformatics studies small chemical structures (e.g. drugs and natural products), in contrast to bioinformatics which allows the study of more complex molecules (e.g. peptides and proteins). But, in recent years new methodologies and technologies have been reducing the gap between chemoinformatics and bioinformatics fields. For example, new molecular representations based on atom-connectivity allow the systematic study of complex molecules, that could be applied to mapping the structural diversity of peptides and may help to understand the roles of PTMs in their physicochemical properties or biological activities . Different computational strategies to develop peptides are based on the analysis of sequence alignments and physicochemical similarity metrics. . However, the lack of data limits the use of alignment algorithms and the classification and prediction of secondary structures . Fortunately, in the last ten years, new computational methods have contributed to decoding the structure-property relationships (SPR) of peptides (P-SPR) . For example, new approaches that depend on the sequence and/or features of peptides (e.g., machine-learning methods, the de novo design, linguistic modeling, pattern insertion methods, and genetic algorithms) , are new research opportunities to explore P-SPR and guide a new era of peptide-based drug design.