Additionally, physicochemical properties are used to compare, filter, and classify molecular structures of pharmaceutical interest. However, the main limitation of physicochemical properties is that these do not describe structural conformations, small chemical changes, or fold peptide differences. But, using consensus similarity metrics (that consider physicochemical and structural approaches) could be implemented to complement the description and comparison between peptide structures using e.g., considering different peptide features: 3D structure, topology, backbone structure, drug-like properties, amino acid sequence, molecular fingerprints, etc.). From a conceptual point of view, a combination of descriptors and structural representations commonly used in chemoinformatics and bioinformatics would provide a comprehensive picture of the peptides . For example, Plisson et al. recently demonstrated that it is possible to predict properties and design new peptide structures from the consensus description of known peptidic information . In that work, the authors remark that "not all similar peptides conserve necessarily similar properties". Such a highlight is related to the concept of "activity cliff" : a pair of compounds (e.g. peptides) with high structural similarity but large and unexpected potency difference.