In principle, however, many of the indices used to characterize numerically DNA representations are graph invariants that describe the distribution of nodes and/or node-node connections in these graphs. In the parlance of graph theory, many authors have referred to some of these indices as Topological Indices (TIs) and applications have been made not only to DNA sequences but also to proteins, viral surfaces, RNA secondary structures and small molecules [23, 24] . Consequently, the method is of more general application taking into consideration that the type of graph representations referred to above have been extended from DNA/ RNA to the study of other types of relevant biological sequences. In particular, González-Díaz et al. extended these representations to the study of protein sequences [25] and Mass Spectra outcomes of proteins and/or protein serum profiles in parasites [26] , toxicoproteomics and diagnosis of cancer patients [27, 28] . In any case, the various numerical parameters of DNA/RNA graph representations (TIs or otherwise) may be used not only to study sequence-sequence similarity but also to fit Quantitative Structure-Activity Relationship (QSAR) models. These QSAR connect structural information with the biological function of a molecular entity under study and may be used to predict unknown entries. Structure here refers not only to drug structure but also to DNA sequence, RNA sequence or secondary structure, and protein sequences or 3D structure [see review 28] .