This report describes a new set of macromolecular descriptors of relevance to nucleic acid QSAR/QSPR studies, nucleic acids’ quadratic indices. These descriptors are calculated from the macromolecular graph’s nucleotide adjacency matrix. A study of the interaction of the antibiotic Paromomycin with the packaging region of the RNA present in type-1 HIV illustrates this approach. A linear discriminant function gave rise to excellent discrimination between 90.10% (91/101) and 81.82% (9/11) of interacting/noninteracting sites of nucleotides in training and test set, respectively. The LOO crossvalidation procedure was used to assess the stability and predictability of the model. Using this approach, the classification model has shown a LOO global good classification of 91.09%. In addition, the model’s overall predictability oscillates from 89.11% until 87.13%, when n
varies from 2 to 3 in leave-n
-out jackknife method. This value stabilizes around 88.12% when n
was > 3. On the other hand, a linear regression model predicted the local binding affinity constants [log K (10-4
)] between a specific nucleotide and the aforementioned antibiotic. The linear model explains almost 92% of the variance of the experimental log K (R = 0.96 and s = 0.07) and LOO press statistics evidenced its predictive ability (q2
= 0.85 and scv = 0.09). These models also permit the interpretation of the driving forces of the interaction process. In this sense, developed equations involve short-reaching (k < 3), middle-reaching (4 < k < 9) and far-reaching (k = 10 or greater) nucleotide’s quadratic indices. This situation points to electronic and topologic nucleotide’s backbone interactions control of the stability profile of Paromomycin-RNA complexes. Consequently, the present approach represents a novel and rather promising way to chem & bioinformatics research.