The design of novel anti-HIV compounds has now become a crucial area for scientists around the world. In this paper a new set of macromolecular descriptors (that are calculated from the macromolecular graph’s nucleotide adjacency matrix) of relevance to nucleic acid QSAR/QSPR studies, nucleic acids’ linear indices. A study of the interaction of the antibiotic Paromomycin with the packaging region of the HIV-1 Ψ-RNA has been performed as example of this approach. A multiple linear regression model predicted the local binding affinity constants [LogK (10⁻⁴M⁻¹)] between a specific nucleotide and the aforementioned antibiotic. The linear model explains more than 87% of the variance of the experimental LogK (R=0.93 and s=0.102×10⁻⁴M⁻¹) and leave-one-out press statistics evidenced its predictive ability (q²=0.82 and s_cv=0.108×10⁻⁴M⁻¹). The comparison with other approaches (macromolecular quadratic indices, Markovian Negentropies and ‘stochastic’ spectral moments) reveals a good behavior of our method.