Akademik Veri Yönetim Sistemi
Journal of Chemistry, cilt.2026, sa.1, 2026 (SCI-Expanded, Scopus)
Topological indices are crucial in modeling of molecular structures of graphs and are also useful in correlation with physicochemical properties. In this paper, we propose the Euler Sombor coindex and Gourava Sombor coindex, and we derive bounds by using some graph invariants of a graph such as the degree of a vertex, the order, and the size of a graph. Such indices are used in the study of graphs and chemical compounds like polyamidoamine, graphene, carbon nanocones, and caterpillars. Based on a quantitative structure–property relationship (QSPR) approach, the usefulness of the nonlinear model obtained through these indices in predicting properties of benchmarking butane derivatives is demonstrated. By scaling them up to drug discovery in the current period, in particular with the aim to ocular diseases, this study shows the use to create predictive models for one out of the “big three” drug properties (refractive index, molar volume, and polarizability) using Gaussian process regression (GPR). Python was utilized for data manipulation and regression. The proposed coindices provide structurally meaningful descriptors and demonstrate promising predictive capability within the QSPR framework.