On topological characterizations and computational analysis of benzenoid networks for drug discovery and development - A, Pradeepa; P, Arathi (Journal of molecular graphics & modelling, 2025)
Topological indices are numerical invariants that provide key insights into the structural properties of molecular graphs and are crucial in predicting physio-chemical and biological activities. This paper applies established computational methodologies for analyzing benzenoid networks and their application to polycyclic aromatic hydrocarbons (PAHs) through degree-based topological indices computed via M-polynomial and NM-polynomial approaches. By examining tessellations, including linear chain, hexagonal, rhomboidal, and triangular configurations alongside their line graphs, this work highlights the influence of molecular topology on biological activity. Notably, the line graph of hexagonal tessellations resembling Kagome structures exhibits the highest potential bioactivity, revealing additional connectivity patterns that offer a structured framework for early-stage drug discovery and potentially enhance the understanding of molecular interactions. These findings underscore the value of topological indices in identifying key structural features, reducing attrition rates in drug development, and improving screening technologies, contributing to efficient drug design. [Display omitted] •This study computes degree-based topological indices for various benzenoid networks, including linear chain, hexagonal, rhomboidal, and triangular tessellations, utilizing M-polynomial and NM-polynomial frameworks.•The findings demonstrate how the structural properties of polycyclic aromatic hydrocarbons (PAHs) influence their stability and biological activity, providing a framework for structure-based drug design.•Incorporating line graphs of the benzenoid networks, particularly hexagonal networks resembling Kagome structures, reveals additional connectivity patterns crucial for understanding molecular interactions.•The computed topological indices are applicable in cheminformatics and bioinformatics for predicting drug-likeness, optimizing molecular structures, and enhancing lead identification, ultimately improving drug design efficiency and reducing attrition rates.