A computational QSPR study of neurotransmitters using distance-based topological descriptors

Neurotransmitter systems are central to neuronal communication and play a pivotal role in the regulation of cognitive, emotional, and motor functions, while their dysfunction is closely associated with numerous neuropsychiatric and neurodegenerative disorders. In this work, graph-theoretic methodologies are employed to systematically investigate the structural characteristics of neurotransmitter-related molecular networks through the computation of the Mostar, Padmakar-Ivan, Szeged, and geometric-arithmetic indices. These topological descriptors quantitatively capture molecular symmetry, connectivity, and distance-based features, thereby enabling a rigorous assessment of structural heterogeneity, balance, and spatial organization. Furthermore, the computed topological indices are utilized to construct regression models for the prediction of key physicochemical properties, including molecular weight, polar surface area, structural complexity, solubility, and melting point. The predictive capability and reliability of the proposed models are evaluated using multiple graphical diagnostics, such as line plots, bar charts, violin plots, and scatter plots, providing a comprehensive validation of the developed structure–property relationships .