A graph-based computational approach for modeling physicochemical properties in drug design.

The efficacy and effectiveness of antibiotics and neuropathic drugs are essentially guided by their physicochemical properties governing stability, bioavailability, and therapeutic activity. This work utilises mathematical modelling and quantitative structure-property relationship (QSPR) analysis for predicting important physicochemical properties such as boiling point, enthalpy of vaporisation, flash point, and molar refraction of chosen antibiotics and neuropathic drugs. Modified degree-based topological indices are utilised as molecular descriptors for correlations between physicochemical functionality and molecular structure. Linear and quadratic forms are various forms of regression models employed for improved predictions. The findings exhibit excellent performance of quadratic models across all but one property compared to linear models, highlighted by significant statistical markers like high [Formula: see text] values and low error margins. These results highlight the potential use of topological descriptors in combination with sound mathematical frameworks for drug optimisation and early-stage screening.