Data Sheet 1_Computation of domination degree-based topological indices using python and QSPR analysis of physicochemical and ADMET properties for heart disease drugs.pdf

Heart disease is a leading cause of death worldwide, highlighting the need for effective treatments for hypertension, arrhythmias, and high cholesterol. This study applies chemical graph theory to analyze the properties of seventeen heart disease drugs by evaluating minimal dominating sets and counting node appearances in these sets. Using Python, six domination degree-based topological indices from the ϕd-polynomial are computed. Regression analysis, including curvilinear and multilinear models, identified correlations between these indices and the physicochemical and ADMET properties of these drugs. QSPR models are developed to assess the ability of these indices to predict key properties, offering insights into their effectiveness for drug design.