Exploring the Symmetry of Curvilinear Regression Models for Enhancing the Analysis of Fibrates Drug Activity through Molecular Descriptors

Quantitative structure-property relationship (QSPR) modeling is crucial in cheminformatics and computational drug discovery for predicting the activity of compounds. Topological indices are a popular molecular descriptor QSPR due to their ability concisely capture structural electronic properties molecules. Here, we investigate use curvilinear regression models analyze fibrates through topological indices, which modulate lipid metabolism improve profile. Our approach predicts physicochemical based on degrees distances from indices. results demonstrate that can enhance accuracy biological activities molecules, including drugs. We also conducted density functional theory (DFT) calculations investigated derivatives gain insights into optimized geometries properties, symmetry. The modeling, considers symmetry shows significant potential improving our understanding