QSPR Analysis of Physicochemical Characteristics of Antithyroid Cancer Compounds Using Degree-Based Topological Indices

Degree-based topological indices and quantitative structure–property relationship (QSPR) modeling have become essential tools in medicinal chemistry and drug design. They provide a framework for understanding the relationship between chemical structure and physicochemical properties. In this study, topological indices derived from molecular graphs of antithyroid cancer drugs were employed as molecular descriptors to develop QSPR models using linear regression. Our analysis revealed several correlations within the dataset. We computed 95% confidence intervals for both the slope and intercept of the linear regression models. To ensure a reliable and unbiased assessment, leave-one-out cross-validation (LOO-CV) was applied to all models and to control the false-discovery rate in the correlation analyses, the Benjamini–Hochberg correction was applied. Additionally, based on correlations, we identified linear equations that describe the relationships between specific indices and physicochemical properties.