Calculation of topological indices along with MATLAB coding in QSPR analysis of calcium channel-blocking cardiac drugs

In this research, medications used for treating heart disease, specifically focusing on calcium channel blockers, were analyzed. A computer-based computing technique was used to simplify calculations and data analysis. Using MATLAB coding, we calculated their degree-based topological indices obtained from the M-polynomial. Various regression analyses were used to establish a relationship between these indices and the physicochemical features of the drugs. QSPR models were created to determine the effectiveness by correlating these indices with eight physicochemical features of the drugs. Confidence intervals were calculated at a 95% level for the intercept and slope in the linear regression models. The results indicate that the inverse sum indeg index (I) proved to be the most dependable indices for predicting boiling point, flashpoint, and enthalpy of vaporization. The symmetric division index (SDD) was effective in forecasting polarizability and molar refractivity, while the second modified Zagreb index ( \(^{m}M_{2}\) ${}^m{M}_2$ ) emerged as the best predictor for molar volume in linear, quadratic, and cubic regression models. Furthermore, the forgotten index (F) was identified as the top estimator for boiling point, flashpoint, enthalpy, and polar surface area in both quadratic and cubic regression models. Lastly, the SDD index, with a correlation coefficient of R = 1, is proposed as the most accurate estimator for the characteristics of polar surface area in quadratic, and cubic regression equations. Calculated feature values show a strong correlation with the actual values, indicating the indices’ reliable predictive capabilities.