Predicting the physicochemical properties of drugs for the treatment of Parkinson's disease using topological indices and MATLAB programming

In this study, twelve drugs used to treat Parkinson's disease were analyzed. To simplify calculations and data analysis, a computer-based computing technique along with the algorithm has been employed. The M-polynomial and their degree-based topological indices derived from the M-polynomial were calculated using MATLAB coding. Linear, quadratic, cubic, logarithmic, inverse, power, compound, s-curve, growth, and exponential regression model analyses were utilized to create QSPR models between the topological indices and eight the Physicochemical properties of the drugs to determine their effectiveness. Confidence intervals at a 95% level were computed for both the slope and intercept of the linear regression models. Also, based on the maximum R-2, optimal equations for estimating the Boiling point, Enthalpy of vaporization, Molar refractivity, Polarizability, and Molar volume using different indices have been determined, and linear, quadratic, and cubic equations have been specified. Calculated feature values are strongly correlated with actual values, indicating reliable predictive capabilities of the indices. For statistical analysis and to determine if there is a significant difference between the averages of the two groups, we used either an independent T-test or Welch's T-test. The results show that the p-value is less than 0.05, which indicates that the mean difference between the samples is statistically significant.