Predicting tuberculosis drug properties using extended energy based topological indices via a python driven QSPR approach
摘要
In the present work, the physicochemical characteristics of important anti-tuberculosis (TB) drugs such as isoniazid, pyrazinamide, ethambutol, ethionamide, linezolid, and levofloxacin are explored using extended energy-based topological indexes. Based on the molecules of the drugs, we calculate the extended energies of many widely recognized indexes such as Zagreb Second Index, Harmonic Index, Randic Index, Sombor Index, Reduced Sombor Index, and Average Sombor Index. All the calculations are done using Python, and the rigorous algorithmic implementation in the form of matrix formulation and computation of the eigenvalue is also given for reproducibility. We use the linear, quadratic, and logarithmic regression models to predict nine important physicochemical parameters: the boiling point, the melting point, the flash point, the molar refractivity, the polarizability, the molar volume, the molecular weight, the logarithm of the partition coefficient, and the surface area. Among the three models, the quadratic regression always yields the best predictability, as reflected in the largest coefficient of determination (