Predicting tuberculosis drug properties using extended energy based topological indices via a python driven QSPR approach

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 ([Formula: see text]) as well as the minimum root mean square error (RMSE) values. Visual analyses such as heatmaps, scatter plot matrices, bar charts, and regression plots are employed to complement the numerical findings. Also, a rigorous discourse about model validity, model significance, and limitations is discussed. The entire source code and dataset are made available through GitHub to allow verification and transparency. The Python-based QSPR methodology, in addition to elucidating the high correlation of the topological descriptors with the properties of drugs, offers a drug design and optimization process in pharmaceutical research in an efficient way.