DFT based structural modeling of chemotherapy drugs via topological indices and curvilinear regression.

The study of thermodynamics and electronic structure of chemotherapy drug is crucial in developing effective cancer treatments. Quantitative Structure-Property Relationship (QSPR) analysis is an essential instrument in creating and enhancing chemotherapeutic drugs. This research employs Density Functional Theory (DFT) to compute thermodynamical and electronic characteristics of different chemotherapeutic drugs. Distance-based topological descriptors are utilized to assess the molecular structure of these chemotherapy drugs. These descriptors are subsequently employed in curvilinear regression models to forecast essential thermodynamical attributes and biological activities. We seek to improve the precision of QSPR models by correlating DFT-derived attributes with topological descriptors via curvilinear regression methods. Our results indicate that curvilinear regression models, especially those with quadratic and cubic curve fitting, markedly enhance the prediction capability for analyzing thermodynamical properties of drugs. Our findings further specify that Wiener index and Gutman index outperformed the indices in predicting the properties of drugs. This method offers an enhanced understanding of the thermodynamics of chemotherapeutic medicines and promotes the creation of more effective and safer therapeutic compounds. The findings could pave the way for more precise and personalised cancer treatment strategies, ultimately improving patient outcomes. The application of topological indices in QSPR modelling, which accounts for molecular symmetry, has significant promise in enhancing our comprehension of compounds' structural and thermodynamical characteristics.