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Abstract
This study investigated many cancer medicines using a wide range of degree sum-based topological indices and entropy. These numerical numbers, commonly referred to as topological indices or molecular descriptors, depict a substance's molecular structure. They have been successfully used to properly reflect different physicochemical properties in a number of Quantitative Structure-Property Relationship (QSPR) and Quanti-tative Structure-Activity Relationship (QSAR) research studies. The purpose of the study was to investigate the relationships between topological neighborhood indices and physicochemical properties using the QSPR model and linear re-gression methodology. We employed linear regression methodology within the QSPR model to examine the connections between physicochemical characteristics and topological neighborhood in-dices. The results revealed a significant correlation between the neighborhood indices un-der scrutiny and the physicochemical features of the potential drugs under investigation. As a result, both neighborhood topological indices and entropy demonstrate potential as valuable tools for future QSPR investigations when evaluating anticancer medi-cations.
Published Version
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