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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 Quantitative 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 regression methodology.
We employed linear regression methodology within the QSPR model to examine the connections between physicochemical characteristics and topological neighborhood indices.
The results revealed a significant correlation between the neighborhood indices under 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 medications.