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Exploring Neighborhood Topological Descriptors for Quantitative Structure-property Relationship (QSPR) Analysis and
Tony Augustine1, Roy Santiago2, Sahaya Vijay Jeyaraj2
1Department of Mathematics, Nirmala College (Autonomous), Muvattupuzha, Ernakulam, Kerala, 686661, India.
Background:
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.
Objective:
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.
Methods:
We employed linear regression methodology within the QSPR model to examine the connections between physicochemical characteristics and topological neighborhood indices.
Results:
The results revealed a significant correlation between the neighborhood indices under scrutiny and the physicochemical features of the potential drugs under investigation.
Conclusion:
As a result, both neighborhood topological indices and entropy demonstrate potential as valuable tools for future QSPR investigations when evaluating anticancer medications.
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