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Current Organic Synthesis

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ISSN (Print): 1570-1794
ISSN (Online): 1875-6271

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Research Article

Exploring Neighborhood Topological Descriptors for Quantitative Structure-property Relationship (QSPR) Analysis and Entropy Measures of Some Anti-cancer Drugs

Author(s): Tony Augustine*, Roy Santiago, Sahaya Vijay Jeyaraj and Mohamad Azeem

Volume 22, Issue 8, 2025

Published on: 10 February, 2025

Page: [934 - 943] Pages: 10

DOI: 10.2174/0115701794349166241217085334

Price: $65

Abstract

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.

Keywords: Neighborhood degree-based topological indices, graph-theoretical approach, entropy, anticancer drugs.

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