Vertex-Edge-WeightedMolecular Graphs: A Study on Topological Indices and Their Relevanceto Physicochemical Properties of Drugs Used in Cancer Treatment

Quantitative structure–property relationship (QSPR) analysis plays a crucial role in predicting physicochemical properties and biological activities of pharmaceutical compounds, aiding in drug design and optimization. This study focuses on leveraging QSPR within the framework of vertex and edge-weighted (VEW) molecular graphs, exploring their significance in drug research. By examining 48 drugs used in the treatment of various cancers and their physicochemical properties, previous studies serve as a foundation for our research. Introducing a novel methodology for computing vertex and edge weights, we highlight the importance of considering atomic properties and interbond dynamics. Statistical analysis, employing linear regression models, reveals enhanced correlations between topological indices and the physicochemical properties of drugs. Comparison with previous studies on unweighted molecular graphs highlights the enhancements achieved with our approach.

Publication Details

Journal
Figshare
Published
2025-02-03
DOI
https://doi.org/10.1021/acs.jcim.4c02013.s001
Primary Topic
Graph theory and applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Vertex-Edge-WeightedMolecular Graphs: A Study on Topological Indices and Their Relevanceto Physicochemical Properties of Drugs Used in Cancer Treatment

Figshare
Graph theory and applications
article

Vertex-Edge-WeightedMolecular Graphs: A Study on Topological Indices and Their Relevanceto Physicochemical Properties of Drugs Used in Cancer Treatment

article en

Abstract

Quantitative structure–property relationship (QSPR) analysis plays a crucial role in predicting physicochemical properties and biological activities of pharmaceutical compounds, aiding in drug design and optimization. This study focuses on leveraging QSPR within the framework of vertex and edge-weighted (VEW) molecular graphs, exploring their significance in drug research. By examining 48 drugs used in the treatment of various cancers and their physicochemical properties, previous studies serve as a foundation for our research. Introducing a novel methodology for computing vertex and edge weights, we highlight the importance of considering atomic properties and interbond dynamics. Statistical analysis, employing linear regression models, reveals enhanced correlations between topological indices and the physicochemical properties of drugs. Comparison with previous studies on unweighted molecular graphs highlights the enhancements achieved with our approach.

Figshare
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Graph theory and applications
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