Augustine Tony, Santiago Roy, Jeyaraj Sahaya Vijay, Azeem Mohamad
Viswajyothi College of Engineering and Technology, Department of Science and Humanities.
Vellore Institute of Technology University, School of Advanced Sciences, Department of Mathematics.
Curr Org Synth. 2025 Feb 10. doi: 10.2174/0115701794349166241217085334.
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.
本研究使用了多种基于度和的拓扑指数和熵来研究多种癌症药物。这些数值通常被称为拓扑指数或分子描述符，描绘了物质的分子结构。它们已成功用于在许多定量结构-性质关系（QSPR）和定量结构-活性关系（QSAR）研究中恰当地反映不同的物理化学性质。
本研究的目的是使用QSPR模型和线性回归方法研究拓扑邻域指数与物理化学性质之间的关系。
我们在QSPR模型中采用线性回归方法来检验物理化学特征与拓扑邻域指数之间的联系。
结果显示，所研究的邻域指数与所研究的潜在药物的物理化学特征之间存在显著相关性。
因此，在评估抗癌药物时，邻域拓扑指数和熵都显示出作为未来QSPR研究有价值工具的潜力。
