基于 Python 的算法通过数学建模来优化磺胺类药物。

Department of Mathematics, University of Sialkot, Sialkot, 51310, Pakistan.

COMSATS University Islamabad Lahore Campus, Lahore, Pakistan.

Sci Rep. 2024 May 28;14(1):12264. doi: 10.1038/s41598-024-62819-0.

This article explores the structural properties of eleven distinct chemical graphs that represent sulfonamide drugs using topological indices by developing python algorithm. To find significant relationships between the topological characteristics of these networks and the characteristics of the associated sulfonamide drugs. We use quantitative structure-property relationship (QSPR) approaches. In order to model and forecast these correlations and provide insights into the structure-activity relationships that are essential for drug design and optimization, linear regression is a vital tool. A thorough framework for comprehending the molecular characteristics and behavior of sulfonamide drugs is provided by the combination of topological indices, graph theory and statistical models which advances the field of pharmaceutical research and development.

本文使用拓扑指数通过开发 Python 算法探索了代表磺胺类药物的十一个不同化学图的结构性质。为了发现这些网络的拓扑特征与相关磺胺类药物特性之间的显著关系，我们使用了定量构效关系（QSPR）方法。为了对这些相关性进行建模和预测，并深入了解对药物设计和优化至关重要的结构-活性关系，线性回归是一个重要工具。拓扑指数、图论和统计模型的结合为磺胺类药物的分子特性和行为提供了一个全面的理解框架，推进了药物研发领域的发展。