Sarwar Khadija, Kanwal Salma, Razzaque Asima
Department of Mathematics, Lahore College for Women University, Lahore, Pakistan.
Preparatory Year, Basic science, King Faisal University, Al-Ahsa, Saudi Arabia.
PLoS One. 2025 Apr 29;20(4):e0319029. doi: 10.1371/journal.pone.0319029. eCollection 2025.
Amino acids are chemical molecules that act as the building blocks of proteins and perform critical functions in biological processes. Their two main functional groups, an amino group (-NH2) and a carboxyl group (-COOH) as well as a changeable side chain (R group) that controls the unique characteristics of each amino acid are what define them. Because they can serve as building blocks for a variety of macromolecules and support biological activities in a variety of ways, amino acids have a wide range of uses in biology, medicine, industry and nutrition. Quantitative Structure-Activity/Property Relationships employ graph invariants to model physicochemical properties of substances. Topological indices are a reliable and computationally efficient technique to express molecular structures and properties, making them indispensable in theoretical and applied chemistry. Gourava indices are valuable mathematical tools that provide deeper insights into the topology and structure of networks and molecular graphs, resulting in improved decision-making and efficiency in research and applications. In this article, Gourva, hyper Gourava, alpha Gourava and gamma Gourava indices are presented and calculated. Curvilinear and multilinear regression models for predicting physicochemical characteristics of amino acids are analyzed.
氨基酸是作为蛋白质的组成部分并在生物过程中发挥关键作用的化学分子。它们的两个主要官能团，一个氨基（-NH2）和一个羧基（-COOH），以及一个控制每种氨基酸独特特性的可变侧链（R基团）定义了它们。由于氨基酸可作为各种大分子的组成部分并以多种方式支持生物活性，因此在生物学、医学、工业和营养领域有广泛用途。定量构效/构性关系利用图不变量来模拟物质的物理化学性质。拓扑指数是表达分子结构和性质的可靠且计算高效的技术，使其在理论化学和应用化学中不可或缺。古勒瓦指数是有价值的数学工具，能更深入地洞察网络和分子图的拓扑结构，从而在研究和应用中提高决策效率。本文介绍并计算了古勒瓦、超古勒瓦、α古勒瓦和γ古勒瓦指数。分析了用于预测氨基酸物理化学特性的曲线和多线性回归模型。
