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. [ABSTRACT FROM AUTHOR]

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