QSPR analysis of monocarboxylic acids against degree and neighbourhood degree sum-based topological descriptors

Chemical Graph Theory is a novel discipline of mathematical science that emerged from modelling chemical struc-tures. In Chemical Graph Theory structural features of molecular graphs of chemical compounds and drugs are analysed using graph theory. It employs statistical methods to study the structure-property relations of the molecules. Topologically character-izing chemical structures enables us to analyse molecular properties and simulate unknown structures with the desired attributes. Topological indices are numerical parameters of a molecular graph that characterise the bonding topology of a molecule and are necessarily structure invariants. In this paper, we apply three different classes of indices, namely, reduced reverse, open and closed neighbourhood degree-sum-based descriptors. We first compute the mentioned descriptors for the family of Monocarboxylic acids and perform Quantitative Structure-Property Relationship ( QSPR) analysis using curvilinear and multi-linear regression models. The relationship of the topological indices to the molecules’ physicochemical properties is then derived. We demonstrate that the aforementioned indices provide accurate predictions of all the properties assessed for Monocarboxylic acids.