Browsing by Author "Sudharsan, S."

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Mathematical Properties of Inverse Sum Index Eccentric Coindices of Graphs
(Abdus Salam School of Mathematical Sciences, 2024) Farahani, M.R.; Pattabiraman, K.; Sudharsan, S.; Patil, S.V.; Alaeiyan, M.; Cancan, M.
Essential and widely studied topological indices, including the well-known Zagreb indices (M1 and M2), and the newly proposed Inverse Sum Indeg Eccentricity Index (ξISI), To ensure the contributions of all edges within a graph are effectively considered. By emphasizing on the total eccentricity of non-adjacent vertices, Hua et al. introduced the eccentric connectivity coindex (ξc). Inspired by their contributions, we introduce the inverse sum indeg eccentric coindex (ξISI), which is defined as the ratio of the product of the eccentricities to the sum of the eccentricities for all isolated pair of vertex in a connected graph. This study primarily aims to establish various bounds for ξISI in finite simple graphs and derives the values of the proposed indices for two specific graph constructions. Additionally, we present a comprehensive set of relationships for ξISI using several graph products. © 2024 Abdus Salam School of mathematical Sciences. All rights reserved.
Qspr Modeling With Topological Indices of Some Potential Drugs Against Cancer
(Taylor & Francis Ltd, 2023) Pattabiraman, K.; Sudharsan, S.; Cancan, Murat
In Sri Lanka as well as the rest of the globe, cancer is the top cause of mortality. One of the key medicines in treating tumors is anticancer medications and delivery dendrimers. To prevent the formation of the rapid proliferation of cancer cells, several tests were carried out. Because of this, research on dendrimers and anti-cancer medications is crucial. Topological indices (TIs) are molecular descriptors numerical values corresponding to the physical characteristics of a molecule's chemical structure. It costs money to determine a molecule's physical characteristics in a lab since it takes a lot of materials, medications, and time. Therefore, the relevant information about molecules may be obtained by computing TIs. This study's goals are to compute hitherto uncalculated eccentricity-based TIs for various anticancer structures and to use curvilinear regression models to forecast the physical characteristics of particular anticancer medications. These anticancer medications were given different TIs developed in this work, allowing the researchers to understand the physical, physicochemical, and chemical characteristics related to them. In addition comparative study of the novel indices with some well-known and mostly used indices in structure-property modeling and anticancer drugs in performed.