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Enhanced Mesh Network Using Novel Forgotten Polynomial Algorithm for Pharmaceutical Design

D. Ahila Jeyanthi*, T. M. Selvarajan

Noorul Islam Centre for Higher Education, Kumaracoil-629180, Tamilnadu, India

* Corresponding Author: D. Ahila Jeyanthi. Email:

Intelligent Automation & Soft Computing 2022, 33(1), 669-680.


The molecular structures are modelled as graphs which are called the molecular graphs. In these graphs, each vertex represents an atom and each edge denotes covalent bond between atoms. It is shown that the topological indices defined on the molecular graphs can reflect the chemical characteristics of chemical compounds and drugs. A large number of previous drug experiments revealed that there are strong inherent connections between the drug’s molecular structures and the bio-medical and pharmacology characteristics. The forgotten topological index is introduced to be applied into chemical compound and drug molecular structures, which is quite helpful for medical and pharmaceutical workers to test the biological features of new drugs. Such approaches are widely applied in developing areas which are not rich enough to afford the relevant equipment and chemical reagents. In this paper, deals with an interconnection network motivated by molecular structures. The different forms of silicate available in nature lead to the introduction of the dominating silicate network. The first section deals. In this paper, the distinct degrees of enhanced Mesh network, triangular Mesh network, star of silicate network, and rhenium trioxide lattice are listed with drug structure analysis and edge dividing technique, we determine the forgotten polynomial of some molecular structure. The second section contains an algorithm of forgotten polynomial. The third section deals with an application of forgotten polynomial of some drugs.


Cite This Article

D. Ahila Jeyanthi and T. M. Selvarajan, "Enhanced mesh network using novel forgotten polynomial algorithm for pharmaceutical design," Intelligent Automation & Soft Computing, vol. 33, no.1, pp. 669–680, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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