{"title":"On Graph theoretic index of Complementary Benzenoids And Macromolecules","authors":"J. Senbagamalar","doi":"10.2174/1574362414666181226154935","DOIUrl":null,"url":null,"abstract":"\n\nA topological index of a graph G is a numerical parameter related to G\nwhich characterizes its molecular topology. In the field of QSAR and QSPR research, theoretical\nproperties of the chemical compounds and their molecular topological indices such as distance\nconnectivity indices and degree connectivity indices are used to predict the bioactivity of different\nmolecular compounds.\n\n\n\nSuch an approach is different from the traditional QSAR methodology,\nwhere one employs selected simpler physico-chemical properties to predict biological activities of\nmolecules. In order to obtain the structure-activity relationships in which theoretical and computational\nmethods are necessary to find appropriate representations of the molecular structure of\nchemical compounds. These representations are realized through the molecular descriptors. Molecular\ndescriptors are numbers containing structural information derived from the structural representation\nused for molecules under study.\n\n\n\nA topological index defined on molecular structure G can be considered as a real valued\nfunction\nf :G→ R+ which maps each durg molecular structure to certain real numbers. Graphene\nsheets are composed of carbon atoms linked in hexagonal shapes with each carbon atom covalently\nbonded to three other carbon atoms. Each sheet of graphene is only one atom thick and each\ngraphene sheet is considered a single molecule. Graphene has the same structure of carbon atoms\nlinked in hexagonal shapes to form carbon nanotubes, but graphene is flat rather than cylindrical..\nThis paper addresses the problem of computing the Wiener , First Zagreb index and Forgotten index\nof Complementary graphs of graphene sheets, triangular benzenoid graph, circumcoronene\nmolecular graph and nanostar dendrimers.\n\n\n\nThe line graphs were used for modeling amino acid sequences of proteins and of the\ngenetic code. The connected graphs are isomorphic to self complementary graphs. Recently, molecular\ngraphs have proved to be highly useful for drugs activity. Non empirical parameters of\nchemical structures derived from graph theoretic formalisms are being widely used by many researchers\nin studies pertaining to molecular design, pharmaceutical drug-design, and environmental\nhazard assessment of chemicals.\n","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2174/1574362414666181226154935","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Signal Transduction Therapy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/1574362414666181226154935","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 2
Abstract
A topological index of a graph G is a numerical parameter related to G
which characterizes its molecular topology. In the field of QSAR and QSPR research, theoretical
properties of the chemical compounds and their molecular topological indices such as distance
connectivity indices and degree connectivity indices are used to predict the bioactivity of different
molecular compounds.
Such an approach is different from the traditional QSAR methodology,
where one employs selected simpler physico-chemical properties to predict biological activities of
molecules. In order to obtain the structure-activity relationships in which theoretical and computational
methods are necessary to find appropriate representations of the molecular structure of
chemical compounds. These representations are realized through the molecular descriptors. Molecular
descriptors are numbers containing structural information derived from the structural representation
used for molecules under study.
A topological index defined on molecular structure G can be considered as a real valued
function
f :G→ R+ which maps each durg molecular structure to certain real numbers. Graphene
sheets are composed of carbon atoms linked in hexagonal shapes with each carbon atom covalently
bonded to three other carbon atoms. Each sheet of graphene is only one atom thick and each
graphene sheet is considered a single molecule. Graphene has the same structure of carbon atoms
linked in hexagonal shapes to form carbon nanotubes, but graphene is flat rather than cylindrical..
This paper addresses the problem of computing the Wiener , First Zagreb index and Forgotten index
of Complementary graphs of graphene sheets, triangular benzenoid graph, circumcoronene
molecular graph and nanostar dendrimers.
The line graphs were used for modeling amino acid sequences of proteins and of the
genetic code. The connected graphs are isomorphic to self complementary graphs. Recently, molecular
graphs have proved to be highly useful for drugs activity. Non empirical parameters of
chemical structures derived from graph theoretic formalisms are being widely used by many researchers
in studies pertaining to molecular design, pharmaceutical drug-design, and environmental
hazard assessment of chemicals.
期刊介绍:
In recent years a breakthrough has occurred in our understanding of the molecular pathomechanisms of human diseases whereby most of our diseases are related to intra and intercellular communication disorders. The concept of signal transduction therapy has got into the front line of modern drug research, and a multidisciplinary approach is being used to identify and treat signaling disorders.
The journal publishes timely in-depth reviews, research article and drug clinical trial studies in the field of signal transduction therapy. Thematic issues are also published to cover selected areas of signal transduction therapy. Coverage of the field includes genomics, proteomics, medicinal chemistry and the relevant diseases involved in signaling e.g. cancer, neurodegenerative and inflammatory diseases. Current Signal Transduction Therapy is an essential journal for all involved in drug design and discovery.