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Graph Applications in Chemoinformatics and Structural Bioinformatics

Graph Applications in Chemoinformatics and Structural Bioinformatics

Eleanor Joyce Gardiner
Copyright: © 2012 |Pages: 35
ISBN13: 9781613500538|ISBN10: 161350053X|EISBN13: 9781613500545
DOI: 10.4018/978-1-61350-053-8.ch017
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MLA

Gardiner, Eleanor Joyce. "Graph Applications in Chemoinformatics and Structural Bioinformatics." Graph Data Management: Techniques and Applications, edited by Sherif Sakr and Eric Pardede, IGI Global, 2012, pp. 386-420. https://doi.org/10.4018/978-1-61350-053-8.ch017

APA

Gardiner, E. J. (2012). Graph Applications in Chemoinformatics and Structural Bioinformatics. In S. Sakr & E. Pardede (Eds.), Graph Data Management: Techniques and Applications (pp. 386-420). IGI Global. https://doi.org/10.4018/978-1-61350-053-8.ch017

Chicago

Gardiner, Eleanor Joyce. "Graph Applications in Chemoinformatics and Structural Bioinformatics." In Graph Data Management: Techniques and Applications, edited by Sherif Sakr and Eric Pardede, 386-420. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-053-8.ch017

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Abstract

The focus of this chapter will be the uses of graph theory in chemoinformatics and in structural bioinformatics. There is a long history of chemical graph theory dating back to the 1860’s and Kekule’s structural theory. It is natural to regard the atoms of a molecule as nodes and the bonds as edges (2D representations) of a labeled graph (a molecular graph). This chapter will concentrate on the algorithms developed to exploit the computer representation of such graphs and their extensions in both two and three dimensions (where an edge represents the distance in 3D space between a pair of atoms), together with the algorithms developed to exploit them. The algorithms will generally be summarized rather than detailed. The methods were later extended to larger macromolecules (such as proteins); these will be covered in less detail.

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