Graph Applications in Chemoinformatics and Structural Bioinformatics

Graph Applications in Chemoinformatics and Structural Bioinformatics

Eleanor Joyce Gardiner (University of Sheffield, UK)
Copyright: © 2013 |Pages: 32
DOI: 10.4018/978-1-4666-3604-0.ch058
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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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Background

The first question to be answered is “What is chemoinformatics?” Chemoinformatics (also known as cheminformatics) is an interface science (N. Brown, 2009) which includes expertise from chemistry, biology, physics, mathematics and computer science. No definitive definition of the term “chemoinformatics” has ever been given but, as chemoinformaticians, we know what we mean when we say it. The term chemoinformatics was first used by Frank Brown:

The use of information technology and management has become a critical part of the drug discovery process. Chemoinformatics is the mixing of those information resources to transform data into information and information into knowledge for the intended purpose of making better decisions faster in the area of drug lead identification and optimization. (F. K. Brown, 1998).

Other definitions from leading practitioners in the field include:

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