Knowledge Discovery and Data Mining
Presents many fundamental technologies and recent advances in data mining and knowledge discovery.
TABLE OF CONTENTS Knowledge Discovery and Data Mining
Chapter 11: Mining the Organic Compound Jungle A Functional Programming Approach
K. E. Burn-Thornton
Plymouth University, UK
J. Bradshaw
Glaxo Wellcome Research & Development, UK
11.1 Introduction
The main aim of this work was to conduct a feasibility trial to determine whether data mining had the potential to provide an enabling technology for the pharmaceutical industry, to provide researchers with the capability of determining the common key characteristics of compounds that determine their functionality, irrespective of compound size. A secondary aim of this work was to investigate whether the powerful lazy evaluation of the functional programming language, Gofer, could be applied to this task, to which it would appear to be ideally suited.
11.2 Decision-support Requirements in the Pharmaceutical Industry
Pharmaceutical companies are continually striving to determine the common key characteristics that determine the functionality of compounds (e.g. relief of asthma) so that they may continue to provide safe and effective medicines. This process is usually conducted in two phases [1]. During phase 1 compounds which possess the same functionality are identified; phase 2 consists of determining the common key characteristics of these compounds. This section describes the historical methods used for the task of determination of the common characteristics of compounds possessing the same functionality, as well as recent advances.
11.2.1 Graphical Comparison
Historically, the task of determining common key characteristics was carried out by visually comparing graphical representations of the structures of compounds (often stored in large databases) in order to determine key substructures [2]. This was often very time consuming but could be improved by comparison of the...
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