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Pattern Discovery in Biomolecular DataTools, Techniques, and Applications$
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Jason T. L. Wang, Bruce A. Shapiro, and Dennis Shasha

Print publication date: 1999

Print ISBN-13: 9780195119404

Published to Oxford Scholarship Online: November 2020

DOI: 10.1093/oso/9780195119404.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 28 October 2021

Representation and Matching of Small Flexible Molecules in Large Databases of 3D Molecular Information

Representation and Matching of Small Flexible Molecules in Large Databases of 3D Molecular Information

Chapter:
(p.111) Chapter 7 Representation and Matching of Small Flexible Molecules in Large Databases of 3D Molecular Information
Source:
Pattern Discovery in Biomolecular Data
Author(s):

Isidore Rigoutsos

Daniel Platt

Publisher:
Oxford University Press
DOI:10.1093/oso/9780195119404.003.0013

In recent years, the need to process and mine available information repositories has been increasing. The variety of the data contained in the targeted databases has given rise to a variety of tools for mining them, and computers have assumed an increasingly important role in this process. One of the many domains in which this scenario has been repeated is that of the drug discovery and design process. Computers have helped researchers to quickly eliminate unlikely drug candidates, to home in on promising ones, and to shorten the lead-compound-search cycle. Researchers are helped in this multidisciplinary effort by accessing proprietary and public resources containing crystallography, nuclear magnetic resonance, toxicology, pharmacology, and other types of data. Using the computer to filter out unlikely candidates can greatly shorten the length of a cycle in this iterative process. Some scenarios encountered in the context of the drug design process include . . . (a) a pharmacophore model that has been proposed from several active molecules—one wishes to determine other molecules that either corroborate or refute the model; (b) a set of untested molecules that exhibit biological activity—one wishes to identify relationships between their 3D structure and the activity; (c) a ligand that has been proposed to be active in a certain conformation- -other molecules that mimic the ligand’s behavior are sought. . . . The common element in all of these cases is that they are in essence searches for member elements in one or more repositories, each of the elements having some desired properties or behavior. Let us take a step back and reexamine the problem we are trying to solve. Two basic elements of the problem are “representation” and “storage.” If answers to both of these questions are available, then one can implement a retrieval system the properties and behavior of which are directly related to those of the two basic elements. We begin with a body of knowledge D that consists of D data items {di / i = 1,..., D}. Each data item is represented by a set of k properties and their respective values.

Keywords:   Chemical compound, Magic vectors, NP-complete, Pharmacophore model, Subgraph isomorphism

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