Chemoinformatics - Theory, Practice and Products - B. Bunin, et al., (Springer, 2007) WW.pdf

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CHEMOINFORMATICS: THEORY, PRACTICE, & PRODUCTS
 
CHEMOINFORMATICS:
THEORY, PRACTICE, &
PRODUCTS
B. A. BUNIN
Collaborative Drug Discovery, San Mateo, CA, U.S.A.
B. SIESEL
Merrill Lynch & Co., San Francisco, CA, U.S.A.
G. A. MORALES
Telik Inc., Palo Alto, CA, U.S.A.
J. BAJORATH
Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany
663918891.002.png
A C.I.P. Catalogue record for this book is available from the Library of Congress.
ISBN-10 1-4020-5000-3 (HB)
ISBN-13 987-1-4020-5000-8 (HB)
ISBN-10 1-4020-5001-1 (e-book)
ISBN-13 987-1-4020-5001-5 (e-book)
Published by Springer,
P.O. Box 17, 3300 AA Dordrecht, The Netherlands.
www.springer.com
Printed on acid-free paper
All Rights Reserved
© 2007 Springer
No part of this work may be reproduced, stored in a retrieval system, or transmitted
in any form or by any means, electronic, mechanical, photocopying, microfilming, recording
or otherwise, without written permission from the Publisher, with the exception
of any material supplied specifically for the purpose of being entered
and executed on a computer system,
for exclusive use by the purchaser of the work.
 
TABLE OF CONTENTS
Foreword
ix
1. Chemoinformatics Theory
1
1.1
Chemoinformatics – What is it?
1
1.2
Chemo- versus Bio-informatics
2
1.3
Scientific Origins
4
1.4
Fundamental Concepts
4
1.4.1 Molecular descriptors and chemical spaces
4
1.4.2 Chemical spaces and molecular similarity
7
1.4.3 Molecular similarity, dissimilarity, and diversity
8
1.4.4 Modification and simplification of chemical spaces
9
1.5
Compound Classification and Selection
11
1.5.1 Cluster analysis
12
1.5.2 Partitioning
13
1.5.3 Support vector machines
16
1.6
Similarity Searching
17
1.6.1 Structural queries and graphs
17
1.6.2 Pharmacophores
18
1.6.3 Fingerprints
21
1.7
Machine Learning Methods
23
1.7.1 Genetic algorithms
23
1.7.2 Neural networks
24
1.8
Library Design
26
1.8.1 Diverse libraries
27
1.8.2 Diversity estimation
28
1.8.3 Multi-objective design
29
1.8.4 Focused libraries
29
1.9
Quantitative Structure-Activity Relationship Analysis
31
1.9.1 Model building
31
1.9.2 Model evaluation
32
1.9.3 3D-QSAR
33
1.9.4 4D-QSAR
34
1.9.5 Probabilistic methods
35
1.10 Virtual Screening and Compound Filtering
35
1.10.1 Biologically active compounds
35
 

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