Advanced Computer-Assisted Techniques in Drug Discovery, Second Edition

361 Pages·1995·6.28 MB·english

by  Han van de Waterbeemd

Advanced Computer-Assisted
Techniques in Drug Discovery
edited by Han van de Waterbeemd
Methods and Principles
in Medicinal Chemistry
Edited by
R. Mannhold
P. Krogsgaard-Larsen
H. Timmerman
Volume 1
Hugo Kubinyi,
QSAR: Hansch Analysis and Related Approaches
Volume 2
Han van de Waterbeemd (ed.),
Chemometric Methods in Molecular Design
Volume 3
Han van de Waterbeemd (ed.),
Advanced Computer- Assisted Techniques in
Drug Discovery
Methods and Principles in
Medicinal Chemistry
edited by R. Mannhold, P. Krogsgaard-Larsen, H. Timmerman
This practice-oriented series of handbooks and monographs introduces the reader to basic
principles and state-of-the-art methods in medicinal chemibtry. Topics treated in-depth
include
W chemical propertiesofdrugs
W characterization of biological activity
W advanced techniques in QSAR
W physiological and biochemical understanding of diseases
Volume I In prepamtion:
Kubinyi,H. H.-D. Holtje, G. Folkers
QSAR: Hansch Analysis and Molecular Modeling and
Related Approaches Drug Design
An Introductory Handbook
1993. XI], 240 pages with 60 figures and
32 tables. Hardcover. - Winter l995/6 -
DM 164.00.
ISBN 3-527-30035-X
(VCH, Weinheim) V. Pliska, B. Testa, H. van de Waterbeemd
(eds.)
Lipophilicity in Drug Research
Volume 2 and Toxicology
van de Waterbeemd, H. (ed.) -Winter 1995/6 -
Chemometric Methods in
Molecular Design
1995. Ca 300 pages. Hardcover.
Ca DM 178.00. 4b
ISBN 3-527-30044-9
(VCH, Weinheim)
VCH
Advanced Computer-
Assisted Techniques in
Drug Discovery
edited by Han van de Waterbeemd
Weinheim New York
Base1 Cambridge Tokyo
Volume editor: Prof. Povl Krogsgaard-Larsen
Dr. Han van de Waterbeemd Dept. of Organic Chemistry
F. Hoffmann - La Roche Ltd. Royal Danish School of Pharmacy
Pharma Research New Technologies DK-2100 Copenhagen
CH-4002 Basel Denmark
Switzerland
Editors:
Prof. Raimund Mannhold Prof. Hendrik Timmerman
Biomedical Research Center Faculty of Chemistry
Molecular Drug Research Group Dept. of Pharmacochemistry
Heinrich- Heine-Universitat Free University of Amsterdam
UniversitatsstraRe 1 De Boelelaan 1083
D-40225 Diisseldorf NL-1081 HV Amsterdam
Germany The Netherlands
I I
This book was carefully produced. Nevertheless, authors, editors and publisher do not warrant
the information contained therein to be free of errors. Readers are advised to keep in mind that
statements, data, illustrations, procedural details or other items may inadvertently be inaccurate.
Published jointly by
VCH Verlagsgesellschaft mbH, Weinheim (Federal Republic of Germany)
VCH Publishers, Inc., New York, NY (USA)
Editorial Director: Dr. Thomas Mager
Production Manager: Dip1.-Ing. (FH) Hans Jorg Maier
Library of Congress Card No. applied for.
British Library Cataloguing-in-Publication Data: A catalogue record for this book is available
from the British Library.
Deutsche Bibliothek Cataloguing-in-Publication Data:
Advanced computer assisted techniques in drug discovery I ed.
by Han van de Waterbeemd. - Weinheim ; New York ; Basel ; Cambridge ; Tokyo : VCH, 1994
(Methods and principles in medicinal chemistry ;V ol. 3)
ISBN 3-527-29248-9
NE: Waterbeemd, Han van de [Hrsg.]; GT
0 VCH Verlagsgesellschaft mbH. D-6945 I Weinheim (Federal Republic of Germany), 1995
Printed on acid-free and chlorine-free paper.
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Preface
The main objective of this series is to offer a practice-oriented survey of techniques
currently used in Medicinal Chemistry. Following the volumes on Hansch analysis
and related approaches (Vol. 1) and multivariate analyses (Vol. 2), the present hand-
book focuses on some new, emerging techniques in drug discovery; emphasis is plat-
ed on showing users how to apply these methods and to avoid time-consuming and
costly errors.
Four major topics are covered. The first centers on three-dimensional QSAR, and
some of the enormous progress achieved in this field is summarized. Both the various
3D-QSAR methods available as well as the chemometric tools for handling the statis-
tical problems involved in 3D-QSAR studies are covered.
Intimately coupled with 3D-QSAR is the current trend in pharmaceutical industry
to establish chemical structure databases as a tool for identifying new leads. Cor-
respondingly, in the second section, problems encountered in our understanding of
molecular similarity and aspects of compound selection by clustering databases are
treated.
The third section covers advanced statistical techniques in drug discovery. Inter
alia the approach of Svante Wold to apply PLS to non-linear structure-activity rela-
tions deserves to be mentioned here.
Last but not least, the use of neural networks for data analysis in QSAR problems
is discussed. Advantages and disadvantages are critically analysed by comparing net-
works versus statistics.
The editors would like to thank all contributors and VCH publishers for their
fruitful cooperation.
Summer 1994
Diisseldorf Raimund Mannhold
Kopenhagen Povl Krogsgaard-Larsen
Amsterdam Hendrik Timmerman
A Personal Foreword
It is no coincidence that the first three volumes of Methods and Principles in
Medicinal Chemistry deal with computer-assisted medicinal chemistry. After the
classical Hansch method in Volume 1 and applications of chemometric methods in
Volume 2, the present volume of the series contains a number of emerging new tech-
niques. Of course, all approaches using molecular modeling techniques, such as
structure-based design and de novo design, rely on computers as well. These will be
treated separately in a forthcoming volume.
This volume is a logical continuation of Volume 2. In fact, after analyzing the
methods that have been developed following the Hansch method, we came to the
conclusion that a number of these techniques have now matured, while others still
require further developments. This criterion was used to select the chapters for
Volumes 2 and 3.
In reviewing the contents of the first three volumes in this series, it is evident that
highly specialized tools have become available for the analysis of complex biological
and chemical data sets in order to unravel quantitative structure-activity relation-
ships. It has not become easier for the bench chemist to select the ideal method for
dealing with the analysis of structure-activity relationships using chemical and bio-
logical data. Specialist support is required to validate and apply statistical or chemo-
metric and other computer-assisted tools. Volume 3 focusses very much on the
newest methods employed by the chemometrician. We hope that, in an indirect way,
some of the methods discussed will be of use to molecular design on a day to day
basis.
I am grateful to, and would like to thank all the contributing authors for their
efforts in compiling this volume.
February 1994, Base1 Han van de Waterbeemd
Contents
Preface ........................................................... V
A Personal Foreword ............................................... VI
1 Introduction ............................................... 1
H . van de Waierbeemd
1.1 3D QSAR .................................................. 1
1.2 Databases .................................................. 4
1.3 Progress in Multivariate Data Analysis ......................... 4
1.4 Scope of this Book .......................................... 5
References ......................................................... 6
2 3D QSAR: The Integration of QSAR with Molecular
Modeling .................................................. 9
2.1 Chemometrics and Molecular Modeling ........................ 9
D. Piiea. c! Cosenfino. G. Moro. L . Bonaii. E . Fraschini.
M . Lasagni and R . Todeschini
2.1 .1 Introduction ................................................ 10
2.1.2 QSAR Methodology using Molecular Modeling and Chemometrics 11
2.1.2.1 Search for the Geometric Pharmacophore ...................... 13
2.1.2.2 Quantitative Correlation between Molecular Properties
and Activity ................................................ 16
2.1.2.3 Computer Programs ......................................... 18
2.1.3 Illustrative Examples ......................................... 18
2.1.3.1 Amnesia-Reversal Compounds ................................. 18
2.1.3.2 Non-Peptide Angiotensin I1 Receptor Antagonists ............... 21
2.1.3.3 HMG-CoA Reductase Inhibitors ............................... 25
2.1.3.4 Antagonists at the 5-HT3 Receptor ............................. 28
2.1.3.5 Polychlorinated Dibenzo-p-dioxins ............................. 32
2.1.4 Conclusions ................................................. 35
References ......................................................... 36
X Contents
2.2 3D QSAR Methods .......................................... 39
A.M. Davis
2.2.1 Introduction ................................................ 39
2.2.2 3D QSAR of a Series of Calcium Channel Agonists ............. 41
2.2.2.1 Molecular Alignment ......................................... 43
2.2.2.2 Charges .................................................... 45
2.2.2.3 Generating 3D Fields ......................................... 45
2.2.2.4 Compilation of GRID Maps .................................. 47
2.2.2.5 Inclusion of Macroscopic Descriptors with 3D Field Data ........ 48
2.2.3 Statistical Analysis ........................................... 49
2.2.3.1 Results of the Analysis ....................................... 51
2.2.3.2 Testing the Model ........................................... 56
2.2.4 Conclusions ................................................. 57
References ......................................................... 59
2.3 GOLPE Philosophy and Applications in 3D QSAR ............. 61
G. Cruciani and S . Clementi
2.3.1 Introduction ................................................ 61
2.3.1.1 3D Molecular Descriptors and Chemometric Tools ............... 63
2.3.1.2 Unfolding Three-way Matrices ................................. 64
2.3.2 The GOLPE Philosophy ...................................... 65
2.3.2.1 Variable Selection ............................................ 68
2.3.3 Applications ................................................ 70
2.3.3.1 PCA on the Target Matrix .................................... 71
2.3.3.2 PCA on the Probe Matrix .................................... 73
2.3.3.3 PLS Analysis on the Target Matrix ............................ 76
2.3.3.4 PLS on Target Matrix as a Strategy
to Ascertain the Active Conformation .......................... 78
2.3.3.5 GOLPE with Different 3D Descriptors ......................... 81
2.3.4 Conclusions and Perspectives .................................. 82
References ......................................................... 87
3 Rational Use of Chemical and Sequence Databases .......... 89
3.1 Molecular Similarity Analysis: Applications in Drug Discovery .... 89
M . A . Johnson. G . M . Muggiora. M . S . Lujiness. J. B. Moon.
J D . Petke and D. C. Rohrer
3.1.1 Introduction ................................................ 89
3.1.2 Similarity-Based Compound Selection .......................... 91
3.1.2.1 Similarity Measures and Neighborhoods ........................ 91
Contents XI
3.1.2.2 Application of 2D and 3D Similarity Measures .................. 94
3.1.2.3 Application of Dissimilarity-Based Compound Selection for Broad
Screening ................................................... 95
3.1.3 Structure-Activity Maps (SAMs) ............................... 96
3.1.3.1 A Visual Analogy ........................................... 96
3.1.3.2 Representing Inter-Structure Distances .......................... 97
3.1.3.3 Structure Maps .............................................. 99
3.1.3.4 Coloring a Structure Map .................................... 101
3.1.4 Field-Based Similarity Methods ................................ 102
3.1.4.1 Field-Based Similarity Measures ............................... 103
3.1.4.2 Field-Based Molecular Superpositions .......................... 104
3.1.4.3 An Example of Field-Based Fitting: Morphine and Clonidine ..... 105
3.1.5 Conclusions ................................................. 108
References ......................................................... 109
3.2 Clustering of Chemical Structure Databases for Compound
Selection ................................................... 111
G.M. Downs and I? Willett
3.2.1 Introduction ................................................ 111
3.2.2 Review of Clustering Methods ................................. 114
3.2.2.1 Hierarchical Clustering Methods ............................... 115
3.2.2.2 Non-Hierarchical Clustering Methods .......................... 118
3.2.3 Choice of Clustering Method ................................. 121
3.2.3.1 Computational Requirements .................................. 121
3.2.3.2 Cluster Shapes .............................................. 122
3.2.3.3 Comparative Studies ......................................... 123
3.2.4 Examples of the Selection of Compounds from Databases by
Clustering Techniques ........................................ 125
3.2.4.1 The Jarvis-Patrick Method .................................... 125
3.2.4.2 The Leader Method .......................................... 126
3.2.5 Conclusions ................................................. 127
References ......................................................... 128
3.3 Receptor Mapping and Phylogenetic Clustering .................. 131
I?J Lewi and H . Moereels
3.3.1 G-protein Coupled Receptors .................................. 132
3.3.2 Principal Coordinates Analysis of 71 Receptor Sequences ......... 135
3.3.3 Principle Coordinates Analysis of 26 Receptor Subtypes .......... 144
3.3.4 Phylogenetic Clustering ....................................... 148
3.3.5 Discussion .................................................. 157
References ......................................................... 161