Dean / Lewis Molecular Diversity in Drug Design

1. Auflage 2007
ISBN: 978-0-306-46873-5
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 263 Seiten

This book focuses on the theoretical problems associated with molecular diversity as it is being applied in the pharmaceutical industry. Therefore, this book deals with algorithms that are involved in understanding chemical space and selection of diverse sets of structures. The algorithms also deal with the problem of focused diversity where chemical libraries are being created within a structured physical volume.

Diversity is necessarily connected to combinational chemistry, although this book is limited to the application of diversity methods to combinational chemistry and does not deal with synthetic methods. It is this focus on algorithms and strategies for exploiting molecular diversity that makes it different from books on combinational chemistry. The intended readership of the book falls into two categories: those actively engaged in applying molecular diversity in the chemical industry and those in academia who are developing strategies to embrace, understand and accept the many problems thrown up by this new research field of molecular diversity.

Autoren/Hrsg.

Dean, P. M.

Lewis, R. A.

Inhaltsverzeichnis

1;Contents;5
2;Contributors;7
3;Acknowledgements;11
4;Preface;13
5;Chapter 1 Issues in Molecular Diversity and the Role of Ligand Binding Sites;15
5.1;1. ISSUES IN MOLECULAR DIVERSITY;15
5.1.1;1.2 Combinatorial Efficiency;17
5.1.2;1.3 Diversity and Similarity;17
5.1.3;1.4 WorkFlows in Combinatorial Chemistry;18
5.1.4;1.5 Combinatorial Chemistry and Diversity Analysis Why bother?;19
5.1.5;1.6 The Similarity Principle;21
5.1.6;1.7 Validation;22
5.1.7;1.8 Data handling;24
5.1.8;1.9 The role of binding sites in library design;24
5.2;2. STRATEGIES FOR SITE ANALYSIS;26
5.2.1;2.1 Choice of a test set of binding sites;26
5.2.2;2.2 Alignment of binding sites;26
5.2.3;2.3 Choice of ligand dataset;27
5.2.4;2.4 Analysis of the ligand conformations;27
5.2.5;2.5 Sites corresponding to specific ligand conformational classes;31
5.2.6;2.6 Analysis of Ligand Protein Contacts;32
5.2.7;2.7 Discussion;35
5.3;3. CONCLUSION;35
5.4;REFERENCES;36
6;Chapter 2 Molecular Diversity in Drug Design. Application to High-speed Synthesis and High-Throughput Screening;37
6.1;1. INTRODUCTION;37
6.2;2. CONSIDERATION OF PHARMACOLOGICAL CONFORMITY BEFORE MOLECULAR DIVERSITY.;39
6.2.1;2.1 Pharmacodynamic Conformity;40
6.2.2;2.2 Pharmacokinetic Conformity;43
6.2.3;2.3 Pharmaceutical Conformity;50
6.3;3. DIVERSITY IN THE CONTEXT OF HSS-HTS;52
6.3.1;3.1 Diversity in Collections:;52
6.3.2;3.2 Assembly of sets of drug-like molecules containing a maximum diversity element;52
6.3.3;3.3 Assembly of sets of drug-like molecules containing a minimal structural conformity element.;54
6.4;4. COMMERCIAL DIVERSITY;55
6.5;5. CONCLUSION;55
6.6;ACKNOWLEDGEMENTS;55
6.7;REFERENCES:;55
7;Chapter 3 Background Theory of Molecular Diversity Background Theory of Molecular Diversity;57
7.1;1. INTRODUCTION;57
7.2;2. DIVERSITY METRICS;58
7.2.1;2.1 Structural Descriptors in Diversity Studies;60
7.2.2;2.2 Topological Indices and Physicochemical Properties;60
7.2.3;2.3 2D fragment-based descriptors;61
7.2.4;2.4 3D Descriptors;63
7.2.5;2.5 Validation of structural descriptors;65
7.3;3. RANDOM OR RATIONAL?;67
7.4;4. DESIGNING DIVERSE LIBRARIES BY ANALYSING PRODUCT SPACE;69
7.5;5. DATABASE COMPARISONS;73
7.6;6. CONCLUSIONS;75
7.7;REFERENCES;76
8;Chapter 4 Absolute vs Relative Similarity and Diversity The Partitioning Approach to relative and absolute diversity;80
8.1;1. INTRODUCTION;80
8.1.1;1.1 Multiple potential pharmacophore method;81
8.1.1.1;1.1.1Relative similarity and diversity;82
8.1.2;1.2 DiverseSolutions chemistry space method;82
8.1.2.1;1.2.1Relative similarity and diversity;83
8.2;2. MULTIPLE POTENTIAL 3D PHARMACOPHORES;83
8.2.1;2.1 Calculation of potential pharmacophores;83
8.2.1.1;2.1.1 Calculation for ligands;84
8.2.1.2;2.1.2 Calculation for targets;84
8.2.1.3;2.1.3 Definition of features – atom types;85
8.2.1.4;2.1.4 Distance ranges;86
8.2.1.5;2.1.5 Conformational sampling;87
8.2.1.6;2.1.6 Chirality;88
8.2.1.7;2.1.7 Frequency Count;88
8.2.1.8;2.1.8 Quality checks;89
8.2.2;2.2 Calculation of relative potential pharmacophores;89
8.2.3;2.3 Generation of complementary pharmacophores for protein sites;91
8.2.4;2.4 3-point versus 4-point pharmacophores;92
8.2.5;2.5 Use of ‘relative’ pharmacophoric similarity and diversity;92
8.2.6;2.6 Use of the protein site for steric constraints;93
8.3;3. BCUT CHEMISTRY SPACE – DIVERSESOLUTIONS (DVS);93
8.3.1;3.1 Receptor -relevant Sub Chemistry Spaces;94
8.4;4. STUDIES USING ABSOLUTE SIMILARITY AND DIVERSITY;94
8.4.1;4.1 Analysis of reference databases;94
8.4.1.1;4.1.1 Multiple 4-point potential pharmacophores;94
8.4.1.2;4.1.2 DVS atomic/molecular properties;95
8.4.2;4.2 Ligand studies;96
8.4.3;4.3 Ligand-receptor studies;96
8.5;5. STUDIES USING RELATIVE SIMILARITY AND DIVERSITY;100
8.5.1;5.1 Ligand – receptor studies using multiple potential pharmacophores;100
8.5.2;5.2 Library design using multiple potential pharmacophores;101
8.5.3;5.3 Analysis of active compounds using DVS;101
8.6;6. CONCLUSIONS;103
8.7;ACKNOWLEDGMENTS;103
8.8;REFERENCES;103
9;Chapter 5 Diversity in Very Large Libraries Diversity in Very Large Libraries;105
9.1;1. INTRODUCTION;105
9.2;2. GENETICS OF MOLECULES;107
9.3;3. IMPLEMENTING ARTIFICIAL EVOLUTION;108
9.3.1;3.1 Operators of Genetic Algorithms;109
9.3.1.1;3.1.1 OperatorMacting on the genome;109
9.3.1.2;3.1.2 Operator for ranking and selection;110
9.3.1.3;3.1.3 What are optimal GA parameters?;111
9.3.2;3.2 Computational Methods to Select Similar Compounds;112
9.3.3;3.3 GA Driven Evolutionary Chemistry;113
9.3.4;3.4 SIMULATED MOLECULAR EVOLUTION;115
9.4;4. DIVERSITY IN LARGE LIBRARIES;123
9.5;REFERENCES;123
10;Chapter 6 Subset-Selection Methods For Chemical Databases Methods for Subset Selection;126
10.1;1. INTRODUCTION;126
10.2;2. CLUSTER-BASEDSELECTIONMETHODS;128
10.3;3. DISSIMILARITY -BASED SELECTION METHODS;132
10.4;4. PARTITION-BASED SELECTION METHODS;136
10.5;5. OPTIMISATION-BASED APPROACHES METHODS;137
10.6;6. EVALUATION AND COMPARISON OF SELECTION METHODS;140
10.7;7. CONCLUSIONS;145
10.8;ACKNOWLEDGEMENTS;147
10.9;REFERENCES;147
11;Chapter 7 Molecular Diversity in Site-focused Libraries Molecular diversity in site-focused libraries;152
11.1;1. INTRODUCTION;152
11.2;2. COMPUTER -AIDED DRUG DESIGN OVERVIEW;154
11.3;3. INDIRECT DESIGN;155
11.4;4. DIRECT DESIGN;157
11.4.1;4.1 Finding key site points;157
11.4.2;4.2 Finding candidate molecules;160
11.4.3;4.3 Evaluating binding;162
11.4.4;4.4 Current limitations;163
11.5;5. CREATING SITE-FOCUSED COMBINATORIAL LIBRARIES;164
11.5.1;5.1 Scaffold Design;165
11.5.2;5.2 Virtual libraries;166
11.5.3;5.3 Incorporating SAR data into library design;167
11.5.3.1;5.3.1 Designing a library from a pharmacophore model;169
11.5.4;5.4 Structure-based approaches to virtual libraries;171
11.5.5;5.5 Other programs for virtual libraries;179
11.6;6. CONCLUSION;181
11.7;REFERENCES;181
12;Chapter 8 Managing Combinatorial Chemistry Information Managing Information;185
12.1;1. INTRODUCTION;185
12.1.1;1.1 Data, Information and Knowledge;187
12.1.2;1.2 Strategic Goals;188
12.2;2. ARCHITECTURE;189
12.2.1;2.1 Relational Databases;189
12.2.2;2.2 Reaction Databases;191
12.2.3;2.3 Reagent Databases;192
12.2.4;2.4 Library Databases;194
12.2.5;2.5 Enumerated Databases;197
12.2.6;2.6 Searching;197
12.2.7;2.7 Integration with Robotics;198
12.2.8;2.8 Plates;198
12.2.9;2.9 Mixtures;199
12.2.10;2.10Assays;200
12.3;3. APPLICATIONS;200
12.3.1;3.1 Similarity;200
12.3.2;3.2 Calculated Properties;201
12.3.3;3.3 Conformational Dependence;202
12.3.4;3.4 Pharmacophores;203
12.4;4. CONCLUSIONS;205
12.5;REFERENCES;206
13;Chapter 9 Design of Small Libraries for Lead Exploration;207
13.1;1. INTRODUCTION;208
13.2;2. COMBINATORIAL CHEMISTRY FOR OPTIMISING A LEAD;211
13.3;3. DEFINING A STRATEGY FOR LEAD EXPLORATION;211
13.3.1;3.1 Chemometrical Aspects;211
13.3.2;3.2 Defining the Search Space;212
13.3.3;3.3 Pre-Processing of Structural Data using Principal Component Analysis (PCA);214
13.3.4;3.4 Design in Principal Properties - Selection of Building Blocks in Clusters;215
13.3.4.1;3.4.1 Design of the Building Block Combinations;218
13.3.4.2;3.4.2 A Design Example;219
13.4;4. CHEMICAL SYNTHESIS;220
13.5;5. BIOLOGICAL TESTING;221
13.5.1;5.1 Importance of Good Biological Testing;221
13.5.1.1;5.1.1 Risk of False Biological Test Result;222
13.5.1.1.1;5.1.1.1 Risk for False Negatives;222
13.5.1.1.2;5.1.1.2 Risk for False Positives;222
13.5.1.1.3;5.1.1.3 Depth of Biological Testing.;223
13.5.2;5.2 Analysing the Biological Result – Multivariate QSAR Modelling;224
13.5.2.1;5.2.1M-QSAR Example;224
13.6;6. DISCUSSION;226
13.7;ACKNOWLEDGEMENTS;227
13.8;REFERENCES;228
14;Chapter 10 The Design of Small- and Medium-sized Focused Combinatorial Libraries Design of focused combinatorial libraries;231
14.1;1. INTRODUCTION;231
14.1.1;1.1 Definitions;232
14.1.2;1.2 Combinatorial Efficiency;234
14.1.3;1.3 Diversity and Similarity;234
14.1.4;1.4 Work Flows in RPS;235
14.1.5;1.5 SAR information;236
14.1.6;1.6 Reagent Filtering and Drug-likeness;236
14.1.7;1.7 Enumeration of the virtual library;237
14.2;2. MOLECULAR DESCRIPTORS AND DIVERSITY METRICS;238
14.2.1;2.1 Definitions;238
14.2.2;2.2 Descriptors;239
14.2.2.1;2.2.1 Substructural keys - SAR scenario: one active chemical family;239
14.2.2.2;2.2.2 Physicochemical properties - SAR scenario: several structurally unrelated compounds with weak activity;240
14.2.3;2.3 Single Conformation 3D descriptors;241
14.2.3.1;2.3.1 Topomeric Descriptors - SAR scenario: exploration of an established lead.;241
14.2.4;2.4 Multiple Conformation 3D descriptors;242
14.2.4.1;2.4.1 Property Matching - SAR scenario: a few structurally related compounds with reasonable activity.;242
14.2.4.2;2.4.2 Pharmacophores - SAR scenario: a few structurally unrelated compounds with reasonable activity.;243
14.2.5;2.5 CoMFA - SAR scenario: an established SAR.;244
14.2.6;2.6 Structure-Based Design - SAR scenario: a knowledge of the receptor site and binding mode;244
14.3;3. DESIGN STRATEGIES;245
14.3.1;3.1 Random design;245
14.3.2;3.2 Design based on reagents;245
14.3.3;3.3 Design Based on Products;246
14.4;4. DIVERSITY ALGORITHMS;246
14.4.1;4.1 Maximising distance matrix scores;247
14.4.2;4.2 Maximising rank scores;247
14.4.3;4.3 Ensemble scoring and distribution fitting;248
14.4.4;4.4 Visualising diversity;249
14.5;5. COMBINATION OF DIVERSITY MEASURES;249
14.6;6. COMMERCIAL PROGRAMS FOR LIBRARY DESIGN;252
14.6.1;6.1.1 Molecular Simulations;252
14.6.2;6.1.2 Tripos;252
14.6.2.1;6.1.2.1 DVS;253
14.6.3;6.1.3 Chemical Design;253
14.7;7. PUBLISHED APPLICATIONS;254
14.8;8. CONCLUSIONS;254
14.9;ACKNOWLEDGMENTS;254
14.10;REFERENCES;255
15;Index;259

Leseproben

Chapter 5
Diversity in Very Large Libraries (p. 93-94)

Diversity in Very Large Libraries
Lutz Weber and Michael Almstetter
Morphochem AG, Am Klopferspitz 19, 82152 Martinsried, Germany

Key words: Combinatorial chemistry, genetic algorithms, combinatorial optimisation, QSAR, evolutionary chemistry, very large compound libraries

Abstract: Combinatorial chemistry methods can be used, in principle, for the synthesis of very large compound libraries. However, these very large libraries are so large that the enumeration of all individual members of a library may not be practicable. We discuss here how one may increase the chances of finding compounds with desired properties from very large libraries by using combinatorial optimisation methods. Neuronal networks, evolutionary programming and especially genetic algorithms are heuristic optimisation methods that can be used implicitly to discover the relation between the structure of molecules and their properties. Genetic algorithms are derived from principles that are used by nature to find optimal solutions. Genetic algorithms have now been adapted and applied with success to problems in combinatorial chemistry. The optimisation behaviour of genetic algorithms was investigated using a library of molecules with known biological activities. From these studies, one can derive methods to estimate the diversity and structure property relationships without the need to enumerate and calculate the properties of the whole search space of these very large libraries.

1. INTRODUCTION

In nature, the evolution of molecules with desired properties may be regarded as a combinatorial optimisation strategy to find solutions in a search space of unlimited size and diversity. Thus, the number of all possible, different proteins comprising only 200 amino acids is 20200, a number that is much larger than the number of particles in the universe (estimated to be in the range of 1088 . ) Similarly, the number of different molecules that could be synthesised by combinatorial chemistry methods far exceeds our synthetic and even computational capabilities in reality. Whilst diversity and various properties of compound libraries in the range of several thousands to millions can be calculated by using a range of different methods, there is little available knowledge and experience for dealing with very large libraries. The task for chemists is therefore to find methods that can be used to choose useful subsets from this practically unlimited space of possible solutions.

The intellectual concept and the emerging synthetic power of combinatorial chemistry are moving the attention of experimental chemists towards a more abstract understanding of their science: instead of synthesising and investigating just a few molecules they are dealing now with libraries and group properties. The answers to questions such as how diverse or similar are any two compounds, are now not just intellectually interesting but also have commercial value. Therefore, the ability to understand and use very large libraries is, in our opinion, connected to the understanding and the development of chemistry in the future.

The discovery of a new medicine may be understood as an evolutionary process that starts with an initial knowledge set, elaborating a hypothesis, making experiments and thereby expanding our knowledge. A new refined hypothesis will give rise to further cycles of knowledge and experiments ending with molecules that satisfy our criteria. If very large compound libraries are considered, one may argue that the desired molecules are already contained within this initial library. A very large library on the other hand means that we are neither practically nor theoretically able to synthesise or compute all members of this library. How can we nevertheless find this molecule? Is it possible to develop methods that automate the discovery of new medicines by using such libraries without human interference?

An answer to these questions would be a novel approach to combinatorial chemistry that tries to connect the selection and synthesis of biologically active compounds from the very large library by mathematical optimisation methods. Heuristic algorithms, like genetic algorithms or neural networks, mimic the Darwinian evolution and do not require the a priori knowledge of structure-activity relationships. These combinatorial optimisation methods (1) have proved to be useful in solving multidimensional problems and are now being used with success in various areas of combinatorial chemistry. Thus, evolutionary chemistry may aid in the selection of information rich subsets of available compound libraries or in designing screening libraries and new compounds to be synthesised, adding thereby a new quality to combinatorial chemistry.