Molecular Diversity in Drug Design (eBook)

Contents 5
Contributors 7
Acknowledgements 11
Preface 13
Chapter 1 Issues in Molecular Diversity and the Role of Ligand Binding Sites 15
1. ISSUES IN MOLECULAR DIVERSITY 15
1.2 Combinatorial Efficiency 17
1.3 Diversity and Similarity 17
1.4 WorkFlows in Combinatorial Chemistry 18
1.5 Combinatorial Chemistry and Diversity Analysis Why bother? 19
1.6 The Similarity Principle 21
1.7 Validation 22
1.8 Data handling 24
1.9 The role of binding sites in library design 24
2. STRATEGIES FOR SITE ANALYSIS 26
2.1 Choice of a test set of binding sites 26
2.2 Alignment of binding sites 26
2.3 Choice of ligand dataset 27
2.4 Analysis of the ligand conformations 27
2.5 Sites corresponding to specific ligand conformational classes 31
2.6 Analysis of Ligand Protein Contacts 32
2.7 Discussion 35
3. CONCLUSION 35
REFERENCES 36
Chapter 2 Molecular Diversity in Drug Design. Application to High-speed Synthesis and High-Throughput Screening 37
1. INTRODUCTION 37
2. CONSIDERATION OF PHARMACOLOGICAL CONFORMITY BEFORE MOLECULAR DIVERSITY. 39
2.1 Pharmacodynamic Conformity 40
2.2 Pharmacokinetic Conformity 43
2.3 Pharmaceutical Conformity 50
3. DIVERSITY IN THE CONTEXT OF HSS-HTS 52
3.1 Diversity in Collections: 52
3.2 Assembly of sets of drug-like molecules containing a maximum diversity element 52
3.3 Assembly of sets of drug-like molecules containing a minimal structural conformity element. 54
4. COMMERCIAL DIVERSITY 55
5. CONCLUSION 55
ACKNOWLEDGEMENTS 55
REFERENCES: 55
Chapter 3 Background Theory of Molecular Diversity Background Theory of Molecular Diversity 57
1. INTRODUCTION 57
2. DIVERSITY METRICS 58
2.1 Structural Descriptors in Diversity Studies 60
2.2 Topological Indices and Physicochemical Properties 60
2.3 2D fragment-based descriptors 61
2.4 3D Descriptors 63
2.5 Validation of structural descriptors 65
3. RANDOM OR RATIONAL? 67
4. DESIGNING DIVERSE LIBRARIES BY ANALYSING PRODUCT SPACE 69
5. DATABASE COMPARISONS 73
6. CONCLUSIONS 75
REFERENCES 76
Chapter 4 Absolute vs Relative Similarity and Diversity The Partitioning Approach to relative and absolute diversity 80
1. INTRODUCTION 80
1.1 Multiple potential pharmacophore method 81
1.1.1Relative similarity and diversity 82
1.2 DiverseSolutions chemistry space method 82
1.2.1Relative similarity and diversity 83
2. MULTIPLE POTENTIAL 3D PHARMACOPHORES 83
2.1 Calculation of potential pharmacophores 83
2.1.1 Calculation for ligands 84
2.1.2 Calculation for targets 84
2.1.3 Definition of features – atom types 85
2.1.4 Distance ranges 86
2.1.5 Conformational sampling 87
2.1.6 Chirality 88
2.1.7 Frequency Count 88
2.1.8 Quality checks 89
2.2 Calculation of relative potential pharmacophores 89
2.3 Generation of complementary pharmacophores for protein sites 91
2.4 3-point versus 4-point pharmacophores 92
2.5 Use of ‘relative’ pharmacophoric similarity and diversity 92
2.6 Use of the protein site for steric constraints 93
3. BCUT CHEMISTRY SPACE – DIVERSESOLUTIONS (DVS) 93
3.1 Receptor -relevant Sub Chemistry Spaces 94
4. STUDIES USING ABSOLUTE SIMILARITY AND DIVERSITY 94
4.1 Analysis of reference databases 94
4.1.1 Multiple 4-point potential pharmacophores 94
4.1.2 DVS atomic/molecular properties 95
4.2 Ligand studies 96
4.3 Ligand-receptor studies 96
5. STUDIES USING RELATIVE SIMILARITY AND DIVERSITY 100
5.1 Ligand – receptor studies using multiple potential pharmacophores 100
5.2 Library design using multiple potential pharmacophores 101
5.3 Analysis of active compounds using DVS 101
6. CONCLUSIONS 103
ACKNOWLEDGMENTS 103
REFERENCES 103
Chapter 5 Diversity in Very Large Libraries Diversity in Very Large Libraries 105
1. INTRODUCTION 105
2. GENETICS OF MOLECULES 107
3. IMPLEMENTING ARTIFICIAL EVOLUTION 108
3.1 Operators of Genetic Algorithms 109
3.1.1 OperatorMacting on the genome 109
3.1.2 Operator for ranking and selection 110
3.1.3 What are optimal GA parameters? 111
3.2 Computational Methods to Select Similar Compounds 112
3.3 GA Driven Evolutionary Chemistry 113
3.4 SIMULATED MOLECULAR EVOLUTION 115
4. DIVERSITY IN LARGE LIBRARIES 123
REFERENCES 123
Chapter 6 Subset-Selection Methods For Chemical Databases Methods for Subset Selection 126
1. INTRODUCTION 126
2. CLUSTER-BASEDSELECTIONMETHODS 128
3. DISSIMILARITY -BASED SELECTION METHODS 132
4. PARTITION-BASED SELECTION METHODS 136
5. OPTIMISATION-BASED APPROACHES METHODS 137
6. EVALUATION AND COMPARISON OF SELECTION METHODS 140
7. CONCLUSIONS 145
ACKNOWLEDGEMENTS 147
REFERENCES 147
Chapter 7 Molecular Diversity in Site-focused Libraries Molecular diversity in site-focused libraries 152
1. INTRODUCTION 152
2. COMPUTER -AIDED DRUG DESIGN OVERVIEW 154
3. INDIRECT DESIGN 155
4. DIRECT DESIGN 157
4.1 Finding key site points 157
4.2 Finding candidate molecules 160
4.3 Evaluating binding 162
4.4 Current limitations 163
5. CREATING SITE-FOCUSED COMBINATORIAL LIBRARIES 164
5.1 Scaffold Design 165
5.2 Virtual libraries 166
5.3 Incorporating SAR data into library design 167
5.3.1 Designing a library from a pharmacophore model 169
5.4 Structure-based approaches to virtual libraries 171
5.5 Other programs for virtual libraries 179
6. CONCLUSION 181
REFERENCES 181
Chapter 8 Managing Combinatorial Chemistry Information Managing Information 185
1. INTRODUCTION 185
1.1 Data, Information and Knowledge 187
1.2 Strategic Goals 188
2. ARCHITECTURE 189
2.1 Relational Databases 189
2.2 Reaction Databases 191
2.3 Reagent Databases 192
2.4 Library Databases 194
2.5 Enumerated Databases 197
2.6 Searching 197
2.7 Integration with Robotics 198
2.8 Plates 198
2.9 Mixtures 199
2.10Assays 200
3. APPLICATIONS 200
3.1 Similarity 200
3.2 Calculated Properties 201
3.3 Conformational Dependence 202
3.4 Pharmacophores 203
4. CONCLUSIONS 205
REFERENCES 206
Chapter 9 Design of Small Libraries for Lead Exploration 207
1. INTRODUCTION 208
2. COMBINATORIAL CHEMISTRY FOR OPTIMISING A LEAD 211
3. DEFINING A STRATEGY FOR LEAD EXPLORATION 211
3.1 Chemometrical Aspects 211
3.2 Defining the Search Space 212
3.3 Pre-Processing of Structural Data using Principal Component Analysis (PCA) 214
3.4 Design in Principal Properties - Selection of Building Blocks in Clusters 215
3.4.1 Design of the Building Block Combinations 218
3.4.2 A Design Example 219
4. CHEMICAL SYNTHESIS 220
5. BIOLOGICAL TESTING 221
5.1 Importance of Good Biological Testing 221
5.1.1 Risk of False Biological Test Result 222
5.1.1.1 Risk for False Negatives 222
5.1.1.2 Risk for False Positives 222
5.1.1.3 Depth of Biological Testing. 223
5.2 Analysing the Biological Result – Multivariate QSAR Modelling 224
5.2.1M-QSAR Example 224
6. DISCUSSION 226
ACKNOWLEDGEMENTS 227
REFERENCES 228
Chapter 10 The Design of Small- and Medium-sized Focused Combinatorial Libraries Design of focused combinatorial libraries 231
1. INTRODUCTION 231
1.1 Definitions 232
1.2 Combinatorial Efficiency 234
1.3 Diversity and Similarity 234
1.4 Work Flows in RPS 235
1.5 SAR information 236
1.6 Reagent Filtering and Drug-likeness 236
1.7 Enumeration of the virtual library 237
2. MOLECULAR DESCRIPTORS AND DIVERSITY METRICS 238
2.1 Definitions 238
2.2 Descriptors 239
2.2.1 Substructural keys - SAR scenario: one active chemical family 239
2.2.2 Physicochemical properties - SAR scenario: several structurally unrelated compounds with weak activity 240
2.3 Single Conformation 3D descriptors 241
2.3.1 Topomeric Descriptors - SAR scenario: exploration of an established lead. 241
2.4 Multiple Conformation 3D descriptors 242
2.4.1 Property Matching - SAR scenario: a few structurally related compounds with reasonable activity. 242
2.4.2 Pharmacophores - SAR scenario: a few structurally unrelated compounds with reasonable activity. 243
2.5 CoMFA - SAR scenario: an established SAR. 244
2.6 Structure-Based Design - SAR scenario: a knowledge of the receptor site and binding mode 244
3. DESIGN STRATEGIES 245
3.1 Random design 245
3.2 Design based on reagents 245
3.3 Design Based on Products 246
4. DIVERSITY ALGORITHMS 246
4.1 Maximising distance matrix scores 247
4.2 Maximising rank scores 247
4.3 Ensemble scoring and distribution fitting 248
4.4 Visualising diversity 249
5. COMBINATION OF DIVERSITY MEASURES 249
6. COMMERCIAL PROGRAMS FOR LIBRARY DESIGN 252
6.1.1 Molecular Simulations 252
6.1.2 Tripos 252
6.1.2.1 DVS 253
6.1.3 Chemical Design 253
7. PUBLISHED APPLICATIONS 254
8. CONCLUSIONS 254
ACKNOWLEDGMENTS 254
REFERENCES 255
Index 259

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.