Scaffold Hopping in Medicinal Chemistry [Hardcover]
Author: Nathan Brown | Language: English | ISBN: 3527333649 | Format: PDF, EPUB
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The first section serves as an introduction to the topic by describing the concept of scaffolds, their discovery, diversity and representation, and their importance for finding new chemical entities. The following part describes the most common tools and methods for scaffold hopping, whether topological, shape-based or structure-based. Methods such as CATS, Feature Trees, Feature Point Pharmacophores (FEPOPS), and SkelGen are discussed among many others. The final part contains three fully documented real-world examples of successful drug development projects by scaffold hopping that illustrate the benefits of the approach for medicinal chemistry.
While most of the case studies are taken from medicinal chemistry, chemical and structural biologists will also benefit greatly from the insights presented here.
- Hardcover: 328 pages
- Publisher: Wiley-VCH; 1 edition (January 13, 2014)
- Language: English
- ISBN-10: 3527333649
- ISBN-13: 978-3527333646
- Product Dimensions: 9.7 x 6.9 x 0.9 inches
- Shipping Weight: 1.8 pounds (View shipping rates and policies)
- Amazon Best Sellers Rank: #822,163 in Books (See Top 100 in Books)
- #42 in Books > Medical Books > Pharmacology > Chemistry
List of Contributors XIII
Preface XVII
A Personal Foreword XIX
Part One Scaffolds: Identification, Representation Diversity, and Navigation 1
1 Identifying and Representing Scaffolds 3
Nathan Brown
1.1 Introduction 3
1.2 History of Scaffold Representations 4
1.3 Functional versus Structural Molecular Scaffolds 7
1.4 Objective and Invariant Scaffold Representations 7
1.4.1 Molecular Frameworks 7
1.4.2 Scaffold Tree 8
1.5 Maximum Common Substructures 9
1.6 Privileged Scaffolds 11
1.7 Conclusions 11
References 12
2 Markush Structures and Chemical Patents 15
David Anthony Cosgrove
2.1 Introduction 15
2.2 Encoding Markush Structures 18
2.2.1 The r_group Record 19
2.2.1.1 Exact R Groups 19
2.2.1.2 Inexact R Groups 19
2.2.1.3 Fused R Groups 19
2.2.2 The Menguin Program 20
2.2.3 Correspondence between the MIL File and the Markush Structure 21
2.3 The Search Algorithm 22
2.3.1 Matching R Groups 25
2.3.1.1 Exact R Groups 25
2.3.1.2 Inexact R Groups 27
2.3.1.3 Fused R Groups 28
2.3.1.4 Hydrogen Atoms 29
2.3.1.5 Managing Multiple Fragment/R Group Matches 30
2.4 Using Periscope for Scaffold Hopping 31
2.4.1 Substructure Searching 32
2.4.2 Free–Wilson Analysis 33
2.4.3 Fast Followers 36
2.5 Conclusions 36
References 37
3 Scaffold Diversity in Medicinal Chemistry Space 39
Sarah R. Langdon, Julian Blagg, and Nathan Brown
3.1 Introduction 39
3.1.1 Scaffold Representation 39
3.1.2 What Do We Mean by Scaffold Diversity? 41
3.2 Scaffold Composition of Medicinal Chemistry Space 41
3.2.1 Natural Products as a Source of Novel Medicinal Chemistry Scaffolds 45
3.2.2 Enumerating Potential Medicinal Chemistry Scaffolds 46
3.2.3 Using Scaffold Composition to Interpret Bioactivity Data 48
3.3 Metrics for Quantifying the Scaffold Diversity of Medicinal Chemistry Space 48
3.4 Visualizing the Scaffold Diversity of Medicinal Chemistry Space 53
3.5 Conclusions 56
References 57
4 Scaffold Mining of Publicly Available Compound Data 61
Ye Hu and J€urgen Bajorath
4.1 Introduction 61
4.2 Scaffold Definition 62
4.3 Selectivity of Scaffolds 63
4.3.1 Privileged Substructures 63
4.3.2 Target Community-Selective Scaffolds 64
4.3.3 Target-Selective Scaffolds 67
4.4 Target Promiscuity of Scaffolds 67
4.4.1 Promiscuous BM Scaffolds and CSKs 67
4.4.2 Scaffold–Target Family Profiles 70
4.4.3 Promiscuous Scaffolds in Drugs 70
4.5 Activity Cliff-Forming Scaffolds 71
4.5.1 Activity Cliff Concept 71
4.5.2 Multitarget Cliff-Forming Scaffolds 71
4.6 Scaffolds with Defined Activity Progression 73
4.6.1 Activity Profile Sequences 73
4.6.2 Conserved Scaffolds 75
4.7 Scaffold Diversity of Pharmaceutical Targets 76
4.7.1 Scaffold Hopping Potential 76
4.7.2 Structural Relationships between Scaffolds 76
4.7.3 Scaffold Hopping in Virtual Screening 78
4.8 Conclusions 79
References 80
5 Exploring Virtual Scaffold Spaces 83
William R. Pitt and Boris Kroeplien
5.1 Introduction 83
5.1.1 Virtual Chemistry 83
5.1.2 Chemical Space 83
5.1.3 Scaffold Definition 84
5.2 The Comprehensive Enumeration of Parts of Chemical Space 85
5.2.1 Fragments 85
5.2.2 Ring Systems 86
5.2.3 Reagents 87
5.3 The Iterative Generation of Virtual Compounds 88
5.3.1 Transformations 88
5.3.2 Manual Selection of Chemical Modifications 88
5.3.3 Analog Generators 89
5.3.4 Inverse QSAR 89
5.3.5 Multiple Objective Optimization 90
5.3.6 Structure-Based De Novo Design 90
5.4 Virtual Synthesis 92
5.4.1 Synthetic Tractability 92
5.4.2 Using Real-Life Reactions for in Silico Molecule Construction 93
5.4.3 Readily Synthesizable Compounds 94
5.4.3.1 Construction 94
5.4.3.2 Searching 95
5.4.3.3 Outside Big Pharma 96
5.4.4 Iterative Approaches 96
5.5 Visualizations of Scaffold Space 96
5.6 A Perspective on the Past and the Future 97
References 99
Part Two Scaffold-Hopping Methods 105
6 Similarity-Based Scaffold Hopping Using 2D Fingerprints 107
Peter Willett
6.1 Fingerprints 107
6.2 Retrospective Studies of Scaffold Hopping Using 2D Fingerprints 109
6.3 Predictive Studies of Scaffold Hopping Using 2D Fingerprints 112
6.4 Conclusions 114
References 115
7 CATS for Scaffold Hopping in Medicinal Chemistry 119
Christian P. Koch, Michael Reutlinger, Nickolay Todoroff, Petra Schneider, and Gisbert Schneider
7.1 Chemically Advanced Template Search 119
7.2 Retrospective Evaluation of Enrichment and Scaffold Hopping Potential 122
7.3 Prospective Scaffold-Hopping Applications 126
7.4 Conclusions 128
References 128
8 Reduced Graphs 131
Kristian Birchall
8.1 Introduction 131
8.2 Generating Reduced Graphs 133
8.2.1 Reduction Scheme 133
8.2.2 Node Labeling 134
8.2.3 Sheffield Implementations 135
8.2.4 Extended Reduced Graphs 136
8.3 Comparison and Usage of Reduced Graphs 137
8.3.1 Conventional Fingerprinting 138
8.3.2 RG-Specific Fingerprints 139
8.3.3 Augmenting Fingerprints with Edit Distance 140
8.3.4 Extended Reduced Graph Fingerprints 141
8.3.5 Graph Matching Approaches 143
8.3.6 Bioisostere Encoding 144
8.4 Summary 146
References 146
9 Feature Trees 149
Nathan Brown
9.1 Introduction 149
9.2 Feature Tree Generation 149
9.3 Feature Tree Comparison 150
9.4 Retrospective Validation 151
9.5 Implementations and Applications 152
9.5.1 MTree: Combinations of Query Molecules 152
9.5.2 Similarity Searching in Large Combinatorial Chemistry Spaces 152
9.6 Conclusions 153
References 154
10 Feature Point Pharmacophores (FEPOPS) 155
Jeremy L. Jenkins
10.1 Similarity Searching in Drug Discovery 155
10.2 FEPOPS: An Analogy to Image Compression 157
10.3 Computing FEPOPS 159
10.4 Scaling and Correlations 162
10.5 Defining Scaffold Hopping 163
10.6 FEPOPS in Similarity Searching and Scaffold Hopping 164
10.7 Alternative Alignment 168
10.8 In Silico Target Prediction 170
10.9 Chemical Space Uniqueness 171
10.10 Perspective on FEPOPS’ 10 Year Anniversary 172
References 173
11 Three-Dimensional Scaffold Replacement Methods 175
Nathan Brown
11.1 Introduction 175
11.2 Generic Three-Dimensional Scaffold Replacement Workflow 175
11.2.1 Molecule Databases 175
11.2.2 Fragment Generation and Filtering 177
11.2.3 Fragment Replacement Search and Scoring 178
11.3 SHOP: Scaffold HOPping by GRID-Based Similarity Searches 178
11.4 ReCore 179
11.5 BROOD 179
11.6 Conclusions 180
References 181
12 Spherical Harmonic Molecular Surfaces (ParaSurf and ParaFit) 183
David W. Ritchie and Violeta I. Perez-Nueno
12.1 Introduction 183
12.2 Spherical Harmonic Surfaces 183
12.3 Rotating Spherical Polar Fourier Expansions 185
12.4 Spherical Harmonic Surface Shape Similarity 186
12.5 Calculating Consensus Shapes and Center Molecules 187
12.6 The ParaSurf and ParaFit Programs 188
12.7 Using Consensus Shapes to Probe the CCR5 Extracellular Pocket 190
12.8 Conclusions 192
References 193
13 The XED Force Field and Spark 195
Martin Slater and Andy Vinter
13.1 Pharmacological Similarity – More than Just Chemical Structure 195
13.2 Improving the Generation of Valid Molecular Fields 199
13.3 The eXtended Electron Distribution (XED) Force Field 200
13.4 The XED Force Field Applied to Scaffold Hopping in Spark 202
13.5 How Spark Works 202
13.6 Application of Spark in Drug Discovery Scenarios 206
13.7 P38 Kinase Inhibitor Fragment Growing Using Spark 207
13.7.1 The Beauty of P38 207
13.8 Creating New Molecules 208
13.9 New Potential Inhibitors 210
13.10 The Far-Reaching Consequences of Using Molecular Fields as Measures of Similarity 212
References 213
14 Molecular Interaction Fingerprints 215
Didier Rognan and Jeremy Desaphy
14.1 Introduction 215
14.2 Target-Annotated Ligand Fingerprints 215
14.2.1 Interacting Atom/Fragment Fingerprints 216
14.2.2 Protein–Ligand Pharmacophores 217
14.3 Ligand-Annotated Target Fingerprints 217
14.4 True Target–Ligand Fingerprints 220
14.4.1 Association Fingerprints 220
14.4.2 Interaction Pattern Fingerprints 222
14.5 Conclusions 225
References 226
15 SkelGen 231
Nikolay P. Todorov
15.1 Introduction 231
15.2 Structure Generation and Optimization 232
15.2.1 Fragments and Fragment Sets 232
15.2.2 Structure Generation 234
15.2.3 Scoring and Optimization 234
15.2.4 Ligand-Based Design 234
15.3 Validation Studies 235
15.3.1 Retrospective Validation Study: CDK2, COX2, ER, MMP-3 235
15.3.2 Estrogen Receptor 235
15.3.3 Histamine H3 Inverse Agonists 236
15.4 Scaffold Hopping Using Fixed Fragments 237
15.5 Scaffold Hopping Using Site Points 238
15.6 Further Considerations for Scaffold Hopping 240
15.6.1 Receptor Flexibility 240
15.6.2 Water Molecules 241
15.6.3 Receptor Specificity 242
15.7 Conclusion 242
References 243
Part Three Case Studies 245
16 Case Study 1: Scaffold Hopping for T-Type Calcium Channel and Glycine Transporter Type 1 Inhibitors 247
Leah C. Konkol, Timothy J. Senter, and Craig W. Lindsley
16.1 Introduction 247
16.2 T-Type Calcium Channel Inhibitors 248
16.3 Scaffold Hopping to Access Novel Calcium T-Type Channel Inhibitors 250
16.4 Scaffold Hopping to Access Novel Glycine Transporter
Type 1 (GlyT1) Inhibitors 253
16.5 Conclusions 255
References 255
17 Case Study 2: Bioisosteric Replacements for the Neurokinin 1 Receptor (NK1R) 259
Francesca Perruccio
17.1 Introduction 259
17.2 Neurokinin 1 (NK1) Therapeutic Areas 259
17.3 The Neurokinin 1 Receptor (NK1R) and Its Mechanism 260
17.4 Neurokinin 1 Antagonists 261
17.5 NK1 Receptor: Target Active Site and Binding Mode 264
17.6 Bioisosteric Replacements in NK1 Receptor Antagonist 266
17.7 Bioisosteric Replacements in NK1 Receptor Antagonist: A Retrospective Study 270
17.8 Summary and Conclusions 274
References 274
18 Case Study 3: Fragment Hopping to Design Highly Potent and Selective Neuronal Nitric Oxide Synthase Inhibitors 279
Haitao Ji and Richard B. Silverman
18.1 Fragment-Based Drug Design 279
18.2 Minimal Pharmacophoric Elements and Fragment Hopping 281
18.3 Fragment Hopping to Design Novel Inhibitors for Neuronal Nitric Oxide Synthase 283
18.4 Fragment Hopping to Optimize Neuronal Nitric Oxide Synthase Inhibitors 288
18.5 Application of Neuronal Nitric Oxide Synthase Inhibitors to the Prevention of Cerebral Palsy 289
References 291
Index 297
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