De novo Molecular Design

Overview

Systematically examining current methods and strategies, this ready reference covers a wide range of molecular structures, from organic-chemical drugs to peptides, Proteins and nucleic acids, in line with emerging new drug classes derived from biomacromolecules.

A leader in the field and one of the pioneers of this young discipline has assembled here the most prominent experts from across the world to provide first-hand knowledge. While most of their methods and examples come ...

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De novo Molecular Design

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Overview

Systematically examining current methods and strategies, this ready reference covers a wide range of molecular structures, from organic-chemical drugs to peptides, Proteins and nucleic acids, in line with emerging new drug classes derived from biomacromolecules.

A leader in the field and one of the pioneers of this young discipline has assembled here the most prominent experts from across the world to provide first-hand knowledge. While most of their methods and examples come from the area of pharmaceutical discovery and development, the approaches are equally applicable for chemical probes and diagnostics, pesticides, and any other molecule designed to interact with a biological system. Numerous images and screenshots illustrate the many examples and method descriptions.

With its broad and balanced coverage, this will be the firststop resource not only for medicinal chemists, biochemists and biotechnologists, but equally for bioinformaticians and molecular designers for many years to come.

From the content:

* Reaction-driven de novo design
* Adaptive methods in molecular design
* Design of ligands against multitarget profiles
* Free energy methods in ligand design
* Fragment-based de novo design
* Automated design of focused and target family-oriented compound libraries
* Molecular de novo design by nature-inspired computing
* 3D QSAR approaches to de novo drug design
* Bioisosteres in de novo design
* De novo design of peptides, proteins and nucleic acid structures, including RNA aptamers

and many more.

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Product Details

  • ISBN-13: 9783527334612
  • Publisher: Wiley
  • Publication date: 12/23/2013
  • Edition number: 1
  • Pages: 576
  • Product dimensions: 6.90 (w) x 9.80 (h) x 1.30 (d)

Meet the Author

Gisbert Schneider is full professor of computer-assisted drug design at ETH Zürich, Switzerland. He studied biochemistry and computer science at the Free University of Berlin, Germany. After several international postdoctoral research activities he joined F. Hoffmann-La Roche Pharmaceuticals in Basel, Switzerland, where he headed the cheminformatics group. From 2002-2009 he was full professor of cheminformatics and bioinformatics (Beilstein Endowed Chair) at Goethe-University Frankfurt, Germany. Professor Schneider coined the terms "scaffold-hopping" and "frequent hitter" in drug design. His research activities concentrate on method development for adaptive molecular design and their tight integration with innovative chemical and biophysical techniques in drug discovery.

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Table of Contents

List of Contributors XV

Foreword XXI

Preface XXIII

1 De Novo Design: From Models to Molecules 1
Gisbert Schneider and Karl-Heinz Baringhaus

1.1 Molecular Representation 1

1.2 The Molecular Design Cycle 9

1.3 Receptor–Ligand Interaction 14

1.4 Modeling Fitness Landscapes 21

1.4.1 Naïve Bayes Classifier 26

1.4.2 Artificial Neural Network 27

1.4.3 Support Vector Machine 27

1.4.4 Gaussian Process 28

1.5 Strategies for Compound Construction 30

1.6 Strategies for Compound Scoring 33

1.6.1 Receptor-Based Scoring 35

1.6.2 Ligand-Based Scoring 37

1.7 Flashback Forward: A Brief History of De Novo Drug Design 37

1.8 Conclusions 43

Acknowledgments 43

References 44

2 Coping with Complexity in Molecular Design 57
Michael M. Hann and Andrew R. Leach

2.1 Introduction 57

2.2 A Simple Model of Molecular Interactions 58

2.3 Enhancements to the Simple Complexity Model 60

2.4 Enumerating and Sampling the Complexity of Chemical Space 61

2.5 Validation of the Complexity Model 65

2.6 Reductionism and Drug Design 67

2.7 Complexity and Information Content as a Factor in De Novo Design 69

2.8 Complexity of Thermodynamic Entropy and Drug Design 73

2.9 Complex Systems, Emergent Behavior, and Molecular Design 74

Acknowledgments 75

References 75

3 The Human Pocketome 79
Ruben Abagyan and Clarisse Gravina Ricci

3.1 Predicted Pockets 79

3.2 Compilation of the Validated Human Pocketome 83

3.3 Diversity and Redundancy of the Human Pocketome 85

3.4 Compound Activity Prediction by Ligand-Pocket Docking and Scoring 87

3.4.1 Optimizing Pocket Sets for Reliable Docking and Scoring Results 87

3.4.2 Difficult Cases: Unusually Large and Multifunctional Pockets 88

3.5 Pocketome-Derived 3D Chemical Fields as Activity Prediction Models 90

3.6 Clustering the Ligands by Function and Subpockets 92

3.7 Conclusions 94

Acknowledgments 94

References 94

4 Structure-Based De Novo Drug Design 97
Alla Srinivas Reddy, Lu Chen, and Shuxing Zhang

4.1 Introduction 97

4.2 Current Progress in SBDND Methodologies 99

4.2.1 Identification of Binding Site 100

4.2.2 Design of Molecules 101

4.2.3 Searching the Chemical Space 105

4.2.4 Scoring Methods 108

4.2.5 Synthetic Accessibility 110

4.3 Recent Applications of Structure-Based De Novo Design 110

4.4 Perspectives and Conclusion 115

Acknowledgment 116

References 116

5 De Novo Design by Fragment Growing and Docking 125
Jacob D. Durrant and Rommie E. Amaro

5.1 Introduction 125

5.2 Case Study I: High-Throughput Screening with Dr Feils 126

5.2.1 Target Identification 126

5.2.2 Small-Molecule Library Design 126

5.2.3 High-Throughput Screening 129

5.2.4 Optimization 130

5.3 Case Study II: Fragment-Based Drug Design with Dr Goode 130

5.3.1 Library Generation 130

5.3.2 Detection Methods 132

5.3.3 Screening 134

5.3.4 Optimization 135

5.3.5 Final Products 137

5.4 Conclusion 138

Disclaimer 138

Acknowledgments 138

References 138

6 Hit and Lead Identification from Fragments 143
Michael Mazanetz, Richard Law, and Mark Whittaker

6.1 Introduction to FBDD 144

6.2 Fragment Library Design Incorporating Computational Methods 148

6.2.1 Fragment Library Design Strategies 148

6.2.2 Molecular Attributes and Physicochemical Properties 150

6.2.3 Influence of Screening Method on Library Selection 151

6.2.4 Removal of Undesirable Functionality 152

6.2.5 Size of Library and Diversity 152

6.2.6 Focused Sets 154

6.2.7 Designing in Fragment Optimization 154

6.3 Fragment Screening 155

6.3.1 Screening by X-Ray Crystallography 155

6.3.2 Screening by NMR 156

6.3.3 Screening by SPR 157

6.3.4 Screening by Biochemical Assay 157

6.3.5 Thermal Shift Assays 158

6.3.6 Isothermal Titration Calorimetry (ITC) 160

6.3.7 Other Biophysical Assay Techniques 160

6.3.8 Assay Techniques for Membrane Proteins 161

6.3.9 Fragment Library Screening Using Computational Methods 161

6.3.10 Ligandability Screening Using Fragments 162

6.4 Fragment Prioritization for Optimization 165

6.4.1 Efficiency Metrics 165

6.4.2 Computational and Thermodynamic Methods for Fragment Selection and Prioritization 167

6.5 Fragment Hit Expansion and Fragment Evolution 170

6.6 Fragment Merging Principles 175

6.7 Fragment Linking Principles 177

6.8 Fragment-Assisted Drug Discovery (FADD) 182

6.9 Conclusion 183

Acknowledgments 184

References 184

7 Pharmacophore-Based De Novo Design 201
Wen-Jing Wang, Qi Huang, and Sheng-Yong Yang

7.1 Introduction 201

7.2 A Summary of the Algorithms of PhDD v1.0 202

7.2.1 The Basic Scheme of PhDD 202

7.2.2 Fragment and Linker Databases 203

7.2.3 Mapping of Fragments onto the Locations of Pharmacophore Features of the Pharmacophore Hypothesis 204

7.2.4 Connecting Fragments by Linkers 205

7.2.5 Assessments to the Generated Molecules 206

7.3 An Introduction to the Modifications in the Updated Version of PhDD (v2.0) 208

7.3.1 The Use of a Designated Fragment 209

7.3.2 Conformation Optimization in the Process of Molecular Construction 209

7.3.3 Two Pharmacophore Features Share One Fragment 210

7.4 Validation of PhDD 210

7.5 Concluding Remarks 212

Acknowledgment 213

References 213

8 3D-QSAR Approaches to De Novo Drug Design 215
Richard D. Cramer

8.1 Introduction 215

8.2 Current Methods 216

8.3 Leapfrog 217

8.4 Recent Advances 219

8.5 Conclusions 223

Acknowledgments 223

References 223

9 Ligand-Based Molecular Design Using Pseudoreceptors 227
Darren Fayne

9.1 Introduction 227

9.2 Pseudoreceptor Algorithms 231

9.3 Successful Applications Overview 232

9.4 Conclusions 240

Acknowledgments 241

References 241

10 Reaction-Driven De Novo Design: a Keystone for Automated Design of Target Family-Oriented Libraries 245
Markus Hartenfeller, Steffen Renner, and Edgar Jacoby

10.1 Introduction 245

10.2 Reaction-Driven Design: Tackling the Problem of Synthetic Feasibility 247

10.2.1 Exploiting the Valuable Knowledge Stored in Electronic Laboratory Notebooks 249

10.2.2 Assessing the Chemical Space of a Focused Set of Reactions 251

10.3 Successful Applications of Reaction-Driven De Novo Design 254

10.4 Reaction-Driven Design of Chemical Libraries Addressing Target Families 256

10.5 Conclusions 261

References 265

11 Multiobjective De Novo Design of Synthetically Accessible Compounds 267
Valerie J. Gillet, Michael J. Bodkin, and Dimitar Hristozov

11.1 Introduction 267

11.2 Design of Synthetically Accessible Compounds 269

11.3 Synthetic Accessibility Using Reaction Vectors 270

11.4 De Novo Design Using Evolutionary Algorithms 276

11.4.1 Optimizing Multiple Objectives 277

11.4.2 Multiobjective De Novo Design 279

11.4.3 Multiobjective De Novo Design Using Reaction Vectors 280

11.5 Conclusions 282

Acknowledgments 283

References 283

12 De Novo Design of Ligands against Multitarget Profiles 287
Jérémy Besnard and Andrew L. Hopkins

12.1 Introduction 287

12.2 Automating the Creativity of Ligand Design 289

12.3 Evolutionary Algorithm 294

12.4 Experimental Validation 295

12.5 Reducing Antitarget Activity 296

12.6 Optimizing D4 Receptor Potency 301

12.7 Designing Novel Ligands to a Defined Profile 301

12.8 Conclusion 304

Acknowledgments 306

References 306

13 Construction of Drug-Like Compounds by Markov Chains 311
Peter S. Kutchukian, Salla I. Virtanen, Eugen Lounkine, Meir Glick, and Eugene I. Shakhnovich

13.1 Introduction 311

13.2 FOG Algorithm and Library Generation 313

13.3 Applications 314

13.3.1 Overview 314

13.3.2 Target Class Prediction of FOG Compounds 314

13.3.3 Design of BACE-1 Inhibitors with FOG 316

13.4 Conclusion 319

Acknowledgments 320

References 320

14 Coping with Combinatorial Space in Molecular Design 325
Florian Lauck and Matthias Rarey

14.1 Introduction 325

14.2 Chemical Space 326

14.2.1 Size Estimation of Chemical Space 327

14.2.2 Enumeration of Chemical Subspaces 328

14.3 Combinatorial Space 330

14.3.1 Generation of Combinatorial Spaces 332

14.3.2 Manipulation of Combinatorial Space 335

14.3.3 Querying Combinatorial Spaces 336

14.3.4 Other Applications of Combinatorial Space 340

14.3.5 Markush Structures 341

14.4 Visualization 342

14.5 Conclusion 343

References 343

15 Fragment-Based Design of Focused Compound Libraries 349
Uta Lessel

15.1 Introduction 349

15.2 General Workflow 351

15.3 Fragment Space 352

15.4 Query 355

15.5 FTrees Fragment Space Search 356

15.6 Scaffold Selection 356

15.7 Design of Focused Libraries 359

15.8 Application Example 360

15.9 Summary and Conclusions 366

Acknowledgments 367

References 367

16 Free Energy Methods in Ligand Design 373
Yvonne Westermaier and Roderick E. Hubbard

16.1 Free Energy (FE) Methods in Lead Optimization (LO) 373

16.1.1 FE Methods: An Emerging Tool in Industry? 374

16.1.2 Finding the Needle in a Haystack: The Role of FE Methods in Fine-Tuning Ligand Discovery 375

16.2 The Variety of In Silico Binding Affinity Methods 377

16.2.1 Thermodynamic Integration (TI) and Alchemical Transformations 377

16.2.2 Free Energy Perturbation (FEP) 378

16.2.3 Potential of Mean Force (PMF) Calculations 379

16.2.4 Nonequilibrium Approaches 380

16.2.5 Other MM-Based Methods 381

16.3 The Choice of a Method for Calculating Binding FE 382

16.3.1 MM-PBSA and MM-GBSA versus FEP/TI 383

16.3.2 LIE versus FEP/TI 383

16.3.3 PMF versus FEP 383

16.3.4 PMF versus TI 383

16.3.5 TI versus FEP 384

16.3.6 PMF/TI/FEP: Absolute or Relative Binding FEs? 384

16.3.7 Equilibrium versus Nonequilibrium Methods 385

16.4 Experimental Data 385

16.5 Current Issues 385

16.6 Practical Examples 387

16.6.1 Studies on Model Systems 387

16.6.2 FE Methods Applied to Pharmaceutically Relevant Systems 389

16.7 Miscellaneous Issues 395

16.8 Best Practices 396

16.9 Conclusions and Outlook 397

Acknowledgments 398

Abbreviations 398

References 399

17 Bioisosteres in De Novo Design 417
Nicholas C. Firth, Julian Blagg, and Nathan Brown

17.1 Introduction 417

17.2 History of Isosterism and Bioisosterism 418

17.3 Methods for Bioisosteric Replacement 421

17.3.1 Databases 422

17.3.2 Descriptors 424

17.4 Exemplar Applications 427

17.4.1 Information-Based Bioisosteric Replacement 427

17.4.2 Drug Guru 429

17.4.3 SkelGen 431

17.5 Conclusions 433

Acknowledgments 433

References 434

18 Peptide Design by Nature-Inspired Algorithms 437
Jan A. Hiss and Gisbert Schneider

18.1 Template-Based Design 437

18.2 Nature-Inspired Optimization 441

18.2.1 Evolutionary Algorithms 444

18.2.2 Particle Swarm Optimization 446

18.2.3 Ant Colony Optimization 449

18.3 Worked Example: De Novo Design of MHC-I Binding Peptides by Ant Colony Optimization 450

18.4 Chemical Modification 456

18.4.1 Backbone Cyclization 456

18.4.2 Stapling 458

18.4.3 End-Capping 458

18.4.4 Sugar-Coating 459

18.5 Conclusions and Outlook 460

Acknowledgments 461

References 461

19 De Novo Computational Protein Design 467
Jeffery G. Saven

19.1 Introduction 467

19.2 Elements of Computational Protein Design 470

19.2.1 Target Structures 470

19.2.2 Degrees of Freedom: Amino Acids and Side-Chain Conformations 470

19.2.3 Energy Functions 471

19.2.4 Solvation 472

19.2.5 Foldability Criteria and Negative Design 472

19.2.6 Sequence Search and Characterization 473

19.3 Efforts in Theoretically Guided Protein Design 477

19.3.1 Toward Catalysis, Redox Activity, and Enzymes 477

19.3.2 De Novo Design and Redesign 478

19.3.3 Protein Reengineering 479

19.3.4 Cofactors and Nonbiological Protein Assemblies 480

19.3.5 Membrane Proteins 481

19.3.6 Protein–Protein Interactions and Protein Assemblies 483

19.4 Conclusion 485

Acknowledgments 485

References 486

20 De Novo Design of Nucleic Acid Structures 495
Barbara Saccà, Andreas Sprengel, and Udo Feldkamp

20.1 Introduction 495

20.2 DNA-Branched Structures 499

20.2.1 De Novo Design of DNA Junctions 499

20.2.2 Tile-to-Tile Binding 504

20.3 Scaffolded DNA Origami Design 505

20.3.1 Monolayer DNA Origami 506

20.3.2 Multilayer DNA Origami 509

20.4 Alternative DNA Designs: between Junctions and Origami 511

20.5 Conclusions 514

Acknowledgments 515

References 515

21 RNA Aptamer Design 519
Cindy Meyer, Ulrich Hahn, and Andrew E. Torda

21.1 Aptamers and Design 519

21.2 Riboswitches and Aptamers 520

21.3 SELEX 521

21.3.1 Introduction 521

21.3.2 The Method 522

21.3.3 Technical Challenges and Recent Developments in SELEX 526

21.4 Speeding Up SELEX by Computational Methods 526

21.4.1 Design of Structures 529

21.5 Structures and Probing Methods 530

21.6 Functional Analyses (In Vitro and In Vivo) 532

21.7 Problems 533

21.8 Future Perspectives 535

References 536

Index 543

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