Computational drug design a guide for computational and medicinal chemists

Computational Drug Design covers all of the major computational drug design techniques in use today, focusing on the process that pharmaceutical chemists employ to design a new drug molecule. The discussions of which computational tools to use and when and how to use them are all based on typical pharmaceutical industry drug design processes.

Following an introduction, the book is divided into three parts:

Part One, The Drug Design Process, sets forth a variety of design processes suitable for a number of different drug development scenarios and drug targets. The author demonstrates how computational techniques are typically used during the design process, helping readers choose the best computational tools to meet their goals.

Part Two, Computational Tools and Techniques, offers a series of chapters, each one dedicated to a single computational technique. Readers discover the strengths and weaknesses of each technique. Moreover, the book tabulates comparative accuracy studies, giving readers an unbiased comparison of all the available techniques.

Part Three, Related Topics, addresses new, emerging, and complementary technologies, including bioinformatics, simulations at the cellular and organ level, synthesis route prediction, proteomics, and prodrug approaches.

The book's accompanying supplementary materials, a special feature, offers graphics of the molecular structures and dynamic reactions discussed in the book as well as demos from computational drug design software companies..

Computational Drug Design is ideal for both students and professionals in drug design, helping them choose and take full advantage of the best computational tools available.

Author Notes

David C. Young, PhD, is HPC Computational Specialist for Computer Sciences Corp., under contract to the Alabama Supercomputer Authority, where he heads user and application support for research and educational activities. Dr. Young has extensive experience in designing drugs and writing drug design software. He is the author of Computational Chemistry: A Practical Guide for Applying Techniques to Real World Problems, also published by Wiley.

Preface	p. xv
Acknowledgments	p. xix
About the Author	p. xxi
Symbols Used in This Book	p. xxiii
Book Abstract	p. xxix
1 Introduction	p. 1
1.1 A Difficult Problem	p. 1
1.2 An Expensive Problem	p. 2
1.3 Where Computational Techniques are Used	p. 3
Bibliography	p. 5
Part I The Drug Design Process	p. 7
2 Properties that Make a Molecule a Good Drug	p. 9
2.1 Compound Testing	p. 10
2.1.1 Biochemical Assays	p. 11
2.1.2 Cell-Based Assays	p. 13
2.1.3 Animal Testing	p. 14
2.1.4 Human Clinical Trials	p. 15
2.2 Molecular Structure	p. 16
2.2.1 Activity	p. 16
2.2.2 Bioavailability and Toxicity	p. 24
2.2.3 Drug Side Effects	p. 26
2.2.4 Multiple Drug Interactions	p. 26
2.3 Metrics for Drug-Likeness	p. 27
2.4 Exceptions to the Rules	p. 33
Bibliography	p. 35
3 Target Identification	p. 41
3.1 Primary Sequence and Metabolic Pathway	p. 41
3.2 Crystallography	p. 43
3.3 2D NMR	p. 44
3.4 Homology Models	p. 45
3.5 Protein Folding	p. 45
Bibliography	p. 46
4 Target Characterization	p. 47
4.1 Analysis of Target Mechanism	p. 47
4.1.1 Kinetics and Crystallography	p. 48
4.1.2 Automated Crevice Detection	p. 48
4.1.3 Transition Structures and Reaction Coordinates	p. 49
4.1.4 Molecular Dynamics Simulations	p. 49
4.2 Where the Target is Expressed	p. 50
4.3 Pharmacophore Identification	p. 50
4.4 Choosing an Inhibitor Mechanism	p. 51
Bibliography	p. 52
5 The Drug Design Process for a Known Protein Target	p. 53
5.1 The Structure-Based Design Process	p. 53
5.2 Initial Hits	p. 55
5.3 Compound Refinement	p. 56
5.4 ADMET	p. 67
5.5 Drug Resistance	p. 67
Bibliography	p. 68
6 The Drug Design Process for an Unknown Target	p. 71
6.1 The Ligand-Based Design Process	p. 71
6.2 Initial Hits	p. 72
6.3 Compound Refinement	p. 73
6.4 ADMET	p. 74
Bibliography	p. 74
7 Drug Design for Other Targets	p. 75
7.1 DNA Binding	p. 76
7.2 RNA as a Target	p. 78
7.3 Allosteric Sites	p. 79
7.4 Receptor Targets	p. 80
7.5 Steroids	p. 81
7.6 Targets inside Cells	p. 82
7.7 Targets within the Central Nervous System	p. 83
7.8 Irreversibly Binding Inhibitors	p. 84
7.9 Upregulating Target Activity	p. 84
Bibliography	p. 85
8 Compound Library Design	p. 87
8.1 Targeted Libraries versus Diverse Libraries	p. 87
8.2 From Fragments versus from Reactions	p. 89
8.3 Non-Enumerative Techniques	p. 90
8.4 Drug-Likeness and Synthetic Accessibility	p. 91
8.5 Analyzing Chemical Diversity and Spanning known Chemistries	p. 93
8.6 Compound Selection Techniques	p. 96
Bibliography p. 99
Part II Computational Tools and Techniques	p. 103
9 Homology Model Building	p. 105
9.1 How much Similarity is Enough?	p. 106
9.2 Steps for Building a Homology Model	p. 107
9.2.1 Step 1: Template Identification	p. 108
9.2.2 Step 2: Alignment between the Unknown and the Template	p. 108
9.2.3 Step 3: Manual Adjustments to the Alignment	p. 110
9.2.4 Step 4: Replace Template Side Chains with Model Side Chains	p. 111
9.2.5 Step 5: Adjust Model for Insertions and Deletions	p. 111
9.2.6 Step 6: Optimization of the Model	p. 112
9.2.7 Step 7: Model Validation	p. 112
9.2.8 Step 8: If Errors are Found, Iterate Back to Previous Steps	p. 115
9.3 Reliability of Results	p. 116
Bibliography	p. 117
10 Molecular Mechanics	p. 119
10.1 A Really Brief Introduction to Molecular Mechanics	p. 119
10.2 Force Fields for Drug Design	p. 121
Bibliography	p. 123
11 Protein Folding	p. 125
11.1 The Difficulty of the Problem	p. 125
11.2 Algorithms	p. 127
11.3 Reliability of Results	p. 129
11.4 Conformational Analysis	p. 130
Bibliography	p. 131
12 Docking	p. 133
12.1 Introduction	p. 133
12.2 Search Algorithms	p. 135
12.2.1 Searching the Entire Space	p. 135
12.2.2 Grid Potentials versus Full Force Field	p. 137
12.2.3 Flexible Active Sites	p. 138
12.2.4 Ligands Covalently Bound to the Active Site	p. 138
12.2.5 Hierarchical Docking Algorithms	p. 139
12.3 Scoring	p. 141
12.3.1 Energy Expressions and Consensus Scoring	p. 141
12.3.2 Binding Free Energies	p. 141
12.3.3 Solvation	p. 144
12.3.4 Ligands Covalently Bound to the Active Site	p. 144
12.3.5 Metrics for Goodness of Fit	p. 144
12.4 Validation of Results	p. 145
12.5 Comparison of Existing Search and Scoring Methods	p. 146
12.6 Special Systems	p. 153
12.7 The Docking Process	p. 155
12.7.1 Protein Preparation	p. 156
12.7.2 Building the Ligand	p. 156
12.7.3 Setting the Bounding Box	p. 157
12.7.4 Docking Options	p. 157
12.7.5 Running the Docking Calculation	p. 158
12.7.6 Analysis of Results	p. 158
Bibliography	p. 159
13 Pharmacophore Models	p. 161
13.1 Components of a Pharmacophore Model	p. 163
13.2 Creating a Pharmacophore Model from Active Compounds	p. 164
13.3 Creating a Pharmacophore Model from the Active Site	p. 166
13.4 Searching Compound Databases	p. 167
13.5 Reliability of Results	p. 168
Bibliography	p. 169
14 QSAR	p. 171
14.1 Conventional QSAR versus 3D-QSAR	p. 171
14.2 The QSAR Process	p. 172
14.3 Descriptors	p. 175
14.4 Automated QSAR Programs	p. 176
14.5 QSAR versus Other Fitting Methods	p. 177
Bibliography	p. 178
15 3D-QSAR	p. 181
15.1 The 3D-QSAR Process	p. 182
15.2 3D-QSAR Software Packages	p. 184
15.3 Summary	p. 184
Bibliography	p. 184
16 Quantum Mechanics in Drug Design	p. 187
16.1 Quantum Mechanics Algorithms and Software	p. 188
16.2 Modeling Systems with Metal Atoms	p. 191
16.3 Increased Accuracy	p. 191
16.4 Computing Reaction Paths	p. 193
16.5 Computing Spectra	p. 193
Bibliography	p. 194
17 De novo and Other AI Techniques	p. 197
17.1 De novo Building of Compounds	p. 198
17.2 Nonquantitative Predictions	p. 201
17.3 Quantitative Predictions	p. 203
Bibliography	p. 205
18 Cheminformatics	p. 207
18.1 Smiles, SLN, and Other Chemical Structure Representations	p. 208
18.2 Similarity and Substructure Searching	p. 209
18.3 2D-to-3D Structure Generation	p. 213
18.4 Clustering Algorithms	p. 214
18.5 Screening Results Analysis	p. 217
18.6 Database Systems	p. 222
Bibliography	p. 223
19 Admet	p. 225
19.1 Oral Bioavailability	p. 227
19.2 Drug Half-Life in the Bloodstream	p. 229
19.3 Blood-Brain Barrier Permeability	p. 231
19.4 Toxicity	p. 231
Bibliography	p. 234
20 Multiobjective Optimization	p. 237
Bibliography	p. 240
21 Automation of Tasks	p. 241
21.1 Built-In Automation Capabilities	p. 241
12.2 Automation Using External Utilities	p. 243
Bibliography	p. 244
Part III Related Topics	p. 245
22 Bioinformatics	p. 247
Bibliography	p. 251
23 Simulations at the Cellular and Organ Level	p. 253
23.1 Cellular Simulations	p. 253
23.2 Organ Simulations	p. 256
Bibliography p. 256
24 Synthesis Route Prediction	p. 259
Bibliography	p. 262
25 Proteomics	p. 263
Bibliography p. 264
26 Prodrug Approaches	p. 267
Bibliography	p. 270
27 Future Developments in Drug Design	p. 273
27.1 Individual Patient Genome Sequencing	p. 273
27.2 Analysis of the Entire Proteome	p. 274
27.3 Drugs Customized for Ethnic Group or Individual Patient	p. 274
27.4 Genetic Manipulation	p. 275
27.5 Cloning	p. 276
27.6 Stem Cells	p. 277
27.7 Longevity	p. 278
Bibliography	p. 279
Appendix About the CD	p. 281
Glossary	p. 285
Index	p. 301

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