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Summary
Summary
Helps you choose the right computational tools and techniques to meet your drug design goals
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.
Table of Contents
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 |