| | Contents | |
| | | |
| |
| | Foreword | V |
| | Preface | IX |
| 1 | Molecular Objects and Design Objectives | 1 |
| 1.1 | What is a Molecule? | 1 |
| 1.2 | Simplistic Molecular Representations | 2 |
| 1.3 | The Molecular Surface | 3 |
| 1.4 | Molecular Shape | 7 |
| 1.5 | The Topological Molecular Graph | 8 |
| 1.6 | Molecular Properties and Graph Invariants | 11 |
| 1.7 | The Drug-likeness Concept | 14 |
| 1.8 | Scaffolds, Linkers, and Side-chains | 16 |
| 1.9 | Substructure Similarity and 憫Privileged Motifs创 | 19 |
| 1.10 | Molecules as Strings | 23 |
| 1.11 | Constructing Molecules from Strings | 25 |
| 1.12 | From Elements to Atom Types | 27 |
| 1.13 | Entering the Third Dimension: Automatic Conformer Generation | 29 |
| 1.14 | The 憫Bioactive创 Conformation | 32 |
| | Literature | 33 |
| 2 | Receptor--Ligand Interaction | 35 |
| 2.1 | The Thermodynamics of Protein--Ligand Interaction | 35 |
| 2.2 | The Entropic Contribution | 38 |
| 2.3 | From Theory to Experiment: Ki and IC50 | 45 |
| 2.4 | QSAR: Estimating Quantitative Structure--Activity Relationships | 47 |
| 2.4.1 | Free--Wilson Analysis | 49 |
| 2.4.2 | The Hansch Model | 51 |
| 2.4.3 | 3D-QSAR | 52 |
| 2.5 | Types of Receptor--Ligand Interaction | 54 |
| 2.6 | The 憫Biophore创 Concept | 58 |
| 2.7 | Potential Pharmacophoric Points | 62 |
| 2.8 | The Correlation Vector Approach to Pharmacophore Modeling | 66 |
| 2.9 | 憫Hard Sphere创 and 憫Fuzzy创 Pharmacophore Models | 68 |
| 2.10 | Lessons from Automated Ligand Docking and Scoring: What Works and What Does Not | 74 |
| 2.11 | Fits Like a Glove: Alternative Ligand Binding Modes and Induced Fit Effects | 77 |
| | Literature | 84 |
| 3 | Creating the Design | 87 |
| 3.1 | Why We Need Computer-assisted Molecular Design | 87 |
| 3.2 | The Number of Drug Targets is Limited | 89 |
| 3.3 | Ligand Binding Sites | 100 |
| 3.4 | Ligand-based Design of Compound Libraries | 108 |
| 3.5 | Similar Compounds Do Not Necessarily Interact with Their Target in Similar Ways | 111 |
| 3.6 | The Same Ligand Can Adopt Multiple Binding Modes | 113 |
| 3.7 | GPCRs Represent a Challenging Target Family | 114 |
| 3.8 | Natural Products Are a Source of Inspiration | 116 |
| 3.9 | Transition State Analogs Are Potent Enzyme Inhibitors | 119 |
| 3.10 | New Targets Sometimes Require a New Ligand Design Concept | 123 |
| 3.11 | De novo Design Concepts | 124 |
| 3.12 | Primary and Secondary Constraints in de novo Design | 125 |
| | Literature | 145 |
| 4 | Virtual Screening Triage | 149 |
| 4.1 | The Drug Discovery Pipeline | 150 |
| 4.2 | High-throughput Screening (HTS): Why Is It Successful? | 151 |
| 4.3 | From Hit to Lead | 153 |
| 4.4 | Rationalizing the Design Process | 154 |
| 4.5 | From High to Low Diversity | 156 |
| 4.6 | Quantifying Diversity is Difficult | 160 |
| 4.7 | From Negative Design to Positive Design | 162 |
| 4.8 | Watch Out for Frequent Hitters! | 165 |
| 4.9 | Shape-matching: A Coarse-grained Filtering Step | 167 |
| 4.10 | The Ultimate Goal: Scaffold-hopping | 170 |
| 4.11 | Assessing Chemotype Diversity in Focused Libraries | 171 |
| 4.12 | It Works! Examples of Successful Scaffold-hops Found by Virtual Screening | 172 |
| 4.13 | Case Studies | 178 |
| 4.13.1 | Design of Kv1.5 Ion Channel Modulators | 178 |
| 4.13.2 | Virtual Screening of a Natural-product-derived Combinatorial Library for Novel 5-Lipoxygenase Inhibitors | 183 |
| 4.13.3 | Scaffold de novo Design for Cannabinoid-1 (CB-1) Receptor Ligands | 186 |
| | Literature | 189 |
| 5 | Secondary Design Constraints and Machine Learning | 193 |
| 5.1 | Physicochemistry and Pharmacokinetics | 193 |
| 5.2 | The 憫Rule of 5创 | 197 |
| 5.3 | Pharmacokinetics | 197 |
| 5.4 | Absorption | 199 |
| 5.5 | Distribution | 202 |
| 5.6 | Metabolism | 203 |
| 5.7 | Elimination | 206 |
| 5.8 | Toxicity | 207 |
| 5.9 | Prodrugs and Bioisosteres | 210 |
| 5.10 | Machine Learning Methods Support Lead Finding and Optimization | 212 |
| 5.11 | An Important Step: Data Scaling | 232 |
| 5.12 | Application of Machine Learning to Compound Library Design | 233 |
| 5.13 | A 憫Pharmacophore Road Map创 | 238 |
| 5.14 | Case Studies | 242 |
| 5.14.1 | Predicting Cross-activities of Allosteric Modulators of Metabotropic Glutamate Receptors (mGluR) | 242 |
| 5.14.2 | Dopamine D3 Antagonists and ACE Inhibitors | 243 |
| 5.14.3 | An Artificial Ant System for Combinatorial Optimization | 248 |
| | Literature | 254 |
| | Subject Index | 257 |
