<p>Series Editors Preface xiii</p> <p>Raimund Mannhold – A Personal Obituary from the Series Editors xvii</p> <p>A Personal Foreword xxi</p> <p><b>1 Open Access Databases and Datasets for Computer-Aided Drug Design. A Short List Used in the Molecular Modelling Group of the SIB 1<br /> </b><i>Antoine Daina, María José Ojeda-Montes, Maiia E. Bragina, Alessandro Cuozzo, Ute F. Röhrig, Marta A.S. Perez, and Vincent Zoete</i></p> <p>References 30</p> <p><b>Part I Small Molecules 39</b></p> <p><b>2 PubChem: A Large-Scale Public Chemical Database for Drug Discovery 41<br /> </b><i>Sunghwan Kim and Evan E. Bolton</i></p> <p>2.1 Introduction 41</p> <p>2.2 Data Content and Organization 42</p> <p>2.3 Tools and Services 45</p> <p>2.3.1 PubChem Search 45</p> <p>2.3.2 Summary Pages 48</p> <p>2.3.3 Literature Knowledge Panel 49</p> <p>2.3.4 2D and 3D Neighbors 50</p> <p>2.3.5 Classification Browser 51</p> <p>2.3.6 Identifier Exchange Service 52</p> <p>2.3.7 Programmatic Access 52</p> <p>2.3.8 PubChem FTP Site and PubChemRDF 53</p> <p>2.4 Drug- and Lead-Likeness of PubChem Compounds 54</p> <p>2.5 Bioactivity Data in PubChem 56</p> <p>2.6 Comparison with Other Databases 57</p> <p>2.7 Use of PubChem Data for Drug Discovery 58</p> <p>2.8 Summary 59</p> <p>Acknowledgments 60</p> <p>References 60</p> <p><b>3 DrugBank Online: A How-to Guide 67<br /> </b><i>Christen M. Klinger, Jordan Cox, Denise So, Teira Stauth, Michael Wilson, Alex Wilson, and Craig Knox</i></p> <p>3.1 Introduction 67</p> <p>3.2 DrugBank 68</p> <p>3.2.1 Overview of DrugBank 68</p> <p>3.2.2 DrugBank Datasets 69</p> <p>3.2.2.1 Drug Cards: An Overview and Navigation Guide 70</p> <p>3.2.2.2 Identification 70</p> <p>3.2.2.3 Pharmacology 71</p> <p>3.2.2.4 Categories 73</p> <p>3.2.2.5 Properties 73</p> <p>3.2.2.6 Targets, Enzymes, Carriers, and Transporters 73</p> <p>3.2.2.7 References 77</p> <p>3.3 Protocols 77</p> <p>3.3.1 General Workflows 77</p> <p>3.3.1.1 Using DrugBank Online’s Search Functionality 77</p> <p>3.3.1.2 Using DrugBank Online’s Advanced Search Functionality 80</p> <p>3.3.1.3 Browsing Drugs Using DrugBank Online’s Drug Categories 83</p> <p>3.3.2 Identifying Chemicals and Relevant Sequences 86</p> <p>3.3.2.1 Searching Using Chemical Structure Search 86</p> <p>3.3.2.2 Using Sequence Search to Find Similar Targets 89</p> <p>3.3.3 Extracting DrugBank Datasets for ml 93</p> <p>3.4 Research Using DrugBank 94</p> <p>3.5 Discussion and Conclusions 95</p> <p>References 96</p> <p><b>4 Bioisosteric Replacement for Drug Discovery Supported by the SwissBioisostere Database 101<br /> </b><i>Antoine Daina, Alessandro Cuozzo, Marta A.S. Perez, and Vincent Zoete</i></p> <p>4.1 Introduction 101</p> <p>4.1.1 Concept of Isosterism and Bioisosterism 101</p> <p>4.1.2 Classical vs. Non-classical Bioisostere and Further Molecular Replacements 102</p> <p>4.1.3 Bioisosteric Replacement in Drug Discovery 105</p> <p>4.2 Construction and Dissemination of SwissBioisostere 106</p> <p>4.2.1 Intention and Requirements 106</p> <p>4.2.2 Bioactivity Data 107</p> <p>4.2.3 Nonsupervised Matched Molecular Pair Analysis 108</p> <p>4.2.4 Database 108</p> <p>4.2.5 Web Interface 109</p> <p>4.3 Content of SwissBioisostere 111</p> <p>4.3.1 Global Content 111</p> <p>4.3.2 Biological and Chemical Contexts 112</p> <p>4.3.3 Fragment Shape Diversity 113</p> <p>4.4 Usage of SwissBioisostere 115</p> <p>4.4.1 Website Usage 115</p> <p>4.4.2 Most Frequent Requests 117</p> <p>4.4.3 Examples Related to Drug Discovery 117</p> <p>4.4.3.1 Use Cases 117</p> <p>4.4.3.2 Replacing Unwanted Chemical Groups 118</p> <p>4.4.3.3 Optimization of Passive Absorption and Blood–Brain Barrier Diffusion 122</p> <p>4.4.3.4 Reduction of Flexibility 124</p> <p>4.4.3.5 Reduction of Aromaticity/Escape from Flatland 128</p> <p>4.5 Conclusive Remarks 133</p> <p>Acknowledgment 133</p> <p>References 133</p> <p><b>Part II Macromolecular Targets and Diseases 139</b></p> <p><b>5 The Protein Data Bank (PDB) and Macromolecular Structure Data Supporting Computer-Aided Drug Design 141<br /> </b><i>David Armstrong, John Berrisford, Preeti Choudhary, Lukas Pravda, James Tolchard, Mihaly Varadi, and Sameer Velankar</i></p> <p>5.1 Introduction 141</p> <p>5.2 Small Molecule Data in Protein Data Bank (PDB) Entries 142</p> <p>5.2.1 What Data are in the PDB Archive? 142</p> <p>5.2.2 Definition of Small Molecules in OneDep 145</p> <p>5.3 Small Molecule Dictionaries 146</p> <p>5.3.1 wwPDB Chemical Component Dictionary (CCD) 146</p> <p>5.3.2 The Peptide Reference Dictionary 147</p> <p>5.4 Additional Ligand Annotations in the PDB Archive 148</p> <p>5.4.1 Linkage Information 148</p> <p>5.4.2 Carbohydrates 149</p> <p>5.5 Validation of Ligands in the Worldwide Protein Data Bank (wwPDB) 150</p> <p>5.5.1 Various Criteria and Software Used for Validating Ligand in Validation Reports 150</p> <p>5.5.2 Identification of Ligand of Interest (LOI) 151</p> <p>5.5.3 Geometric and Conformational Validation 152</p> <p>5.5.4 Ligand Fit to Experimental Electron Density Validation 152</p> <p>5.5.5 Accessing wwPDB Validation Reports from PDBe Entry Pages 154</p> <p>5.5.6 Other Planned Improvements to Enhance Ligand Validation 154</p> <p>5.6 PDBe Tools for Ligand Analysis 155</p> <p>5.6.1 Ligand Interactions 155</p> <p>5.6.1.1 Classifying Ligand Interactions 155</p> <p>5.6.1.2 Data Availability 156</p> <p>5.6.2 Ligand Environment Component 156</p> <p>5.6.3 Chemistry Process and FTP 158</p> <p>5.6.4 PDBeChem Pages 158</p> <p>5.7 Ligand-Related Annotations in the PDBe-KB 158</p> <p>5.7.1 Introduction to PDBe-KB 158</p> <p>5.7.2 Data Access Mechanisms for Ligand-Related Annotations 160</p> <p>5.7.3 Ligand-Related Annotations on the Aggregated Views of Proteins 162</p> <p>5.8 Case Study: Using PDB Data to Support Drug Discovery 164</p> <p>5.9 Conclusions and Outlook 165</p> <p>5.9.1 Upcoming Features and Improvements 166</p> <p>References 167</p> <p><b>6 The SWISS-MODEL Repository of 3D Protein Structures and Models 175<br /> </b><i>Xavier Robin, Andrew Mark Waterhouse, Stefan Bienert, Gabriel Studer, Leila T. Alexander, Gerardo Tauriello, Torsten Schwede, and Joana Pereira</i></p> <p>6.1 Introduction 175</p> <p>6.2 SMR Database Content and Model Providers 176</p> <p>6.2.1 PDB 177</p> <p>6.2.2 Swiss-model 177</p> <p>6.2.3 AlphaFold Database 179</p> <p>6.2.4 ModelArchive 180</p> <p>6.3 Protein Feature Annotation and Cross-References to Computational Resources 181</p> <p>6.3.1 Structural Features, Ligands, and Oligomers 181</p> <p>6.3.2 SWISS-MODEL associated tools 182</p> <p>6.3.3 Web and API Access 183</p> <p>6.4 Quality Estimates and Benchmarking 188</p> <p>6.5 Binding Site Conformational States 189</p> <p>6.6 SMR and Computer-Aided Structure-based Drug Design 190</p> <p>6.7 Conclusion and Outlook 191</p> <p>References 193</p> <p><b>7 PDB-REDO in Computational-Aided Drug Design (CADD) 201<br /> </b><i>Ida de Vries, Anastassis Perrakis, and Robbie P. Joosten</i></p> <p>7.1 History and Concepts 201</p> <p>7.1.1 X-ray Structure Models 201</p> <p>7.1.2 PDB-REDO Development 202</p> <p>7.1.2.1 First Uniformity 203</p> <p>7.1.2.2 Automatic Rebuilding of Protein Backbone and Side Chains 203</p> <p>7.1.2.3 Automated Model Completion Approaches 204</p> <p>7.1.2.4 Systematic Integration of Structural Knowledge 205</p> <p>7.1.2.5 Overview of PDB-REDO Pipeline 205</p> <p>7.2 Structure Improvements by PDB-REDO 206</p> <p>7.2.1 Parametrization and Rebuilding Effects on Small Molecule Ligands 206</p> <p>7.2.1.1 Re-refinement Improves Ligand Conformation 206</p> <p>7.2.1.2 Side Chain Rebuilding Improves Ligand Binding Sites 207</p> <p>7.2.1.3 Histidine Flip and Improved Ligand Parameterization 208</p> <p>7.2.2 Building of Protein Loops and Ligands into Protein Structure Models 210</p> <p>7.2.2.1 Loop Building Completes a Binding Site Region 210</p> <p>7.2.2.2 Loop Building Results in Improved Binding Sites 211</p> <p>7.2.2.3 Building new Compounds into Density 212</p> <p>7.2.3 Nucleic Acid Improvements by PDB-REDO 213</p> <p>7.2.4 Glycoprotein Structure Model Rebuilding 214</p> <p>7.2.5 Metal Binding Sites 214</p> <p>7.2.6 Limitations of the PDB-REDO Databank 216</p> <p>7.3 Access the PDB-REDO Databank and Metadata 218</p> <p>7.3.1 Downloading and Inspecting Individual PDB-REDO Entries 218</p> <p>7.3.2 Data Available in PDB-REDO Entries 220</p> <p>7.3.3 Usage of the Uniform and FAIR Validation Data 220</p> <p>7.3.4 Creating Datasets from the PDB-REDO Databank 222</p> <p>7.3.5 Submitting Structure Models to the PDB-REDO Pipeline 223</p> <p>7.4 Conclusions 223</p> <p>Acknowledgments and Funding 224</p> <p>List of Abbreviations and Symbols 224</p> <p>References 225</p> <p><b>8 Pharos and TCRD: Informatics Tools for Illuminating Dark Targets 231<br /> </b><i>Keith J. Kelleher, Timothy K. Sheils, Stephen L. Mathias, Dac-Trung Nguyen, Vishal Siramshetty, Ajay Pillai, Jeremy J. Yang, Cristian G. Bologa, Jeremy S. Edwards, Tudor I. Oprea, and Ewy Mathé</i></p> <p>8.1 Introduction 231</p> <p>8.2 Methods 233</p> <p>8.2.1 Data Organization 233</p> <p>8.2.1.1 Target Alignment 234</p> <p>8.2.1.2 Disease Alignment 234</p> <p>8.2.1.3 Ligand Alignment 234</p> <p>8.2.1.4 Data and UI Updates 235</p> <p>8.2.2 Programmatic Access and Data Download 235</p> <p>8.2.3 UI Organization 235</p> <p>8.2.3.1 List Pages 236</p> <p>8.2.3.2 Details Pages 236</p> <p>8.2.3.3 Search 238</p> <p>8.2.3.4 Tutorials 240</p> <p>8.2.4 Analysis Methods Within Pharos 240</p> <p>8.2.4.1 Searching for Ligands 240</p> <p>8.2.4.2 Finding Targets by Amino Acid Sequence 241</p> <p>8.2.4.3 Finding Targets with Similar Annotations 241</p> <p>8.2.4.4 Finding Targets with Predicted Activity 241</p> <p>8.2.4.5 Enrichment Scores for Filter Values 241</p> <p>8.3 Use Cases 242</p> <p>8.3.1 Hypothesizing the Role of a Dark Target 242</p> <p>8.3.1.1 Primary Documentation 242</p> <p>8.3.1.2 List Analysis 247</p> <p>8.3.1.3 Downloading Data 251</p> <p>8.3.1.4 Variations on this Use Case 251</p> <p>8.3.2 Characterizing a Novel Chemical Compound 251</p> <p>8.3.2.1 Finding Predicted Targets 252</p> <p>8.3.2.2 Analyzing Similar Ligands 254</p> <p>8.3.2.3 Ligand Details Pages 256</p> <p>8.3.2.4 Variations on this Use Case 257</p> <p>8.3.3 Investigating Diseases 260</p> <p>8.4 Discussion 262</p> <p>Funding 264</p> <p>References 264</p> <p><b>Part III Users’ Points of View 269</b></p> <p><b>9 Mining for Bioactive Molecules in Open Databases 271<br /> </b><i>Guillem Macip, Júlia Mestres-Truyol, Pol Garcia-Segura, Bryan Saldivar-Espinoza, Santiago Garcia-Vallvé, and Gerard Pujadas</i></p> <p>9.1 Introduction 271</p> <p>9.2 Main Tools for Virtual Screening 272</p> <p>9.2.1 ADMET and PAINS Filtering 272</p> <p>9.2.2 Protein–Ligand Docking 274</p> <p>9.2.3 Pharmacophore Search 275</p> <p>9.2.4 Shape/Electrostatic Similarity 276</p> <p>9.2.5 Protein-Structure Databases 277</p> <p>9.2.6 The Protein Data Bank 278</p> <p>9.2.7 The PDB-REDO Databank 278</p> <p>9.2.8 The SWISS-MODEL Repository 279</p> <p>9.2.9 The AlphaFold Protein Structure Database 279</p> <p>9.3 Validating Binding Site and Ligand Coordinates in Three-Dimensional Protein Complexes 280</p> <p>9.4 Databases for Searching New Drugs 281</p> <p>9.4.1 Coconut 281</p> <p>9.4.2 GDBs 282</p> <p>9.4.3 Zinc 20 282</p> <p>9.5 Databases of Bioactive Molecules 282</p> <p>9.5.1 The BindingDB Database 283</p> <p>9.5.2 PubChem 283</p> <p>9.5.3 ChEMBL 284</p> <p>9.6 Databases of Inactive/Decoy Molecules 285</p> <p>9.6.1 Collecting Experimentally Inactive Compounds from PubChem 285</p> <p>9.6.2 Collecting Presumed Inactive Compounds from Decoy Databases 285</p> <p>9.6.3 Building Custom-Based Decoy Sets 286</p> <p>9.7 Main Metrics for Evaluating the Success of a Virtual Screening 286</p> <p>9.8 Concluding Remarks 288</p> <p>References 289</p> <p><b>10 Open Access Databases – An Industrial View 299<br /> </b><i>Michael Przewosny</i></p> <p>10.1 Academic vs. Industrial Research 299</p> <p>10.2 Scaffold-Hopping 310</p> <p>10.3 Virtual-Screening 311</p> <p>Abbreviations 312</p> <p>References 313</p> <p>Index 317</p>