Open Access Databases and Datasets for Drug Discovery

Antoine Daina is a Senior Scientist at the Molecular Modelling Group of the SIB Swiss Institute of Bioinformatics in charge of methodological developments in the SwissDrugDesign program.Michael Przewosny has over 20 years of experience in pharmaceutical research and drug discovery, having worked as laboratory manager for different pharmaceutical companies.Vincent Zoete is a Group Leader at the Molecular Modelling Group of the SIB Swiss Institute of Bioinformatics and an Associate Professor at the University of Lausanne, Department of Oncology UNIL-CHUV, Ludwig Institute for Cancer Research.

• Series Editors Preface xiiiRaimund Mannhold – A Personal Obituary from the Series Editors xviiA Personal Foreword xxi1 Open Access Databases and Datasets for Computer-Aided Drug Design. A Short List Used in the Molecular Modelling Group of the SIB 1Antoine Daina, María José Ojeda-Montes, Maiia E. Bragina, Alessandro Cuozzo, Ute F. Röhrig, Marta A.S. Perez, and Vincent ZoeteReferences 30Part I Small Molecules 392 PubChem: A Large-Scale Public Chemical Database for Drug Discovery 41Sunghwan Kim and Evan E. Bolton2.1 Introduction 412.2 Data Content and Organization 422.3 Tools and Services 452.3.1 PubChem Search 452.3.2 Summary Pages 482.3.3 Literature Knowledge Panel 492.3.4 2D and 3D Neighbors 502.3.5 Classification Browser 512.3.6 Identifier Exchange Service 522.3.7 Programmatic Access 522.3.8 PubChem FTP Site and PubChemRDF 532.4 Drug- and Lead-Likeness of PubChem Compounds 542.5 Bioactivity Data in PubChem 562.6 Comparison with Other Databases 572.7 Use of PubChem Data for Drug Discovery 582.8 Summary 59Acknowledgments 60References 603 DrugBank Online: A How-to Guide 67Christen M. Klinger, Jordan Cox, Denise So, Teira Stauth, Michael Wilson, Alex Wilson, and Craig Knox3.1 Introduction 673.2 DrugBank 683.2.1 Overview of DrugBank 683.2.2 DrugBank Datasets 693.2.2.1 Drug Cards: An Overview and Navigation Guide 703.2.2.2 Identification 703.2.2.3 Pharmacology 713.2.2.4 Categories 733.2.2.5 Properties 733.2.2.6 Targets, Enzymes, Carriers, and Transporters 733.2.2.7 References 773.3 Protocols 773.3.1 General Workflows 773.3.1.1 Using DrugBank Online’s Search Functionality 773.3.1.2 Using DrugBank Online’s Advanced Search Functionality 803.3.1.3 Browsing Drugs Using DrugBank Online’s Drug Categories 833.3.2 Identifying Chemicals and Relevant Sequences 863.3.2.1 Searching Using Chemical Structure Search 863.3.2.2 Using Sequence Search to Find Similar Targets 893.3.3 Extracting DrugBank Datasets for ml 933.4 Research Using DrugBank 943.5 Discussion and Conclusions 95References 964 Bioisosteric Replacement for Drug Discovery Supported by the SwissBioisostere Database 101Antoine Daina, Alessandro Cuozzo, Marta A.S. Perez, and Vincent Zoete4.1 Introduction 1014.1.1 Concept of Isosterism and Bioisosterism 1014.1.2 Classical vs. Non-classical Bioisostere and Further Molecular Replacements 1024.1.3 Bioisosteric Replacement in Drug Discovery 1054.2 Construction and Dissemination of SwissBioisostere 1064.2.1 Intention and Requirements 1064.2.2 Bioactivity Data 1074.2.3 Nonsupervised Matched Molecular Pair Analysis 1084.2.4 Database 1084.2.5 Web Interface 1094.3 Content of SwissBioisostere 1114.3.1 Global Content 1114.3.2 Biological and Chemical Contexts 1124.3.3 Fragment Shape Diversity 1134.4 Usage of SwissBioisostere 1154.4.1 Website Usage 1154.4.2 Most Frequent Requests 1174.4.3 Examples Related to Drug Discovery 1174.4.3.1 Use Cases 1174.4.3.2 Replacing Unwanted Chemical Groups 1184.4.3.3 Optimization of Passive Absorption and Blood–Brain Barrier Diffusion 1224.4.3.4 Reduction of Flexibility 1244.4.3.5 Reduction of Aromaticity/Escape from Flatland 1284.5 Conclusive Remarks 133Acknowledgment 133References 133Part II Macromolecular Targets and Diseases 1395 The Protein Data Bank (PDB) and Macromolecular Structure Data Supporting Computer-Aided Drug Design 141David Armstrong, John Berrisford, Preeti Choudhary, Lukas Pravda, James Tolchard, Mihaly Varadi, and Sameer Velankar5.1 Introduction 1415.2 Small Molecule Data in Protein Data Bank (PDB) Entries 1425.2.1 What Data are in the PDB Archive? 1425.2.2 Definition of Small Molecules in OneDep 1455.3 Small Molecule Dictionaries 1465.3.1 wwPDB Chemical Component Dictionary (CCD) 1465.3.2 The Peptide Reference Dictionary 1475.4 Additional Ligand Annotations in the PDB Archive 1485.4.1 Linkage Information 1485.4.2 Carbohydrates 1495.5 Validation of Ligands in the Worldwide Protein Data Bank (wwPDB) 1505.5.1 Various Criteria and Software Used for Validating Ligand in Validation Reports 1505.5.2 Identification of Ligand of Interest (LOI) 1515.5.3 Geometric and Conformational Validation 1525.5.4 Ligand Fit to Experimental Electron Density Validation 1525.5.5 Accessing wwPDB Validation Reports from PDBe Entry Pages 1545.5.6 Other Planned Improvements to Enhance Ligand Validation 1545.6 PDBe Tools for Ligand Analysis 1555.6.1 Ligand Interactions 1555.6.1.1 Classifying Ligand Interactions 1555.6.1.2 Data Availability 1565.6.2 Ligand Environment Component 1565.6.3 Chemistry Process and FTP 1585.6.4 PDBeChem Pages 1585.7 Ligand-Related Annotations in the PDBe-KB 1585.7.1 Introduction to PDBe-KB 1585.7.2 Data Access Mechanisms for Ligand-Related Annotations 1605.7.3 Ligand-Related Annotations on the Aggregated Views of Proteins 1625.8 Case Study: Using PDB Data to Support Drug Discovery 1645.9 Conclusions and Outlook 1655.9.1 Upcoming Features and Improvements 166References 1676 The SWISS-MODEL Repository of 3D Protein Structures and Models 175Xavier Robin, Andrew Mark Waterhouse, Stefan Bienert, Gabriel Studer, Leila T. Alexander, Gerardo Tauriello, Torsten Schwede, and Joana Pereira6.1 Introduction 1756.2 SMR Database Content and Model Providers 1766.2.1 PDB 1776.2.2 Swiss-model 1776.2.3 AlphaFold Database 1796.2.4 ModelArchive 1806.3 Protein Feature Annotation and Cross-References to Computational Resources 1816.3.1 Structural Features, Ligands, and Oligomers 1816.3.2 SWISS-MODEL associated tools 1826.3.3 Web and API Access 1836.4 Quality Estimates and Benchmarking 1886.5 Binding Site Conformational States 1896.6 SMR and Computer-Aided Structure-based Drug Design 1906.7 Conclusion and Outlook 191References 1937 PDB-REDO in Computational-Aided Drug Design (CADD) 201Ida de Vries, Anastassis Perrakis, and Robbie P. Joosten7.1 History and Concepts 2017.1.1 X-ray Structure Models 2017.1.2 PDB-REDO Development 2027.1.2.1 First Uniformity 2037.1.2.2 Automatic Rebuilding of Protein Backbone and Side Chains 2037.1.2.3 Automated Model Completion Approaches 2047.1.2.4 Systematic Integration of Structural Knowledge 2057.1.2.5 Overview of PDB-REDO Pipeline 2057.2 Structure Improvements by PDB-REDO 2067.2.1 Parametrization and Rebuilding Effects on Small Molecule Ligands 2067.2.1.1 Re-refinement Improves Ligand Conformation 2067.2.1.2 Side Chain Rebuilding Improves Ligand Binding Sites 2077.2.1.3 Histidine Flip and Improved Ligand Parameterization 2087.2.2 Building of Protein Loops and Ligands into Protein Structure Models 2107.2.2.1 Loop Building Completes a Binding Site Region 2107.2.2.2 Loop Building Results in Improved Binding Sites 2117.2.2.3 Building new Compounds into Density 2127.2.3 Nucleic Acid Improvements by PDB-REDO 2137.2.4 Glycoprotein Structure Model Rebuilding 2147.2.5 Metal Binding Sites 2147.2.6 Limitations of the PDB-REDO Databank 2167.3 Access the PDB-REDO Databank and Metadata 2187.3.1 Downloading and Inspecting Individual PDB-REDO Entries 2187.3.2 Data Available in PDB-REDO Entries 2207.3.3 Usage of the Uniform and FAIR Validation Data 2207.3.4 Creating Datasets from the PDB-REDO Databank 2227.3.5 Submitting Structure Models to the PDB-REDO Pipeline 2237.4 Conclusions 223Acknowledgments and Funding 224List of Abbreviations and Symbols 224References 2258 Pharos and TCRD: Informatics Tools for Illuminating Dark Targets 231Keith 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 Mathé8.1 Introduction 2318.2 Methods 2338.2.1 Data Organization 2338.2.1.1 Target Alignment 2348.2.1.2 Disease Alignment 2348.2.1.3 Ligand Alignment 2348.2.1.4 Data and UI Updates 2358.2.2 Programmatic Access and Data Download 2358.2.3 UI Organization 2358.2.3.1 List Pages 2368.2.3.2 Details Pages 2368.2.3.3 Search 2388.2.3.4 Tutorials 2408.2.4 Analysis Methods Within Pharos 2408.2.4.1 Searching for Ligands 2408.2.4.2 Finding Targets by Amino Acid Sequence 2418.2.4.3 Finding Targets with Similar Annotations 2418.2.4.4 Finding Targets with Predicted Activity 2418.2.4.5 Enrichment Scores for Filter Values 2418.3 Use Cases 2428.3.1 Hypothesizing the Role of a Dark Target 2428.3.1.1 Primary Documentation 2428.3.1.2 List Analysis 2478.3.1.3 Downloading Data 2518.3.1.4 Variations on this Use Case 2518.3.2 Characterizing a Novel Chemical Compound 2518.3.2.1 Finding Predicted Targets 2528.3.2.2 Analyzing Similar Ligands 2548.3.2.3 Ligand Details Pages 2568.3.2.4 Variations on this Use Case 2578.3.3 Investigating Diseases 2608.4 Discussion 262Funding 264References 264Part III Users’ Points of View 2699 Mining for Bioactive Molecules in Open Databases 271Guillem Macip, Júlia Mestres-Truyol, Pol Garcia-Segura, Bryan Saldivar-Espinoza, Santiago Garcia-Vallvé, and Gerard Pujadas9.1 Introduction 2719.2 Main Tools for Virtual Screening 2729.2.1 ADMET and PAINS Filtering 2729.2.2 Protein–Ligand Docking 2749.2.3 Pharmacophore Search 2759.2.4 Shape/Electrostatic Similarity 2769.2.5 Protein-Structure Databases 2779.2.6 The Protein Data Bank 2789.2.7 The PDB-REDO Databank 2789.2.8 The SWISS-MODEL Repository 2799.2.9 The AlphaFold Protein Structure Database 2799.3 Validating Binding Site and Ligand Coordinates in Three-Dimensional Protein Complexes 2809.4 Databases for Searching New Drugs 2819.4.1 Coconut 2819.4.2 GDBs 2829.4.3 Zinc 20 2829.5 Databases of Bioactive Molecules 2829.5.1 The BindingDB Database 2839.5.2 PubChem 2839.5.3 ChEMBL 2849.6 Databases of Inactive/Decoy Molecules 2859.6.1 Collecting Experimentally Inactive Compounds from PubChem 2859.6.2 Collecting Presumed Inactive Compounds from Decoy Databases 2859.6.3 Building Custom-Based Decoy Sets 2869.7 Main Metrics for Evaluating the Success of a Virtual Screening 2869.8 Concluding Remarks 288References 28910 Open Access Databases – An Industrial View 299Michael Przewosny10.1 Academic vs. Industrial Research 29910.2 Scaffold-Hopping 31010.3 Virtual-Screening 311Abbreviations 312References 313Index 317