Open Access Databases and Datasets for Drug Discovery

Cover
Half Title
Methods and Principles in Medicinal Chemistry Series: Volume 83
Open Access Databases and Datasets for Drug Discovery
Copyright
Contents
Series Editors Preface
Raimund Mannhold – A Personal Obituary from the Series Editors
A Personal Foreword
1. Open Access Databases and Datasets for Computer-Aided Drug Design. A Short List Used in the Molecular Modelling Group of the SIB
	References
Part I. Small Molecules
	2. PubChem: A Large-Scale Public Chemical Database for Drug Discovery
		2.1 Introduction
		2.2 Data Content and Organization
		2.3 Tools and Services
			2.3.1 PubChem Search
			2.3.2 Summary Pages
			2.3.3 Literature Knowledge Panel
			2.3.4 2D and 3D Neighbors
			2.3.5 Classification Browser
			2.3.6 Identifier Exchange Service
			2.3.7 Programmatic Access
			2.3.8 PubChem FTP Site and PubChemRDF
		2.4 Drug- and Lead-Likeness of PubChem Compounds
		2.5 Bioactivity Data in PubChem
		2.6 Comparison with Other Databases
		2.7 Use of PubChem Data for Drug Discovery
		2.8 Summary
		Acknowledgments
		References
	3. DrugBank Online: A How-to Guide
		3.1 Introduction
		3.2 DrugBank
			3.2.1 Overview of DrugBank
			3.2.2 DrugBank Datasets
				3.2.2.1 Drug Cards: An Overview and Navigation Guide
				3.2.2.2 Identification
				3.2.2.3 Pharmacology
				3.2.2.4 Categories
				3.2.2.5 Properties
				3.2.2.6 Targets, Enzymes, Carriers, and Transporters
				3.2.2.7 References
		3.3 Protocols
			3.3.1 GeneralWorkflows
				3.3.1.1 Using DrugBank Online’s Search Functionality
				3.3.1.2 Using DrugBank Online’s Advanced Search Functionality
				3.3.1.3 Browsing Drugs Using DrugBank Online’s Drug Categories
			3.3.2 Identifying Chemicals and Relevant Sequences
				3.3.2.1 Searching Using Chemical Structure Search
				3.3.2.2 Using Sequence Search to Find Similar Targets
			3.3.3 Extracting DrugBank Datasets for ML
		3.4 Research Using DrugBank
		3.5 Discussion and Conclusions
		References
	4. Bioisosteric Replacement for Drug Discovery Supported by the SwissBioisostere Database
		4.1 Introduction
			4.1.1 Concept of Isosterism and Bioisosterism
			4.1.2 Classical vs. Non-classical Bioisostere and Further Molecular Replacements
			4.1.3 Bioisosteric Replacement in Drug Discovery
		4.2 Construction and Dissemination of SwissBioisostere
			4.2.1 Intention and Requirements
			4.2.2 Bioactivity Data
			4.2.3 Nonsupervised Matched Molecular Pair Analysis
			4.2.4 Database
			4.2.5 Web Interface
		4.3 Content of SwissBioisostere
			4.3.1 Global Content
			4.3.2 Biological and Chemical Contexts
			4.3.3 Fragment Shape Diversity
		4.4 Usage of SwissBioisostere
			4.4.1 Website Usage
			4.4.2 Most Frequent Requests
			4.4.3 Examples Related to Drug Discovery
				4.4.3.1 Use Cases
				4.4.3.2 Replacing Unwanted Chemical Groups
				4.4.3.3 Optimization of Passive Absorption and Blood–Brain Barrier Diffusion
				4.4.3.4 Reduction of Flexibility
				4.4.3.5 Reduction of Aromaticity/Escape from Flatland
		4.5 Conclusive Remarks
		Acknowledgment
		References
Part II. Macromolecular Targets and Diseases
	5. The Protein Data Bank (PDB) and Macromolecular Structure Data Supporting Computer-Aided Drug Design
		5.1 Introduction
		5.2 Small Molecule Data in Protein Data Bank (PDB) Entries
			5.2.1 What Data are in the PDB Archive?
			5.2.2 Definition of Small Molecules in OneDep
		5.3 Small Molecule Dictionaries
			5.3.1 wwPDB Chemical Component Dictionary (CCD)
			5.3.2 The Peptide Reference Dictionary
		5.4 Additional Ligand Annotations in the PDB Archive
			5.4.1 Linkage Information
			5.4.2 Carbohydrates
		5.5 Validation of Ligands in the Worldwide Protein Data Bank (wwPDB)
			5.5.1 Various Criteria and Software Used for Validating Ligand in Validation Reports
			5.5.2 Identification of Ligand of Interest (LOI)
			5.5.3 Geometric and Conformational Validation
			5.5.4 Ligand Fit to Experimental Electron Density Validation
			5.5.5 Accessing wwPDB Validation Reports from PDBe Entry Pages
			5.5.6 Other Planned Improvements to Enhance Ligand Validation
		5.6 PDBe Tools for Ligand Analysis
			5.6.1 Ligand Interactions
				5.6.1.1 Classifying Ligand Interactions
				5.6.1.2 Data Availability
			5.6.2 Ligand Environment Component
			5.6.3 Chemistry Process and FTP
			5.6.4 PDBeChem Pages
		5.7 Ligand-Related Annotations in the PDBe-KB
			5.7.1 Introduction to PDBe-KB
			5.7.2 Data Access Mechanisms for Ligand-Related Annotations
			5.7.3 Ligand-Related Annotations on the Aggregated Views of Proteins
		5.8 Case Study: Using PDB Data to Support Drug Discovery
		5.9 Conclusions and Outlook
			5.9.1 Upcoming Features and Improvements
		References
	6. The SWISS-MODEL Repository of 3D Protein Structures and Models
		6.1 Introduction
		6.2 SMR Database Content and Model Providers
			6.2.1 PDB
			6.2.2 SWISS-MODEL
			6.2.3 AlphaFold Database
			6.2.4 ModelArchive
		6.3 Protein Feature Annotation and Cross-References to Computational Resources
			6.3.1 Structural Features, Ligands, and Oligomers
			6.3.2 SWISS-MODEL associated tools
			6.3.3 Web and API Access
		6.4 Quality Estimates and Benchmarking
		6.5 Binding Site Conformational States
		6.6 SMR and Computer-Aided Structure-based Drug Design
		6.7 Conclusion and Outlook
		References
	7. PDB-REDO in Computational-Aided Drug Design (CADD)
		7.1 History and Concepts
			7.1.1 X-ray Structure Models
			7.1.2 PDB-REDO Development
				7.1.2.1 First Uniformity
				7.1.2.2 Automatic Rebuilding of Protein Backbone and Side Chains
				7.1.2.3 Automated Model Completion Approaches
				7.1.2.4 Systematic Integration of Structural Knowledge
				7.1.2.5 Overview of PDB-REDO Pipeline
		7.2 Structure Improvements by PDB-REDO
			7.2.1 Parametrization and Rebuilding Effects on Small Molecule Ligands
				7.2.2.1 Loop Building Completes a Binding Site Region
				7.2.2.2 Loop Building Results in Improved Binding Sites
				7.2.2.3 Building new Compounds into Density
			7.2.2 Building of Protein Loops and Ligands into Protein Structure Mo
			7.2.3 Nucleic Acid Improvements by PDB-REDO
			7.2.4 Glycoprotein Structure Model Rebuilding
			7.2.5 Metal Binding Sites
			7.2.6 Limitations of the PDB-REDO Databank
		7.3 Access the PDB-REDO Databank and Metadata
			7.3.1 Downloading and Inspecting Individual PDB-REDO Entries
			7.3.2 Data Available in PDB-REDO Entries
			7.3.3 Usage of the Uniform and FAIR Validation Data
			7.3.4 Creating Datasets from the PDB-REDO Databank
			7.3.5 Submitting Structure Models to the PDB-REDO Pipeline
		7.4 Conclusions
		Acknowledgments and Funding
		List of Abbreviations and Symbols
	8. Pharos and TCRD: Informatics Tools for Illuminating Dark Targets
		8.1 Introduction
		8.2 Methods
			8.2.1 Data Organization
				8.2.1.1 Target Alignment
				8.2.1.2 Disease Alignment
				8.2.1.3 Ligand Alignment
				8.2.1.4 Data and UI Updates
			8.2.2 Programmatic Access and Data Download
			8.2.3 UI Organization
				8.2.3.1 List Pages
				8.2.3.2 Details Pages
				8.2.3.3 Search
				8.2.3.4 Tutorials
			8.2.4 Analysis Methods Within Pharos
				8.2.4.1 Searching for Ligands
				8.2.4.2 Finding Targets by Amino Acid Sequence
				8.2.4.3 Finding Targets with Similar Annotations
				8.2.4.4 Finding Targets with Predicted Activity
				8.2.4.5 Enrichment Scores for Filter Values
		8.3 Use Cases
			8.3.1 Hypothesizing the Role of a Dark Target
				8.3.1.1 Primary Documentation
				8.3.1.2 List Analysis
				8.3.1.3 Downloading Data
				8.3.1.4 Variations on this Use Case
			8.3.2 Characterizing a Novel Chemical Compound
				8.3.2.1 Finding Predicted Targets
				8.3.2.2 Analyzing Similar Ligands
				8.3.2.3 Ligand Details Pages
				8.3.2.4 Variations on this Use Case
			8.3.3 Investigating Diseases
		8.4 Discussion
		Funding
		References
Part III. Users’ Points of View
	9. Mining for Bioactive Molecules in Open Databases
		9.1 Introduction
		9.2 Main Tools for Virtual Screening
			9.2.1 ADMET and PAINS Filtering
			9.2.2 Protein–Ligand Docking
			9.2.3 Pharmacophore Search
			9.2.4 Shape/Electrostatic Similarity
			9.2.5 Protein-Structure Databases
			9.2.6 The Protein Data Bank
			9.2.7 The PDB-REDO Databank
			9.2.8 The SWISS-MODEL Repository
			9.2.9 The AlphaFold Protein Structure Database
		9.3 Validating Binding Site and Ligand Coordinates in Three-Dimensional Protein Complexes
		9.4 Databases for Searching New Drugs
			9.4.1 COCONUT
			9.4.2 GDBs
			9.4.3 ZINC20
		9.5 Databases of Bioactive Molecules
			9.5.1 The BindingDB Database
			9.5.2 PubChem
			9.5.3 ChEMBL
		9.6 Databases of Inactive/Decoy Molecules
			9.6.1 Collecting Experimentally Inactive Compounds from PubChem
			9.6.2 Collecting Presumed Inactive Compounds from Decoy Databases
			9.6.3 Building Custom-Based Decoy Sets
		9.7 Main Metrics for Evaluating the Success of a Virtual Screening
		9.8 Concluding Remarks
		References
	10. Open Access Databases – An Industrial View
		10.1 Academic vs. Industrial Research
		10.2 Scaffold-Hopping
		10.3 Virtual-Screening
		Abbreviations
		References
Index