Open Access Databases and Datasets for Drug Discovery / / edited by Antoine Daina, Michael Przewosny, and Vincent Zoete

• Pubblicazione: Weinheim, Germany : , : Wiley-VCH, , [2024]; ©2024
• Edizione: First edition.
• Descrizione fisica: 1 online resource (348 pages)
• Disciplina: 615.19
• Soggetto topico: Drugs - Design - Data processing; Drug development - Data processing
• Persona (resp. second.): DainaAntoine; PrzewosnyMichael; ZoeteVincent
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover -- Title Page -- Copyright -- Contents -- Series Editors Preface -- Raimund Mannhold - A Personal Obituary from the Series Editors -- A Personal Foreword -- Chapter 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 -- Chapter 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 -- Chapter 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 General Workflows -- 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. ; Chapter 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 -- Chapter 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 -- Chapter 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 -- Chapter 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 -- 7.2.1.1 Re‐refinement Improves Ligand Conformation -- 7.2.1.2 Side Chain Rebuilding Improves Ligand Binding Sites -- 7.2.1.3 Histidine Flip and Improved Ligand Parameterization -- 7.2.2 Building of Protein Loops and Ligands into Protein Structure Models -- 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 -- 7.2.3 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 -- References -- Chapter 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 -- Chapter 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 -- Chapter 10 Open Access Databases - An Industrial View -- 10.1 Academic vs. Industrial Research -- 10.2 Scaffold‐Hopping -- 10.3 Virtual‐Screening -- References -- Index -- EULA.
• Titolo autorizzato: Open Access Databases and Datasets for Drug Discovery
• ISBN: 3-527-83049-9; 3-527-83047-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910831048503321
• Lo trovi qui: Univ. Federico II
• Serie: Methods and principles in medicinal chemistry.