Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Advanced computer-assisted techniques in drug discovery [[electronic resource] /] / edited by Han van de Waterbeemd
| Advanced computer-assisted techniques in drug discovery [[electronic resource] /] / edited by Han van de Waterbeemd |
| Pubbl/distr/stampa | Weinheim ; ; New York, : VCH, c1995 |
| Descrizione fisica | 1 online resource (367 p.) |
| Disciplina |
615.10285
615.1900285 |
| Altri autori (Persone) | WaterbeemdHan van de |
| Collana | Methods and principles in medicinal chemistry |
| Soggetto topico |
Pharmaceutical chemistry - Data processing
Drugs - Design - Data processing Drugs - Research - Data processing QSAR (Biochemistry) |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-84288-5
9786611842888 3-527-61567-9 3-527-61566-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Advanced Computer- Assisted Techniques in Drug Discovery; Preface; A Personal Foreword; Contents; 1 Introduction; 1.1 3D QSAR; 1.2 Databases; 1.3 Progress in Multivariate Data Analysis; 1.4 Scope of this Book; References; 2 3D QSAR: The Integration of QSAR with Molecular Modeling; 2.1 Chemometrics and Molecular Modeling; 2.1.1 Introduction; 2.1.2 QSAR Methodology using Molecular Modeling and Chemometrics; 2.1.2.1 Search for the Geometric Pharmacophore; 2.1.2.2 Quantitative Correlation between Molecular Properties and Activity; 2.1.2.3 Computer Programs; 2.1.3 Illustrative Examples
2.1.3.1 Amnesia-Reversal Compounds2.1.3.2 Non-Peptide Angiotensin II Receptor Antagonists; 2.1.3.3 HMG-CoA Reductase Inhibitors; 2.1.3.4 Antagonists at the 5-HT3 Receptor; 2.1.3.5 Polychlorinated Dibenzo-p-dioxins; 2.1.4 Conclusions; References; 2.2 3D QSAR Methods; 2.2.1 Introduction; 2.2.2 3D QSAR of a Series of Calcium Channel Agonists; 2.2.2.1 Molecular Alignment; 2.2.2.2 Charges; 2.2.2.3 Generating 3D Fields; 2.2.2.4 Compilation of GRID Maps; 2.2.2.5 Inclusion of Macroscopic Descriptors with 3D Field Data; 2.2.3 Statistical Analysis; 2.2.3.1 Results of the Analysis 2.2.3.2 Testing the Model2.2.4 Conclusions; References; 2.3 GOLPE Philosophy and Applications in 3D QSAR; 2.3.1 Introduction; 2.3.1.1 3D Molecular Descriptors and Chemometric Tools; 2.3.1.2 Unfolding Three-way Matrices; 2.3.2 The GOLPE Philosophy; 2.3.2.1 Variable Selection; 2.3.3 Applications; 2.3.3.1 PCA on the Target Matrix; 2.3.3.2 PCA on the Probe Matrix; 2.3.3.3 PLS Analysis on the Target Matrix; 2.3.3.4 PLS on Target Matrix as a Strategy to Ascertain the Active Conformation; 2.3.3.5 GOLPE with Different 3D Descriptors; 2.3.4 Conclusions and Perspectives; References 3 Rational Use of Chemical and Sequence Databases3.1 Molecular Similarity Analysis: Applications in Drug Discovery; 3.1.1 Introduction; 3.1.2 Similarity-Based Compound Selection; 3.1.2.1 Similarity Measures and Neighborhoods; 3.1.2.2 Application of 2D and 3D Similarity Measures; 3.1.2.3 Application of Dissimilarity-Based Compound Selection for Broad Screening; 3.1.3 Structure-Activity Maps (SAMs); 3.1.3.1 A Visual Analogy; 3.1.3.2 Representing Inter-Structure Distances; 3.1.3.3 Structure Maps; 3.1.3.4 Coloring a Structure Map; 3.1.4 Field-Based Similarity Methods 3.1.4.1 Field-Based Similarity Measures3.1.4.2 Field-Based Molecular Superpositions; 3.1.4.3 An Example of Field-Based Fitting: Morphine and Clonidine; 3.1.5 Conclusions; References; 3.2 Clustering of Chemical Structure Databases for Compound Selection; 3.2.1 Introduction; 3.2.2 Review of Clustering Methods; 3.2.2.1 Hierarchical Clustering Methods; 3.2.2.2 Non-Hierarchical Clustering Methods; 3.2.3 Choice of Clustering Method; 3.2.3.1 Computational Requirements; 3.2.3.2 Cluster Shapes; 3.2.3.3 Comparative Studies 3.2.4 Examples of the Selection of Compounds from Databases by Clustering Techniques |
| Record Nr. | UNINA-9910144110203321 |
| Weinheim ; ; New York, : VCH, c1995 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advanced computer-assisted techniques in drug discovery [[electronic resource] /] / edited by Han van de Waterbeemd
| Advanced computer-assisted techniques in drug discovery [[electronic resource] /] / edited by Han van de Waterbeemd |
| Pubbl/distr/stampa | Weinheim ; ; New York, : VCH, c1995 |
| Descrizione fisica | 1 online resource (367 p.) |
| Disciplina |
615.10285
615.1900285 |
| Altri autori (Persone) | WaterbeemdHan van de |
| Collana | Methods and principles in medicinal chemistry |
| Soggetto topico |
Pharmaceutical chemistry - Data processing
Drugs - Design - Data processing Drugs - Research - Data processing QSAR (Biochemistry) |
| ISBN |
1-281-84288-5
9786611842888 3-527-61567-9 3-527-61566-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Advanced Computer- Assisted Techniques in Drug Discovery; Preface; A Personal Foreword; Contents; 1 Introduction; 1.1 3D QSAR; 1.2 Databases; 1.3 Progress in Multivariate Data Analysis; 1.4 Scope of this Book; References; 2 3D QSAR: The Integration of QSAR with Molecular Modeling; 2.1 Chemometrics and Molecular Modeling; 2.1.1 Introduction; 2.1.2 QSAR Methodology using Molecular Modeling and Chemometrics; 2.1.2.1 Search for the Geometric Pharmacophore; 2.1.2.2 Quantitative Correlation between Molecular Properties and Activity; 2.1.2.3 Computer Programs; 2.1.3 Illustrative Examples
2.1.3.1 Amnesia-Reversal Compounds2.1.3.2 Non-Peptide Angiotensin II Receptor Antagonists; 2.1.3.3 HMG-CoA Reductase Inhibitors; 2.1.3.4 Antagonists at the 5-HT3 Receptor; 2.1.3.5 Polychlorinated Dibenzo-p-dioxins; 2.1.4 Conclusions; References; 2.2 3D QSAR Methods; 2.2.1 Introduction; 2.2.2 3D QSAR of a Series of Calcium Channel Agonists; 2.2.2.1 Molecular Alignment; 2.2.2.2 Charges; 2.2.2.3 Generating 3D Fields; 2.2.2.4 Compilation of GRID Maps; 2.2.2.5 Inclusion of Macroscopic Descriptors with 3D Field Data; 2.2.3 Statistical Analysis; 2.2.3.1 Results of the Analysis 2.2.3.2 Testing the Model2.2.4 Conclusions; References; 2.3 GOLPE Philosophy and Applications in 3D QSAR; 2.3.1 Introduction; 2.3.1.1 3D Molecular Descriptors and Chemometric Tools; 2.3.1.2 Unfolding Three-way Matrices; 2.3.2 The GOLPE Philosophy; 2.3.2.1 Variable Selection; 2.3.3 Applications; 2.3.3.1 PCA on the Target Matrix; 2.3.3.2 PCA on the Probe Matrix; 2.3.3.3 PLS Analysis on the Target Matrix; 2.3.3.4 PLS on Target Matrix as a Strategy to Ascertain the Active Conformation; 2.3.3.5 GOLPE with Different 3D Descriptors; 2.3.4 Conclusions and Perspectives; References 3 Rational Use of Chemical and Sequence Databases3.1 Molecular Similarity Analysis: Applications in Drug Discovery; 3.1.1 Introduction; 3.1.2 Similarity-Based Compound Selection; 3.1.2.1 Similarity Measures and Neighborhoods; 3.1.2.2 Application of 2D and 3D Similarity Measures; 3.1.2.3 Application of Dissimilarity-Based Compound Selection for Broad Screening; 3.1.3 Structure-Activity Maps (SAMs); 3.1.3.1 A Visual Analogy; 3.1.3.2 Representing Inter-Structure Distances; 3.1.3.3 Structure Maps; 3.1.3.4 Coloring a Structure Map; 3.1.4 Field-Based Similarity Methods 3.1.4.1 Field-Based Similarity Measures3.1.4.2 Field-Based Molecular Superpositions; 3.1.4.3 An Example of Field-Based Fitting: Morphine and Clonidine; 3.1.5 Conclusions; References; 3.2 Clustering of Chemical Structure Databases for Compound Selection; 3.2.1 Introduction; 3.2.2 Review of Clustering Methods; 3.2.2.1 Hierarchical Clustering Methods; 3.2.2.2 Non-Hierarchical Clustering Methods; 3.2.3 Choice of Clustering Method; 3.2.3.1 Computational Requirements; 3.2.3.2 Cluster Shapes; 3.2.3.3 Comparative Studies 3.2.4 Examples of the Selection of Compounds from Databases by Clustering Techniques |
| Record Nr. | UNISA-996217063403316 |
| Weinheim ; ; New York, : VCH, c1995 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advanced computer-assisted techniques in drug discovery [[electronic resource] /] / edited by Han van de Waterbeemd
| Advanced computer-assisted techniques in drug discovery [[electronic resource] /] / edited by Han van de Waterbeemd |
| Pubbl/distr/stampa | Weinheim ; ; New York, : VCH, c1995 |
| Descrizione fisica | 1 online resource (367 p.) |
| Disciplina |
615.10285
615.1900285 |
| Altri autori (Persone) | WaterbeemdHan van de |
| Collana | Methods and principles in medicinal chemistry |
| Soggetto topico |
Pharmaceutical chemistry - Data processing
Drugs - Design - Data processing Drugs - Research - Data processing QSAR (Biochemistry) |
| ISBN |
1-281-84288-5
9786611842888 3-527-61567-9 3-527-61566-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Advanced Computer- Assisted Techniques in Drug Discovery; Preface; A Personal Foreword; Contents; 1 Introduction; 1.1 3D QSAR; 1.2 Databases; 1.3 Progress in Multivariate Data Analysis; 1.4 Scope of this Book; References; 2 3D QSAR: The Integration of QSAR with Molecular Modeling; 2.1 Chemometrics and Molecular Modeling; 2.1.1 Introduction; 2.1.2 QSAR Methodology using Molecular Modeling and Chemometrics; 2.1.2.1 Search for the Geometric Pharmacophore; 2.1.2.2 Quantitative Correlation between Molecular Properties and Activity; 2.1.2.3 Computer Programs; 2.1.3 Illustrative Examples
2.1.3.1 Amnesia-Reversal Compounds2.1.3.2 Non-Peptide Angiotensin II Receptor Antagonists; 2.1.3.3 HMG-CoA Reductase Inhibitors; 2.1.3.4 Antagonists at the 5-HT3 Receptor; 2.1.3.5 Polychlorinated Dibenzo-p-dioxins; 2.1.4 Conclusions; References; 2.2 3D QSAR Methods; 2.2.1 Introduction; 2.2.2 3D QSAR of a Series of Calcium Channel Agonists; 2.2.2.1 Molecular Alignment; 2.2.2.2 Charges; 2.2.2.3 Generating 3D Fields; 2.2.2.4 Compilation of GRID Maps; 2.2.2.5 Inclusion of Macroscopic Descriptors with 3D Field Data; 2.2.3 Statistical Analysis; 2.2.3.1 Results of the Analysis 2.2.3.2 Testing the Model2.2.4 Conclusions; References; 2.3 GOLPE Philosophy and Applications in 3D QSAR; 2.3.1 Introduction; 2.3.1.1 3D Molecular Descriptors and Chemometric Tools; 2.3.1.2 Unfolding Three-way Matrices; 2.3.2 The GOLPE Philosophy; 2.3.2.1 Variable Selection; 2.3.3 Applications; 2.3.3.1 PCA on the Target Matrix; 2.3.3.2 PCA on the Probe Matrix; 2.3.3.3 PLS Analysis on the Target Matrix; 2.3.3.4 PLS on Target Matrix as a Strategy to Ascertain the Active Conformation; 2.3.3.5 GOLPE with Different 3D Descriptors; 2.3.4 Conclusions and Perspectives; References 3 Rational Use of Chemical and Sequence Databases3.1 Molecular Similarity Analysis: Applications in Drug Discovery; 3.1.1 Introduction; 3.1.2 Similarity-Based Compound Selection; 3.1.2.1 Similarity Measures and Neighborhoods; 3.1.2.2 Application of 2D and 3D Similarity Measures; 3.1.2.3 Application of Dissimilarity-Based Compound Selection for Broad Screening; 3.1.3 Structure-Activity Maps (SAMs); 3.1.3.1 A Visual Analogy; 3.1.3.2 Representing Inter-Structure Distances; 3.1.3.3 Structure Maps; 3.1.3.4 Coloring a Structure Map; 3.1.4 Field-Based Similarity Methods 3.1.4.1 Field-Based Similarity Measures3.1.4.2 Field-Based Molecular Superpositions; 3.1.4.3 An Example of Field-Based Fitting: Morphine and Clonidine; 3.1.5 Conclusions; References; 3.2 Clustering of Chemical Structure Databases for Compound Selection; 3.2.1 Introduction; 3.2.2 Review of Clustering Methods; 3.2.2.1 Hierarchical Clustering Methods; 3.2.2.2 Non-Hierarchical Clustering Methods; 3.2.3 Choice of Clustering Method; 3.2.3.1 Computational Requirements; 3.2.3.2 Cluster Shapes; 3.2.3.3 Comparative Studies 3.2.4 Examples of the Selection of Compounds from Databases by Clustering Techniques |
| Record Nr. | UNINA-9910830440503321 |
| Weinheim ; ; New York, : VCH, c1995 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Biosilico
| Biosilico |
| Pubbl/distr/stampa | London, : Elsevier Science, ©2003 |
| Descrizione fisica | 1 online resource |
| Collana | Drug discovery today publications |
| Soggetto topico |
Drugs - Design
Drugs - Design - Data processing Biochemistry - Data processing Drugs - Computer simulation Drug development - Data processing Drug Design Medical Informatics |
| Soggetto genere / forma |
Periodicals
Periodicals. |
| ISSN | 1478-5282 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996198118503316 |
| London, : Elsevier Science, ©2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Biosilico
| Biosilico |
| Pubbl/distr/stampa | London, : Elsevier Science, ©2003 |
| Descrizione fisica | 1 online resource |
| Collana | Drug discovery today publications |
| Soggetto topico |
Drugs - Design
Drugs - Design - Data processing Biochemistry - Data processing Drugs - Computer simulation Drug development - Data processing Drug Design Medical Informatics |
| Soggetto genere / forma |
Periodicals
Periodicals. |
| ISSN | 1478-5282 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910143062803321 |
| London, : Elsevier Science, ©2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computational drug design : a guide for computational and medicinal chemists / / David C. Young
| Computational drug design : a guide for computational and medicinal chemists / / David C. Young |
| Autore | Young David C. <1964-> |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Hoboken, N.J. : , : John Wiley & Sons, , 2009 |
| Descrizione fisica | 1 online resource (xxxvi, 307 pages) : illustrations |
| Disciplina | 615/.190285 |
| Soggetto topico |
Drugs - Design - Mathematical models
Drugs - Design - Data processing |
| ISBN |
1-282-26783-3
9786612267833 0-470-45185-8 0-470-45184-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
COMPUTATIONAL DRUG DESIGN; CONTENTS; PREFACE; ACKNOWLEDGMENTS; ABOUT THE AUTHOR; SYMBOLS USED IN THIS BOOK; BOOK ABSTRACT; 1 Introduction; 1.1 A Difficult Problem; 1.2 An Expensive Problem; 1.3 Where Computational Techniques are Used; Bibliography; PART I THE DRUG DESIGN PROCESS; 2 Properties that Make a Molecule a Good Drug; 2.1 Compound Testing; 2.1.1 Biochemical Assays; 2.1.2 Cell-Based Assays; 2.1.3 Animal Testing; 2.1.4 Human Clinical Trials; 2.2 Molecular Structure; 2.2.1 Activity; 2.2.2 Bioavailability and Toxicity; 2.2.3 Drug Side Effects; 2.2.4 Multiple Drug Interactions
2.3 Metrics for Drug-Likeness; 2.4 Exceptions to the Rules; Bibliography; 3 Target Identification; 3.1 Primary Sequence and Metabolic Pathway; 3.2 Crystallography; 3.3 2D NMR; 3.4 Homology Models; 3.5 Protein Folding; Bibliography; 4 Target Characterization; 4.1 Analysis of Target Mechanism; 4.1.1 Kinetics and Crystallography; 4.1.2 Automated Crevice Detection; 4.1.3 Transition Structures and Reaction Coordinates; 4.1.4 Molecular Dynamics Simulations; 4.2 Where the Target is Expressed; 4.3 Pharmacophore Identification; 4.4 Choosing an Inhibitor Mechanism; Bibliography 5 The Drug Design Process for a Known Protein Target; 5.1 The Structure-Based Design Process; 5.2 Initial Hits; 5.3 Compound Refinement; 5.4 ADMET; 5.5 Drug Resistance; Bibliography; 6 The Drug Design Process for an Unknown Target; 6.1 The Ligand-Based Design Process; 6.2 Initial Hits; 6.3 Compound Refinement; 6.4 ADMET; Bibliography; 7 Drug Design for Other Targets; 7.1 DNA Binding; 7.2 RNA as a Target; 7.3 Allosteric Sites; 7.4 Receptor Targets; 7.5 Steroids; 7.6 Targets inside Cells; 7.7 Targets within the Central Nervous System; 7.8 Irreversibly Binding Inhibitors 7.9 Upregulating Target Activity; Bibliography; 8 Compound Library Design; 8.1 Targeted Libraries versus Diverse Libraries; 8.2 From Fragments versus from Reactions; 8.3 Non-Enumerative Techniques; 8.4 Drug-Likeness and Synthetic Accessibility; 8.5 Analyzing Chemical Diversity and Spanning known Chemistries; 8.6 Compound Selection Techniques; Bibliography; PART II COMPUTATIONAL TOOLS AND TECHNIQUES; 9 Homology Model Building; 9.1 How much Similarity is Enough?; 9.2 Steps for Building a Homology Model; 9.2.1 Step 1: Template Identification 9.2.2 Step 2: Alignment between the Unknown and the Template; 9.2.3 Step 3: Manual Adjustments to the Alignment; 9.2.4 Step 4: Replace Template Side Chains with Model Side Chains; 9.2.5 Step 5: Adjust Model for Insertions and Deletions; 9.2.6 Step 6: Optimization of the Model; 9.2.7 Step 7: Model Validation; 9.2.8 Step 8: If Errors are Found, Iterate Back to Previous Steps; 9.3 Reliability of Results; Bibliography; 10 Molecular Mechanics; 10.1 A Really Brief Introduction to Molecular Mechanics; 10.2 Force Fields for Drug Design; Bibliography; 11 Protein Folding; 11.1 The Difficulty of the Problem |
| Record Nr. | UNINA-9910145814603321 |
Young David C. <1964->
|
||
| Hoboken, N.J. : , : John Wiley & Sons, , 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Drug discovery today Biosilico
| Drug discovery today Biosilico |
| Pubbl/distr/stampa | [Amsterdam] ; ; [New York], : Elsevier Science, 2004 |
| Descrizione fisica | 1 online resource |
| Soggetto topico |
Drugs - Design
Drugs - Design - Data processing Biochemistry - Data processing Drugs - Computer simulation Drug Design Medical Informatics Simulation par ordinateur Développement de médicaments Informatique médicale |
| Soggetto genere / forma |
Periodical
Periodicals. Périodique électronique (Descripteur de forme) Ressource Internet (Descripteur de forme) |
| Soggetto non controllato | Pharmacy, Therapeutics, & Pharmacology |
| ISSN | 1878-0997 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
Biosilico
DDT. |
| Record Nr. | UNINA-9910143062703321 |
| [Amsterdam] ; ; [New York], : Elsevier Science, 2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Drug discovery today Biosilico
| Drug discovery today Biosilico |
| Pubbl/distr/stampa | [Amsterdam] ; ; [New York], : Elsevier Science, 2004 |
| Descrizione fisica | 1 online resource |
| Soggetto topico |
Drugs - Design
Drugs - Design - Data processing Biochemistry - Data processing Drugs - Computer simulation Drug Design Medical Informatics Simulation par ordinateur Développement de médicaments Informatique médicale |
| Soggetto genere / forma |
Periodical
Periodicals. Périodique électronique (Descripteur de forme) Ressource Internet (Descripteur de forme) |
| Soggetto non controllato | Pharmacy, Therapeutics, & Pharmacology |
| ISSN | 1878-0997 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
Biosilico
DDT. |
| Record Nr. | UNISA-996198117903316 |
| [Amsterdam] ; ; [New York], : Elsevier Science, 2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Open Access Databases and Datasets for Drug Discovery / / edited by Antoine Daina, Michael Przewosny, and Vincent Zoete
| Open Access Databases and Datasets for Drug Discovery / / edited by Antoine Daina, Michael Przewosny, and Vincent Zoete |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Weinheim, Germany : , : Wiley-VCH, , [2024] |
| Descrizione fisica | 1 online resource (348 pages) |
| Disciplina | 615.19 |
| Collana | Methods and Principles in Medicinal Chemistry Series |
| Soggetto topico |
Drugs - Design - Data processing
Drug development - Data processing |
| ISBN |
3-527-83049-9
3-527-83047-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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. |
| Record Nr. | UNINA-9910831048503321 |
| Weinheim, Germany : , : Wiley-VCH, , [2024] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
