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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 Médicaments - Conception Médicaments - Conception - Informatique Biochimie - Informatique Médicaments - Simulation par ordinateur Médicaments - Développement - Informatique Médecine - Informatique |
| Soggetto genere / forma |
Periodical
periodicals. Periodicals. Périodiques. |
| 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 | ||
| ||
Chemoinformatics for drug discovery / / edited by Jürgen Bajorath
| Chemoinformatics for drug discovery / / edited by Jürgen Bajorath |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2014] |
| Descrizione fisica | 1 online resource (415 p.) |
| Disciplina | 615.1/9 |
| Altri autori (Persone) | BajorathJürgen |
| Soggetto topico |
Cheminformatics
Drug development - Data processing Pharmacy informatics |
| ISBN |
1-118-74309-1
1-118-74278-8 1-118-74305-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910132246803321 |
| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Chemoinformatics for drug discovery / / edited by Jürgen Bajorath
| Chemoinformatics for drug discovery / / edited by Jürgen Bajorath |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2014] |
| Descrizione fisica | 1 online resource (415 p.) |
| Disciplina | 615.1/9 |
| Altri autori (Persone) | BajorathJürgen |
| Soggetto topico |
Cheminformatics
Drug development - Data processing Pharmacy informatics |
| ISBN |
9781118743096
1118743091 9781118742785 1118742788 9781118743058 1118743059 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Chemoinformatics for Drug Discovery -- Copyright -- Contents -- Preface -- Contributors -- 1 What Are Our Models Really Telling Us? A Practical Tutorial on Avoiding Common Mistakes When Building Predictive Models -- 1.1 Introduction -- 1.2 Preliminaries -- 1.3 Datasets -- 1.3.1 Exploring Datasets -- 1.4 Building Predictive Models -- 1.5 Evaluating the Performance of Predictive Models -- 1.5.1 Pearson's r -- 1.5.2 Kendall's Tau -- 1.5.3 Root-Mean-Square Deviation (RMSD) -- 1.6 Molecular Descriptors -- 1.7 Building and Testing a Random Forest Model -- 1.8 Experimental Error and Model Performance -- 1.9 Model Applicability -- 1.10 Comparing Predictive Models -- 1.11 Conclusion -- References -- Source Code Listings -- 2 The Challenge of Creativity in Drug Design -- 2.1 Drug Design History: Incrementalism and Serendipity -- 2.2 Physical Reality and Computational Methods -- 2.2.1 Protein Structure-Based Methods -- 2.2.2 Molecular Similarity -- 2.2.3 3D QSAR: Physically Realistic Activity Prediction -- 2.3 Summary -- References -- 3 A Rough Set Theory Approach to the Analysis of Gene Expression Profiles -- 3.1 Introduction -- 3.2 Methodology -- 3.2.1 Basic Theory -- 3.2.2 Measures of Classification Accuracy and Quality -- 3.2.3 An Illustrative Example -- 3.2.4 Essential and Superfluous Information -- 3.2.5 Rule Generation -- 3.3 Drug-Induced Gene Expression and Phospholipidosis in Human Hepatoma HepG2 Cells -- 3.3.1 Dataset -- 3.3.2 Determination of D-Reducts -- 3.3.3 Generation of Preliminary Rules -- 3.3.4 Rule Simplification-Reduction of Attribute Values -- 3.4 Discussion -- 3.5 Summary and Conclusions -- Notes -- References -- 4 Bimodal Partial Least-Squares Approach and Its Application to Chemogenomics Studies for Molecular Design -- 4.1 Introduction -- 4.2 Material and Methods -- 4.2.1 Aminergic GPCR Inhibitory Activity Data.
4.2.2 Ligand and Protein Descriptors for LPLS Analysis -- 4.2.3 L-Shaped PLS -- 4.2.4 Atom Colorings Derived from Regression Coefficient Matrix -- 4.3 Results and Discussion -- 4.3.1 LPLS Analysis -- 4.3.2 Atom Colorings and Support by Molecular Modeling -- 4.4 Conclusion -- 4.5 Acknowledgments -- References -- 5 Stability in Molecular Fingerprint Comparison -- 5.1 Introduction -- 5.2 Methods -- 5.2.1 2D Methods -- 5.2.2 Generation of Molecular Isosteres: WABE -- 5.2.3 Tanimoto and Significance -- 5.3 Results -- 5.4 Conclusions and Directions -- References -- 6 Critical Assessment of Virtual Screening for Hit Identification -- 6.1 Introduction -- 6.2 Factors Affecting the Outcome and Evaluation of Virtual Screening Campaigns -- 6.2.1 General Scientific Factors -- 6.2.2 Characteristics of Practical Applications -- 6.3 How to Evaluate Virtual Screening Performance? -- 6.4 Virtual Versus High-Throughput Screening -- 6.4.1 Do We Need Virtual Screening? -- 6.4.2 Underutilized Strengths -- 6.5 Structural Novelty Revisited: Exemplary Cases -- 6.6 Expectations and Selected Applications -- 6.6.1 Inhibitors of Multifunctional Proteins: Cytohesins -- 6.6.2 First-in-Class Inhibitor for Ecto-5′-Nucleotidase -- 6.7 Conclusions: What Is Possible? What Is Not? -- References -- 7 Chemometric Applications of Naïve Bayesian Models in Drug Discovery: Beyond Compound Ranking -- 7.1 Introduction -- 7.1.1 Naïve Bayesian Models -- 7.2 Virtual Screening Using Bayesian Models -- 7.2.1 Reverse Virtual Screening: Target Fishing -- 7.2.2 Comparison to Other Molecular Representations and Machine Learning Techniques -- 7.3 Data Types and Data Quality Requirements -- 7.3.1 Compound Structure -- 7.3.2 Biological Activity -- 7.3.3 Binning of Potency and Guidelines for Multiclass Bayesian Models -- 7.4 Target and Phenotype Comparison in Chemical and Biological Activity Space. 7.4.1 Comparison of Compound Classes Using Bayesian Weights -- 7.5 Mining for Enriched Features and Interpreting Them -- 7.5.1 Understanding Chemist's Chemical Preferences -- 7.6 Shortcomings of NBM -- 7.7 Summary -- Acknowledgments -- References -- 8 Chemoinformatics in Lead Optimization -- 8.1 Historical Introduction -- 8.2 Lead Optimization Is a Large, Complex, Multiobjective Process -- 8.3 Chemoinformatics Methods for Multiobjective Optimization -- 8.3.1 Rules of Thumb -- 8.3.2 Filters -- 8.3.3 Desirability Functions -- 8.3.4 Probabilistic Scoring -- 8.3.5 Finding the "Best" Balance of Properties -- 8.4 Case Studies -- 8.4.1 Retrospective Analyses -- 8.4.2 MPO-Guided Hit to Candidate -- 8.5 Conclusion -- References -- 9 Using Chemoinformatics Tools to Analyze Chemical Arrays in Lead Optimization -- 9.1 Introduction -- 9.2 Lead Optimization Projects -- 9.3 Coverage of Chemistry and Property S pace ( DeltaS- DeltaA Plots) -- 9.4 Temporal Analysis of Lead Optimization -- 9.5 Modeling Lead Optimization as a Self-Avoiding Random Walk -- 9.6 Insights from the Data Analysis -- 9.7 Extracting Information on Arrays from the Archive -- 9.7.1 Annotating by Arrays -- 9.7.2 Automatic Chemotype Detector -- 9.7.3 Detecting Seed Compounds -- 9.8 Conclusions -- Acknowledgments -- References -- 10 Exploration of Structure-Activity Relationships (SARs) and Transfer of Key Elements in Lead Optimization -- 10.1 Introduction -- 10.2 Methods for SAR Analysis -- 10.2.1 Similarity Principle -- 10.2.2 Molecular Scaffolds -- 10.2.3 Privileged Substructures -- 10.2.4 Investigating the Outliers: Activity Cliffs -- 10.2.5 Quantification of Activity Cliffs -- 10.2.6 Matched Molecular Pairs -- 10.2.7 Exploration of Activity Cliffs for SAR Analysis -- 10.2.8 Visualization to Support SAR Analysis -- 10.2.9 Solutions in Pharmaceutical Industry -- 10.3 SAR Transfer in Rescaffolding. 10.3.1 Concepts of Rescaffolding -- 10.3.2 2D-Based Approaches Beyond 2D-Fingerprints -- 10.3.3 3D-Ligand-Based Approaches -- 10.3.4 3D-Protein-Based Approaches -- 10.4 Addressing Antitarget Activity -- 10.4.1 SAR Transfer in Lead Optimization -- 10.4.2 Identification and Application of Antitarget Activity Hotspots -- 10.4.3 Application Examples to Address hERG and CYP3A4 Inhibition -- 10.4.4 Integration in Lead Optimization Projects -- 10.5 Conclusion -- Acknowledgments -- References -- Chapter 11 Development and Applications of Global ADMET Models: In Silico Prediction of Human Microsomal Lability -- 11.1 Introduction -- 11.1.1 Structure-Based ADMET Models -- 11.1.2 Ligand-Based ADMET Models -- 11.2 Case Study on Metabolic Lability -- 11.2.1 Model Building -- 11.2.2 Dataset -- 11.2.3 Results -- 11.2.4 Application of a Global Model for Metabolic Lability in the Optimization of DGAT1 Inhibitors -- 11.3 Conclusion -- References -- Chapter 12 Chemoinformatics and Beyond: Moving from Simple Models to Complex Relationships in Pharmaceutical Computational Toxicology -- 12.1 Introduction -- 12.2 Data-Driven Modeling -- 12.2.1 Linking Chemical Structure to In Vitro Results -- 12.2.2 Chemical Structure and Using All Available Data -- 12.2.3 Combination of Evidence -- 12.2.4 Focusing on Biological Data -- 12.3 Delivering Impact: Bringing It to the Customer -- 12.3.1 Technical Solution -- 12.3.2 Facilitate Usage -- 12.4 Summary and Outlook -- Acknowledgments -- References -- Chapter 13 Applications of Cheminformatics in Pharmaceutical Research: Experiences at Boehringer Ingelheim in Germany -- 13.1 Introduction -- 13.2 Infrastructure and Systems -- 13.2.1 General Overview -- 13.2.2 Cheminformatics Database (CIDB) -- 13.2.3 Workflow Systems -- 13.2.4 BI Chemical Property Structure Planning System (BICEPS) -- 13.2.5 Project Data Marts. 13.2.6 Database of Virtual Combinatorial Libraries (BICLAIM-DB) -- 13.3 Methods and Applications -- 13.3.1 BIMESH : The HTS Data Analysis -- 13.3.2 BioProfile -- 13.3.3 SAR Analysis -- 13.3.4 Searches in BICLAIM -Space -- 13.3.5 Automatic Annotation of Controlled Substances -- 13.4 Discussion -- References -- Chapter 14 Lessons Learned from 30 Years of Developing Successful Integrated Cheminformatic Systems -- 14.1 Introduction -- 14.2 History -- 14.2.1 Cousin: 1981-2001 -- 14.2.2 ChemLink : 2001-2003 -- 14.2.3 RGate : 2003+ -- 14.2.4 Beacon Projects (~2000-2004) -- 14.2.5 Mobius: 2005 Till Present -- 14.3 Keys to the Success of Mobius: A Technical Perspective -- 14.3.1 Data Sources -- 14.3.2 Metaview -- 14.3.3 Query Engine -- 14.3.4 Ad Hoc Query Interface -- 14.3.5 Software Components -- 14.4 Lessons Learned: The Bottom Line -- 14.4.1 Quality Software -- 14.4.2 Data, Data, Data -- 14.4.3 Commitment to the User -- 14.4.4 Continuity -- 14.4.5 Importance of Developers That Understand the Science -- 14.4.6 Speed Kills -- 14.4.7 Bottom-Up Beats Top-Down -- 14.4.8 Use Off-the-Shelf Whenever Possible -- 14.4.9 Rollout Systems Using the Apostle Approach -- 14.4.10 Training -- 14.4.11 Support/Maintenance -- 14.4.12 Succession Planning -- 14.5 Build Versus Buy Versus Open Source -- 14.6 Conclusions and Summary -- References -- Chapter 15 Molecular Similarity Analysis -- 15.1 Introduction -- 15.2 A Brief History of Molecular Similarity Analysis -- 15.3 Cognitive Aspects of Similarity -- 15.4 Molecular Similarity Measures -- 15.4.1 Mathematical Description of Molecular Similarity -- 15.4.2 Representing Molecular and Chemical Information -- 15.4.3 Weighted Representations -- 15.4.4 Molecular Similarity Functions or Coefficients -- 15.5 Some Issues in Molecular Similarity Analysis -- 15.5.1 Asymmetric Similarity -- 15.5.2 2D and 3D Similarity Methods. 15.5.3 Data Fusion and Consensus Methods. |
| Record Nr. | UNINA-9910829021703321 |
| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 | ||
| ||
Pathway analysis for drug discovery [[electronic resource] ] : computational infrastructure and applications / / edited by Anton Yuryev
| Pathway analysis for drug discovery [[electronic resource] ] : computational infrastructure and applications / / edited by Anton Yuryev |
| Pubbl/distr/stampa | Hoboken, N.J., : John Wiley & Sons, c2008 |
| Descrizione fisica | 1 online resource (332 p.) |
| Disciplina |
615.19
615/.190285 |
| Altri autori (Persone) | YuryevAnton |
| Collana | Wiley series on technologies for the pharmaceutical industry |
| Soggetto topico |
Drug development - Data processing
DNA microarrays - Data processing Computational biology |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-83126-3
9786611831264 0-470-39927-9 0-470-39926-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
PATHWAY ANALYSIS FOR DRUG DISCOVERY; CONTENTS; Preface; Contributors; 1 Introduction to Pathway Analysis; 2 Software Infrastructure and Data Model for Pathway Analysis; 3 Automatic Pathway Inference in Heterogeneous Biological Association Networks; 4 Algorithmic Basis for Pathway Visualization; 5 Pathway Analysis of High-Throughput Experimental Data; 6 Integrative Pathway Analysis of Disease Molecular Data; 7 Whole-Genome Expression Profiling of Papillary Serous Ovarian Cancer: Activated Pathways, Potential Targets, and Noise; 8 Mammalian Proteome and Toxicant Network Analysis
9 Unraveling Mechanisms of Toxicity with the Power of Pathways: ToxWiz Tool as an Illustrative Example10 Impact of Chemistry Information on Pathway Analysis; 11 Propagation of Concentration Perturbations in Equilibrium Protein Binding Networks; 12 An Adaptive System Model of the Yeast Glucose Sensor System; 13 Present and Future of Pathway Analysis in Drug Discovery; Index |
| Record Nr. | UNINA-9910144448403321 |
| Hoboken, N.J., : John Wiley & Sons, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Pathway analysis for drug discovery [[electronic resource] ] : computational infrastructure and applications / / edited by Anton Yuryev
| Pathway analysis for drug discovery [[electronic resource] ] : computational infrastructure and applications / / edited by Anton Yuryev |
| Pubbl/distr/stampa | Hoboken, N.J., : John Wiley & Sons, c2008 |
| Descrizione fisica | 1 online resource (332 p.) |
| Disciplina |
615.19
615/.190285 |
| Altri autori (Persone) | YuryevAnton |
| Collana | Wiley series on technologies for the pharmaceutical industry |
| Soggetto topico |
Drug development - Data processing
DNA microarrays - Data processing Computational biology |
| ISBN |
1-281-83126-3
9786611831264 0-470-39927-9 0-470-39926-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
PATHWAY ANALYSIS FOR DRUG DISCOVERY; CONTENTS; Preface; Contributors; 1 Introduction to Pathway Analysis; 2 Software Infrastructure and Data Model for Pathway Analysis; 3 Automatic Pathway Inference in Heterogeneous Biological Association Networks; 4 Algorithmic Basis for Pathway Visualization; 5 Pathway Analysis of High-Throughput Experimental Data; 6 Integrative Pathway Analysis of Disease Molecular Data; 7 Whole-Genome Expression Profiling of Papillary Serous Ovarian Cancer: Activated Pathways, Potential Targets, and Noise; 8 Mammalian Proteome and Toxicant Network Analysis
9 Unraveling Mechanisms of Toxicity with the Power of Pathways: ToxWiz Tool as an Illustrative Example10 Impact of Chemistry Information on Pathway Analysis; 11 Propagation of Concentration Perturbations in Equilibrium Protein Binding Networks; 12 An Adaptive System Model of the Yeast Glucose Sensor System; 13 Present and Future of Pathway Analysis in Drug Discovery; Index |
| Record Nr. | UNINA-9910830835903321 |
| Hoboken, N.J., : John Wiley & Sons, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Pathway analysis for drug discovery : computational infrastructure and applications / / edited by Anton Yuryev
| Pathway analysis for drug discovery : computational infrastructure and applications / / edited by Anton Yuryev |
| Pubbl/distr/stampa | Hoboken, N.J., : John Wiley & Sons, c2008 |
| Descrizione fisica | 1 online resource (332 p.) |
| Disciplina | 615/.190285 |
| Altri autori (Persone) | YuryevAnton |
| Collana | Wiley series on technologies for the pharmaceutical industry |
| Soggetto topico |
Drug development - Data processing
DNA microarrays - Data processing Computational biology |
| ISBN |
9786611831264
9781281831262 1281831263 9780470399279 0470399279 9780470399262 0470399260 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
PATHWAY ANALYSIS FOR DRUG DISCOVERY; CONTENTS; Preface; Contributors; 1 Introduction to Pathway Analysis; 2 Software Infrastructure and Data Model for Pathway Analysis; 3 Automatic Pathway Inference in Heterogeneous Biological Association Networks; 4 Algorithmic Basis for Pathway Visualization; 5 Pathway Analysis of High-Throughput Experimental Data; 6 Integrative Pathway Analysis of Disease Molecular Data; 7 Whole-Genome Expression Profiling of Papillary Serous Ovarian Cancer: Activated Pathways, Potential Targets, and Noise; 8 Mammalian Proteome and Toxicant Network Analysis
9 Unraveling Mechanisms of Toxicity with the Power of Pathways: ToxWiz Tool as an Illustrative Example10 Impact of Chemistry Information on Pathway Analysis; 11 Propagation of Concentration Perturbations in Equilibrium Protein Binding Networks; 12 An Adaptive System Model of the Yeast Glucose Sensor System; 13 Present and Future of Pathway Analysis in Drug Discovery; Index |
| Record Nr. | UNINA-9911020276303321 |
| Hoboken, N.J., : John Wiley & Sons, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Structure-based design of drugs and other bioactive molecules : tools and strategies / / Arun K. Ghosh and Sandra Gemma
| Structure-based design of drugs and other bioactive molecules : tools and strategies / / Arun K. Ghosh and Sandra Gemma |
| Autore | Ghosh Arun K. |
| Pubbl/distr/stampa | Weinheim, Germany : , : Wiley-VCH, , 2014 |
| Descrizione fisica | 1 online resource (476 p.) |
| Disciplina | 615.190285 |
| Soggetto topico |
Drug development - Data processing
Bioactive compounds - Analysis Biopolymers |
| ISBN |
3-527-66523-4
3-527-66521-8 3-527-66524-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Structure-based Design of Drugs and Other Bioactive Molecules: Tools and Strategies; Contents; Preface; 1 From Traditional Medicine to Modern Drugs: Historical Perspective of Structure-Based Drug Design; 1.1 Introduction; 1.2 Drug Discovery During 1928-1980; 1.3 The Beginning of Structure-Based Drug Design; 1.4 Conclusions; References; Part One: Concepts, Tools, Ligands, and Scaffolds for Structure-Based Design of Inhibitors; 2 Design of Inhibitors of Aspartic Acid Proteases; 2.1 Introduction; 2.2 Design of Peptidomimetic Inhibitors of Aspartic Acid Proteases
2.3 Design of Statine-Based Inhibitors2.4 Design of Hydroxyethylene Isostere-Based Inhibitors; 2.5 Design of Inhibitors with Hydroxyethylamine Isosteres; 2.5.1 Synthesis of Optically Active α-Aminoalkyl Epoxide; 2.6 Design of (Hydroxyethyl)urea-Based Inhibitors; 2.7 (Hydroxyethyl)sulfonamide-Based Inhibitors; 2.8 Design of Heterocyclic/Nonpeptidomimetic Aspartic Acid Protease Inhibitors; 2.8.1 Hydroxycoumarin- and Hydroxypyrone-Based Inhibitors; 2.8.2 Design of Substituted Piperidine-Based Inhibitors; 2.8.3 Design of Diaminopyrimidine-Based Inhibitors 2.8.4 Design of Acyl Guanidine-Based Inhibitors2.8.5 Design of Aminopyridine-Based Inhibitors; 2.8.6 Design of Aminoimidazole- and Aminohydantoin-Based Inhibitors; 2.9 Conclusions; References; 3 Design of Serine Protease Inhibitors; 3.1 Introduction; 3.2 Catalytic Mechanism of Serine Protease; 3.3 Types of Serine Protease Inhibitors; 3.4 Halomethyl Ketone-Based Inhibitors; 3.5 Diphenyl Phosphonate-Based Inhibitors; 3.6 Trifluoromethyl Ketone Based Inhibitors; 3.6.1 Synthesis of Trifluoromethyl Ketones; 3.7 Peptidyl Boronic Acid-Based Inhibitors 3.7.1 Synthesis of α-Aminoalkyl Boronic Acid Derivatives3.8 Peptidyl α-Ketoamide- and α-Ketoheterocycle-Based Inhibitors; 3.8.1 Synthesis of α-Ketoamide and α-Ketoheterocyclic Templates; 3.9 Design of Serine Protease Inhibitors Based Upon Heterocycles; 3.9.1 Isocoumarin-Derived Irreversible Inhibitors; 3.9.2 β-Lactam-Derived Irreversible Inhibitors; 3.10 Reversible/Noncovalent Inhibitors; 3.11 Conclusions; References; 4 Design of Proteasome Inhibitors; 4.1 Introduction; 4.2 Catalytic Mechanism of 20S Proteasome; 4.3 Proteasome Inhibitors; 4.3.1 Development of Boronate Proteasome Inhibitors 4.3.2 Development of β-Lactone Natural Product-Based Proteasome Inhibitors4.3.3 Development of Epoxy Ketone-Derived Inhibitors; 4.3.4 Noncovalent Proteasome Inhibitors; 4.4 Synthesis of β-Lactone Scaffold; 4.5 Synthesis of Epoxy Ketone Scaffold; 4.6 Conclusions; References; 5 Design of Cysteine Protease Inhibitors; 5.1 Introduction; 5.2 Development of Cysteine Protease Inhibitors with Michael Acceptors; 5.3 Design of Noncovalent Cysteine Protease Inhibitors; 5.4 Conclusions; References; 6 Design of Metalloprotease Inhibitors; 6.1 Introduction; 6.2 Design of Matrix Metalloprotease Inhibitors 6.3 Design of Inhibitors of Tumor Necrosis Factor-α-Converting Enzymes |
| Record Nr. | UNINA-9910132190803321 |
Ghosh Arun K.
|
||
| Weinheim, Germany : , : Wiley-VCH, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Structure-based design of drugs and other bioactive molecules : tools and strategies / / Arun K. Ghosh and Sandra Gemma
| Structure-based design of drugs and other bioactive molecules : tools and strategies / / Arun K. Ghosh and Sandra Gemma |
| Autore | Ghosh Arun K. |
| Pubbl/distr/stampa | Weinheim, Germany : , : Wiley-VCH, , 2014 |
| Descrizione fisica | 1 online resource (476 p.) |
| Disciplina | 615.190285 |
| Soggetto topico |
Drug development - Data processing
Bioactive compounds - Analysis Biopolymers |
| ISBN |
3-527-66523-4
3-527-66521-8 3-527-66524-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Structure-based Design of Drugs and Other Bioactive Molecules: Tools and Strategies; Contents; Preface; 1 From Traditional Medicine to Modern Drugs: Historical Perspective of Structure-Based Drug Design; 1.1 Introduction; 1.2 Drug Discovery During 1928-1980; 1.3 The Beginning of Structure-Based Drug Design; 1.4 Conclusions; References; Part One: Concepts, Tools, Ligands, and Scaffolds for Structure-Based Design of Inhibitors; 2 Design of Inhibitors of Aspartic Acid Proteases; 2.1 Introduction; 2.2 Design of Peptidomimetic Inhibitors of Aspartic Acid Proteases
2.3 Design of Statine-Based Inhibitors2.4 Design of Hydroxyethylene Isostere-Based Inhibitors; 2.5 Design of Inhibitors with Hydroxyethylamine Isosteres; 2.5.1 Synthesis of Optically Active α-Aminoalkyl Epoxide; 2.6 Design of (Hydroxyethyl)urea-Based Inhibitors; 2.7 (Hydroxyethyl)sulfonamide-Based Inhibitors; 2.8 Design of Heterocyclic/Nonpeptidomimetic Aspartic Acid Protease Inhibitors; 2.8.1 Hydroxycoumarin- and Hydroxypyrone-Based Inhibitors; 2.8.2 Design of Substituted Piperidine-Based Inhibitors; 2.8.3 Design of Diaminopyrimidine-Based Inhibitors 2.8.4 Design of Acyl Guanidine-Based Inhibitors2.8.5 Design of Aminopyridine-Based Inhibitors; 2.8.6 Design of Aminoimidazole- and Aminohydantoin-Based Inhibitors; 2.9 Conclusions; References; 3 Design of Serine Protease Inhibitors; 3.1 Introduction; 3.2 Catalytic Mechanism of Serine Protease; 3.3 Types of Serine Protease Inhibitors; 3.4 Halomethyl Ketone-Based Inhibitors; 3.5 Diphenyl Phosphonate-Based Inhibitors; 3.6 Trifluoromethyl Ketone Based Inhibitors; 3.6.1 Synthesis of Trifluoromethyl Ketones; 3.7 Peptidyl Boronic Acid-Based Inhibitors 3.7.1 Synthesis of α-Aminoalkyl Boronic Acid Derivatives3.8 Peptidyl α-Ketoamide- and α-Ketoheterocycle-Based Inhibitors; 3.8.1 Synthesis of α-Ketoamide and α-Ketoheterocyclic Templates; 3.9 Design of Serine Protease Inhibitors Based Upon Heterocycles; 3.9.1 Isocoumarin-Derived Irreversible Inhibitors; 3.9.2 β-Lactam-Derived Irreversible Inhibitors; 3.10 Reversible/Noncovalent Inhibitors; 3.11 Conclusions; References; 4 Design of Proteasome Inhibitors; 4.1 Introduction; 4.2 Catalytic Mechanism of 20S Proteasome; 4.3 Proteasome Inhibitors; 4.3.1 Development of Boronate Proteasome Inhibitors 4.3.2 Development of β-Lactone Natural Product-Based Proteasome Inhibitors4.3.3 Development of Epoxy Ketone-Derived Inhibitors; 4.3.4 Noncovalent Proteasome Inhibitors; 4.4 Synthesis of β-Lactone Scaffold; 4.5 Synthesis of Epoxy Ketone Scaffold; 4.6 Conclusions; References; 5 Design of Cysteine Protease Inhibitors; 5.1 Introduction; 5.2 Development of Cysteine Protease Inhibitors with Michael Acceptors; 5.3 Design of Noncovalent Cysteine Protease Inhibitors; 5.4 Conclusions; References; 6 Design of Metalloprotease Inhibitors; 6.1 Introduction; 6.2 Design of Matrix Metalloprotease Inhibitors 6.3 Design of Inhibitors of Tumor Necrosis Factor-α-Converting Enzymes |
| Record Nr. | UNINA-9910817249603321 |
|
Ghosh Arun K.
|
||
| Weinheim, Germany : , : Wiley-VCH, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
