Dr. Thomas Engel - Faculty for Chemistry and Pharmacy
Dr. Thomas Engel - Faculty for Chemistry and Pharmacy
ContentsForeword
List of Contributors
1. Introduction
2. QSAR/QSPR
3. Prediction of Physicochemical Properties ofCompounds
4. Chemical Reactions
5 Structure–Spectrum Correlations and Computer-Assisted Structure Elucidation
6.1 Drug Discovery: An Overview
6.2 Bridging Information on Drugs, Targets, and Diseases
6.3 Chemoinformatics in Natural Product Research
6.4 Chemoinformatics of Chinese Herbal Medicines
6.5 PubChem
6.6 Pharmacophore Perception and Applications
6.7 Prediction, Analysis, and Comparison of Active Sites
6.8 Structure-Based Virtual Screening
6.9 Prediction of ADME Properties
6.10 Prediction of Xenobiotic Metabolism
6.11 Chemoinformatics at the CADD Group of the National Cancer Institute
6.12 Uncommon Data Sources for QSAR Modeling
6.13 Future Perspectives of Computational Drug Design
7. Computational Approaches in Agricultural Research
8. Chemoinformatics in Modern Regulatory Science
9. Chemometrics in Analytical Chemistry
10. Chemoinformatics in Food Science
11. Computational Approaches to Cosmetics Products Discovery
12. Applications in Materials Science
13. Process Control and Soft Sensors
14. Future Directions
Index
(Thomas Engel and Johann Gasteiger)
1.1 The Rationale for the Books
1.2 Development of the Field
1.3 The Basis of Chemoinformatics and the Diversity of Applications
(Wolfgang Sippl and Dina Robaa)
2.1 Introduction
2.2 Data Handling and Curation
2.3 Molecular Descriptors
2.4 Methods for Data Analysis
2.5 Classification Methods
2.6 Methods for DataModeling
2.7 Summary on Data Analysis Methods
2.8 Model Validation
2.9 Regulatory Use of QSARs
(Igor V. Tetko, Aixia Yan, and Johann Gasteiger)
3.1 Introduction
3.2 Overview of Modeling Approaches to Predict Physicochemical Properties
3.3 Methods for the Prediction of Individual Properties
3.4 Limitations of StatisticalMethods
3.5 Outlook and Perspectives
4.1 Chemical Reactions – An Introduction (Johann Gasteiger)
4.2 Reaction Prediction and Synthesis Design (Jonathan M. Goodman)
4.2.1 Introduction
4.2.2 Reaction Prediction
4.2.3 Synthesis Design
4.2.4 Conclusion
4.3 Explorations into Biochemical Pathways (Oliver Sacher and Johann Gasteiger)
4.3.1 Introduction
4.3.2 The BioPath.Database
4.3.3 BioPath.Explore
4.3.4 Search Results
4.3.5 Exploitation of the Information in BioPath.Database
4.3.6 Summary
(Joao Aires de Sousa)
5.1 Introduction
5.2 Molecular Descriptors
5.3 Infrared Spectra
5.4 NMR Spectra
5.5 Mass Spectra
5.6 Computer-Aided Structure Elucidation (CASE)
(Lothar Terfloth, Simon Spycher, and Johann Gasteiger)
6.1.1 Introduction
6.1.2 Definitions of Some Terms Used in Drug Design
6.1.3 The Drug Discovery Process
6.1.4 Bio- and Chemoinformatics Tools for Drug Design
6.1.5 Ligand-Based Drug Design
6.1.6 Target Identification and Validation
6.1.7 Lead Finding
6.1.8 Lead Optimization
6.1.9 Preclinical and Clinical Trials
6.1.10 Outlook: Future Perspectives
(Andreas Steffen and BertramWeiss)
6.2.1 Introduction
6.2.2 Existing Data Sources
6.2.3 Drug Discovery Use Cases in Computational Life Sciences
6.2.4 Discussion and Outlook
(Teresa Kaserer, Daniela Schuster, and Judith M. Rollinger)
6.3.1 Introduction
6.3.2 Potential and Challenges
6.3.3 Access to Software and Data
6.3.4 In Silico Driven Pharmacognosy-Hyphenated Strategies
6.3.5 Opportunities
6.3.6 Miscellaneous Applications
6.3.7 Limits
6.3.8 Conclusion and Outlook
(Jun Xu)
6.4.1 Introduction
6.4.2 Type 2 Diabetes:TheWestern Approach
6.4.3 Type 2 Diabetes:The Chinese Herbal Medicines Approach
6.4.4 Building a Bridge
6.4.5 Screening Approach
(Wolf-D. Ihlenfeldt)
6.5.1 Introduction
6.5.2 Objectives
6.5.3 Architecture
6.5.4 Data Sources
6.5.5 Submission Processing and Structure Representation
6.5.6 Data Augmentation
6.5.7 Preparation for Database Storage
6.5.8 Query Data Preparation and Structure Searching
6.5.9 Structure Query Input
6.5.10 Query Processing
6.5.11 Getting Started with PubChem
6.5.12 Web Services
6.5.13 Conclusion
(Thomas Seidel, GerhardWolber, andManuela S. Murgueitio)
6.6.1 Introduction
6.6.2 Historical Development of the Modern Pharmacophore Concept
6.6.3 Representation of Pharmacophores
6.6.4 Pharmacophore Modeling
6.6.5 Application of Pharmacophores in Drug Design
6.6.6 Software for Computer-Aided Pharmacophore Modeling and Screening
6.6.7 Summary
(Andrea Volkamer,Mathias M. von Behren, Stefan Bietz, and Matthias Rarey)
6.7.1 Introduction
6.7.2 Active Site Prediction Algorithms
6.7.3 Target Prioritization: Druggability Prediction
6.7.4 Search for Sequentially Homologous Pockets
6.7.5 Target Comparison: Virtual Active Site Screening
6.7.6 Summary and Outlook
(Adrian Kolodzik, Nadine Schneider, and Matthias Rarey)
6.8.1 Introduction
6.8.2 Docking Algorithms
6.8.3 Scoring
6.8.4 Structure-Based Virtual ScreeningWorkflow
6.8.5 Protein-Based Pharmacophoric Filters
6.8.6 Validation
6.8.7 Summary and Outlook
(Aixia Yan)
6.9.1 Introduction
6.9.2 General Consideration on SPR/QSPR Models
6.9.3 Estimation of Aqueous Solubility (log S)
6.9.4 Estimation of Blood–Brain Barrier Permeability (log BB)
6.9.5 Estimation of Human Intestinal Absorption (HIA)
6.9.6 Other ADME Properties
6.9.7 Summary
(Anthony Long and Ernest Murray)
6.10.1 Introduction: The Importance of Xenobiotic Biotransformation in the Life Sciences
6.10.2 Biotransformation Types
6.10.3 Brief Review of Methods
6.10.4 User Needs: Scientists Use Metabolism Information in Different Ways
6.10.5 Case Studies
(Megan L. Peach andMarc C. Nicklaus)
6.11.1 Introduction and History
6.11.2 Chemical Information Services
6.11.3 Tools and Software
6.11.4 Synthesis and Activity Predictions
6.11.5 Downloadable Datasets
(Alexander Tropsha)
6.12.1 Introduction
6.12.2 ObservationalMetadata and QSAR Modeling
6.12.3 Pharmacovigilance and QSAR
6.12.4 Conclusions
(Gisbert Schneider)
6.13.1 Where Do the Medicines of the Future Come from?
6.13.2 Integrating Design, Synthesis, and Testing
6.13.3 Toward Precision Medicine
6.13.4 Learning from Nature: From Complex Templates to Simple Designs
6.13.5 Conclusions
(Klaus-Jürgen Schleifer)
7.1 Introduction
7.2 Research Strategies
7.2.1 Ligand-Based Approaches
7.2.2 Structure-Based Approaches
7.3 Estimation of Adverse Effects
7.3.1 In Silico Toxicology
7.3.2 Programs and Databases
7.3.3 In Silico Toxicology Models
7.4 Conclusion
(Chihae Yang, James F. Rathman, Aleksey Tarkhov, Oliver Sacher, Thomas Kleinoeder, Jie Liu, ThomasMagdziarz, AleksandraMostraq, Joerg Marusczyk, DarshanMehta, Christof Schwab, and Bruno Bienfait)
8.1 Introduction
8.2 Data Gap Filling Methods in Risk Assessment
8.3 Database and Knowledge Base
8.4 New Approach Descriptors
8.5 Chemical Space Analysis
8.6 Summary
(Anita Rácz, Dávid Bajusz, and Károly Héberger)
9.1 Introduction
9.2 Sources of Data: Data Preprocessing
9.3 Data Analysis Methods
9.4 Validation
9.5 Applications
9.6 Outlook and Prospects
(Andrea Peña-Castillo, Oscar Méndez-Lucio, John R. Owen, Karina Martínez-Mayorga, and José L.Medina-Franco)
10.1 Introduction
10.2 Scope of Chemoinformatics in Food Chemistry
10.3 Molecular Databases of Food Chemicals
10.4 Chemical Space of Food Chemicals
10.5 Structure–Property Relationships
10.6 Computational Screening and Data Mining of Food Chemicals Libraries
10.7 Conclusion
(Soheila Anzali, Frank Pflücker, Lilia Heider, and Alfred Jonczyk)
11.1 Introduction: Cosmetics Demands on Computational Approaches
11.2 Case I:The Multifunctional Role of Ectoine as a Natural Cell Protectant (Product: Ectoine, "Cell Protection Factor", and Moisturizer)
11.3 Case II: A Smart Cyclopeptide Mimics the RGD Containing Cell Adhesion Proteins at the Right Site (Product: Cyclopeptide-5: Antiaging)
11.4 Conclusions: Cases I and II
(TuC. Le, andDavid A. Winkler)
12.1 Introduction
12.2 Why Materials Are Harder to Model than Molecules
12.3 Why Are Chemoinformatics Methods Important Now?
12.4 How Do You Describe Materials Mathematically?
12.5 HowWell do Chemoinformatics MethodsWork on Materials?
12.6 What Are the Pitfalls when Modeling Materials?
12.7 How Do You Make Good Models and Avoid the Pitfalls?
12.8 Materials Examples
12.9 Biomaterials Examples
12.10 Perspectives
(Kimito Funatsu)
13.1 Introduction
13.2 Roles of Soft Sensors
13.3 Problems with Soft Sensors
13.4 Adaptive Soft Sensors
13.5 Database Monitoring for Soft Sensors
13.6 Efficient Process Control Using Soft Sensors
13.7 Conclusions
(Johann Gasteiger)
14.1 Well-Established Fields of Application
14.2 Emerging Fields of Application
14.3 Renaissance of Some Fields
14.4 Combined Use of Chemoinformatics Methods
14.5 Impact on Chemical Research