Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment
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Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment describes the historical evolution of quantitative structure-activity relationship (QSAR) approaches and their fundamental principles. This book includes clear, introductory coverage of the statistical methods applied in QSAR and new QSAR techniques, such as HQSAR and G-QSAR. Containing real-world examples that illustrate important methodologies, this book identifies QSAR as a valuable tool for many different applications, including drug discovery, predictive toxicology and risk assessment. Written in a straightforward and engaging manner, this is the ideal resource for all those looking for general and practical knowledge of QSAR methods.
Key Features
- Includes numerous practical examples related to QSAR methods and applications
- Follows the Organization for Economic Co-operation and Development principles for QSAR model development
- Discusses related techniques such as structure-based design and the combination of structure- and ligand-based design tools
Readership
New researchers, professors and graduate students across the pharmaceutical sciences (including pharmacology, toxicology and medicinal chemistry); secondary audience of regulatory officials and risk assessors in toxicology and environmental health
Table of Contents
- Dedication
- Foreword
- References
- Preface
- Chapter 1. Background of QSAR and Historical Developments
- 1.1 Introduction
- 1.2 Physicochemical Aspects of Biological Activity of Drugs and Chemicals
- 1.3 Structure–Activity Relationship
- 1.4 Historical Development of QSARs: A Journey of Knowledge Enrichment
- 1.5 Applications of QSAR
- 1.6 Regulatory Perspectives of QSAR
- 1.7 Overview and Conclusion
- References
- Chapter 2. Chemical Information and Descriptors
- 2.1 Introduction
- 2.2 Concept of Descriptors
- 2.3 Type of Descriptors
- 2.4 Descriptors Commonly Used in QSAR Studies
- 2.5 Overview and Conclusion
- References
- Chapter 3. Classical QSAR
- 3.1 Introduction
- 3.2 The Free–Wilson Model
- 3.3 The Fujita–Ban Model
- 3.4 The LFER Model
- 3.5 Kubinyi’s Bilinear Model
- 3.6 The Mixed Approach
- 3.7 Overview and Conclusions
- References
- Chapter 4. Topological QSAR
- 4.1 Introduction
- 4.2 Topology: A Method of Chemical Structure Representation
- 4.3 Graphs and Matrices: Platforms for the Topological Paradigm
- 4.4 Topological Indices
- 4.5 Conclusion and Possibilities
- References
- Chapter 5. Computational Chemistry
- 5.1 Introduction
- 5.2 Computer Use in Chemistry
- 5.3 Conformational Analysis and Energy Minimization
- 5.4 Molecular Mechanics
- 5.5 Molecular Dynamics
- 5.6 Quantum Mechanics
- 5.7 Overview and Conclusion
- References
- Chapter 6. Selected Statistical Methods in QSAR
- 6.1 Introduction
- 6.2 Regression-Based Approaches
- 6.3 Classification-Based QSAR
- 6.4 Machine Learning Techniques
- 6.5 Conclusion
- References
- Chapter 7. Validation of QSAR Models
- 7.1 Introduction
- 7.2 Different Validation Methods
- 7.3 A Practical Example of the Calculation of Common Validation Metrics and the AD
- 7.4 QSAR model reporting format
- 7.5 Overview and Conclusion
- References
- Chapter 8. Introduction to 3D-QSAR
- 8.1 Introduction
- 8.2 Comparative Molecular Field Analysis
- 8.3 Comparative Molecular Similarity Indices Analysis
- 8.4 Molecular Shape Analysis
- 8.5 Receptor Surface Analysis
- 8.6 Other Approaches
- 8.7 Overview and Conclusions
- References
- Chapter 9. Newer QSAR Techniques
- 9.1 Introduction
- 9.2 HQSAR
- 9.3 G-QSAR
- 9.4 Other Approaches
- 9.5 Overview and Conclusions
- References
- Chapter 10. Other Related Techniques
- 10.1 Introduction
- 10.2 Pharmacophore
- 10.3 Structure-Based Design–Docking
- 10.4 Combination of Structure- and Ligand-Based Design Tools
- 10.5 In Silico Screening of Chemical Libraries: VS
- 10.6 Overview and Conclusions
- References
- Chapter 11. SAR and QSAR in Drug Discovery and Chemical Design—Some Examples
- 11.1 Introduction
- 11.2 Successful Applications of QSAR and Other In Silico Methods: Representative Examples
- 11.3 Conclusion
- References
- Chapter 12. Future Avenues
- 12.1 Introduction
- 12.2 Application Areas
- 12.3 Conclusion
- References
- Index
Product details
- No. of pages: 484
- Language: English
- Copyright: © Academic Press 2015
- Published: March 3, 2015
- Imprint: Academic Press
- eBook ISBN: 9780128016336
- Paperback ISBN: 9780128015056
About the Authors
Kunal Roy
Dr. Kunal Roy is a Professor in the Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India. He has been a recipient of Commonwealth Academic Staff Fellowship (University of Manchester, 2007) and Marie Curie International Incoming Fellowship (University of Manchester, 2013). The field of his research interest is QSAR and Molecular Modeling with application in Drug Design and Ecotoxicological Modeling. Dr. Roy has published more than 280 research articles in refereed journals (current SCOPUS h index 38). He has also coauthored two QSAR-related books, edited three QSAR books and published more than ten book chapters. Dr. Roy is a Co-Editor-in-Chief of Molecular Diversity (Springer Nature) and Editor-in-Chief of International Journal of Quantitative Structure-Property Relationships (IJQSPR) (IGI Global). He also serves as a member of Editorial Boards of several International Journals.
Affiliations and Expertise
Associate Professor, Drug Theoretics and Cheminformatics Lab, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
Supratik Kar
Affiliations and Expertise
MPharm, Researcher, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
Rudra Narayan Das
Affiliations and Expertise
MPharm, Researcher, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
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