Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment

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.

• 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

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

• 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