Online resource; title from PDF title page (Ebsco, viewed March 18, 2015).

Includes bibliographical references and index.

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.

Machine generated contents note: 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- 9. Newer QSAR Techniques -- 9.1. Introduction -- 9.2. HQSAR -- 9.3.G-QSAR -- 9.4. Other Approaches -- 9.5. Overview and Conclusions -- References -- 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 sillco Screening of Chemical Libraries: VS -- 10.6. Overview and Conclusions -- References -- 11. SAR and QSAR in Drug Discovery and Chemical Design -- Some Examples -- 11.1. Introduction -- 11.2. Successful Applications of QSAR and Other In Sillco Methods: Representative Examples -- 11.3. Conclusion -- References -- 12. Future Avenues -- 12.1. Introduction -- 12.2. Application Areas -- 12.3. Conclusion -- References.