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 m...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Roy, Kunal, 1971- (Autor), Kar, Supratik (Autor), Das, Rudra Narayan (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam ; Boston : Academic Press, an imprint of Elsevier, [2015]
�2015
Temas:
QSAR (Biochemistry)
Pharmacology.
Pharmacological Phenomena
Quantitative Structure-Activity Relationship
Pharmacology
Relations structure-activit�e quantitatives (Biochimie)
Pharmacologie.
pharmacology.
MEDICAL > Pharmacology.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.

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