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Quantitative Structure-Activity Relationships in Drug Design, Predictive Toxicology, and Risk Assessment

Book Description

Quantitative structure-activity relationships (QSARs) represent predictive models derived from the application of statistical tools correlating biological activity or other properties of chemicals with descriptors representative of molecular structure and/or property. Quantitative Structure-Activity Relationships in Drug Design, Predictive Toxicology, and Risk Assessment discusses recent advancements in the field of QSARs with special reference to their application in drug development, predictive toxicology, and chemical risk analysis. Focusing on emerging research in the field, this book is an ideal reference source for industry professionals, students, and academicians in the fields of medicinal chemistry and toxicology.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
    1. Mission
    2. Coverage
  5. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
  6. Foreword
  7. Preface
  8. Acknowledgment
  9. Chapter 1: An Introduction to the Basic Concepts in QSAR-Aided Drug Design
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. BIOLOGICAL ACTIVITY DATA
    5. CHEMICAL STRUCTURE REPRESENTATION
    6. MOLECULAR DESCRIPTORS
    7. DESCRIPTOR SELECTION METHODS
    8. QSAR MODEL BUILDING METHODS
    9. STATISTICAL TREATMENTS AND MODEL VALIDATION
    10. 3D-QSAR STUDIES
    11. CASE STUDIES
    12. FUTURE RESEARCH DIRECTIONS
    13. CONCLUSION
    14. REFERENCES
    15. KEY TERMS AND DEFINITIONS
    16. ENDNOTES
  10. Chapter 2: The “ETA” Indices in QSAR/QSPR/QSTR Research
    1. ABSTRACT
    2. INTRODUCTION
    3. THE ETA INDICES
    4. SAMPLE CALCULATION
    5. AN OVERVIEW OF THE MODELED STUDIES
    6. CONCLUSION AND FUTURE AVENUES
    7. REFERENCES
    8. KEY TERMS AND DEFINITIONS
  11. Chapter 3: Evolution of Multivariate Image Analysis in QSAR
    1. ABSTRACT
    2. INTRODUCTION
    3. MULTIVARIATE IMAGE ANALYSIS IN QSAR
    4. CASE STUDY: TRYPANOSOMIASIS
    5. FUTURE RESEARCH DIRECTIONS
    6. CONCLUSION
    7. REFERENCES
    8. ADDITIONAL READING
    9. KEY TERMS AND DEFINITIONS
  12. Chapter 4: Quantitative Structure-Activity/Property/Toxicity Relationships through Conceptual Density Functional Theory-Based Reactivity Descriptors
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. QUANTITATIVE STRUCTURE-ACTIVITY/PROPERTY/TOXICITY RELATIONSHIPS
    4. 3. CONCEPTUAL DENSITY FUNCTIONAL THEORY
    5. 4. ELECTROPHILICITY INDEX, NET ELECTROPHILICITY INDEX, AND THEIR LOCAL VARIANTS IN PREDICTING TOXICITY
    6. 5. GROUP PHILICITY TOWARDS MODELING QSAR
    7. 6. EFFECT OF CHOSEN FUNCTIONALS IN BUILDING GLOBAL REACTIVITY DESCRIPTORS BASED QSAR/QSPR/QSTR MODELS
    8. 7. CONCLUSION
    9. ACKNOWLEDGMENT
    10. REFERENCES
    11. KEY TERMS AND DEFINITIONS
    12. APPENDIX: LIST OF SYMBOLS AND ABBREVIATIONS
  13. Chapter 5: Importance of Applicability Domain of QSAR Models
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. APPLICABILITY DOMAIN (AD)
    5. APPLICABILTY DOMAIN APPROACHES
    6. 1. RANGES IN THE DESCRIPTOR SPACE APPROACH
    7. 2. GEOMETRICAL METHODS
    8. 3. DISTANCE-BASED METHODS
    9. 4. PROBABILITY DENSITY DISTRIBUTION
    10. 5. RANGE OF THE RESPONSE VARIABLE
    11. 6. MISCELLANEOUS APPROACHES
    12. PRACTICAL EXAMPLES OF DETERMINATION OF AD
    13. FUTURE RESEARCH DIRECTION
    14. CONCLUSION
    15. REFERENCES
    16. ADDITIONAL READING
    17. KEY TERMS AND DEFINITIONS
  14. Chapter 6: QSAR of Antioxidants
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. MECHANISM OF ACTION OF ANTIOXIDANTS
    5. METHODS OF ANTIOXIDANTS DETERMINATION
    6. COMPUTER AIDED DRUG DESIGN (CADD)
    7. QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS (QSAR)
    8. STATISTICAL METHODS
    9. VALIDATION OF QSAR MODELS
    10. CONCLUSION
    11. REFERENCES
    12. KEY TERMS AND DEFINITIONS
  15. Chapter 7: QSAR Studies on Bacterial Efflux Pump Inhibitors
    1. ABSTRACT
    2. INTRODUCTION
    3. OVERVIEW OF BACTERIAL EFFLUX PUMPS AND ANTIBIOTIC RESISTANCE
    4. QSAR APPROACHES IN SEARCH FOR EFFLUX PUMP INHIBITORS
    5. DESCRIPTORS AND METHODS
    6. FUTURE RESEARCH AND OUTLOOK
    7. CONCLUDING REMARKS
    8. ACKNOWLEDGMENT
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
  16. Chapter 8: Integrated in Silico Methods for the Design and Optimization of Novel Drug Candidates
    1. ABSTRACT
    2. INTRODUCTION
    3. QSAR-AIDED IN SILICO INTEGRATIONS FOR SMALL-SCALE LIGAND SCREENING
    4. HITS IDENTIFICATION, SELECTION, AND NOVEL SAR RECOMMENDATIONS
    5. CASE STUDY: IN SILICO DESIGN AND IDENTIFICATION OF NOVEL 6-FLUOROQUINOLONES AS POTENTIAL INHIBITORS AGAINST MYCOBACTERIUM TUBERCULOSIS DNA GYRASE
    6. CONCLUSION
    7. REFERENCES
    8. KEY TERMS AND DEFINITIONS
    9. ENDNOTES
  17. Chapter 9: Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors
    1. ABSTRACT
    2. INTRODUCTION
    3. HCV GENOME AND STRUCTURE
    4. COMPUTATIONAL METHOD IN DRUG DESIGN
    5. NONSTRUCTURAL PROTEIN 5B (NS5B)
    6. QSAR APPROACH EMPLOYED TO IDENTIFY NOVEL NS5B INHIBITORS
    7. VIRTUAL SCREENING METHOD IN IDENTIFYING NS5B POLYMERASE INHIBITORS
    8. NS3/NS4A PROTEASE
    9. CONVENTIONAL QSAR APPROACH IN DISCOVERY FOR NS3/4A PROTEASE INHIBITORS
    10. CONCLUSION
    11. FUTURE DIRECTION
    12. ACKNOWLEDGMENT
    13. REFERENCES
    14. KEY TERMS AND DEFINITIONS
  18. Chapter 10: QSAR Models towards Cholinesterase Inhibitors for the Treatment of Alzheimer's Disease
    1. ABSTRACT
    2. INTRODUCTION
    3. ACETYLCHOLINESTERASE ENZYME
    4. BACKGROUND
    5. AN OVERVIEW OF DUAL BINDING SITE ACHEIS
    6. HISTORY OF QSAR
    7. QUALITY OF FIT AND QSAR MODEL VALIDATION METHODS
    8. COMPUTATIONAL APPROACHES ON DRUG DESIGN
    9. CONCLUSION AND FUTURE PROSPECTIVE
    10. REFERENCES
    11. KEY TERMS AND DEFINITIONS
  19. Chapter 11: Ligand- and Structure-Based Drug Design of Non-Steroidal Aromatase Inhibitors (NSAIs) in Breast Cancer
    1. ABSTRACT
    2. INTRODUCTION
    3. BREAST CANCER AND ITS TREATMENT
    4. AROMATASE BIOCHEMISTRY
    5. STRUCTURE OF AROMATASE
    6. EXPRESSION OF GENE OF AROMATASE
    7. AROMATASE INHIBITORS (AIs)
    8. LIGAND- AND STRUCTURE-BASED DRUG DESIGN (LBDD AND SBDD) OF AROMATASE INHIBITORS
    9. FUTURE RESEARCH DIRECTIONS
    10. CONCLUSION
    11. REFERENCES
    12. ADDITIONAL READING
    13. KEY TERMS AND DEFINITIONS
  20. Chapter 12: Computational Techniques Application in Environmental Exposure Assessment
    1. ABSTRACT
    2. INTRODUCTION
    3. BASIC IDEA AND INCREASING IMPORTANCE OF QSPR/QSAR METHODS
    4. QSPR/QSAR MODELLING ALGORITHM
    5. EVALUATION OF MOBILITY OF POPS IN THE ENVIRONMENT
    6. PERSPECTIVE OF FURTHER DEVELOPEMENT OF COMPUTATIONAL TECHNIQUES FOR ENVIRONMENTAL EXPOSURE ASSESSMENT
    7. CONCLUSION
    8. ACKNOWLEDGMENT
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
  21. Chapter 13: QSAR-Based Studies of Nanomaterials in the Environment
    1. ABSTRACT
    2. INTRODUCTION
    3. NANOMATERIALS AND ENVIRONMENTAL RISKS
    4. IN SILICO STUDIES OF NANOMATERIALS IN THE ENVIRONMENT
    5. A QSAR PERTURBATION-BASED MODEL FORWARD-TARGETING ECOTOXITICY OF METAL-BASED NANOPARTICLES
    6. CONCLUDING REMARKS AND RECOMMENDATIONS
    7. FUTURE RESEARCH DIRECTIONS
    8. ACKNOWLEDGMENT
    9. REFERENCES
    10. KEY TERMS AND DEFINITIONS
  22. Chapter 14: Quantitative Nanostructure-Activity Relationship Models for the Risk Assessment of NanoMaterials
    1. ABSTRACT
    2. INTRODUCTION
    3. BACKGROUND
    4. PERSPECTIVES AND FUTURE OF COMPUTATIONAL NANOTOXICOLOGY
    5. CONCLUSION
    6. ACKNOWLEDGMENT
    7. REFERENCES
    8. ADDITIONAL READING
    9. KEY TERMS AND DEFINITIONS
  23. Chapter 15: QSPR/QSAR Analyses by Means of the CORAL Software
    1. ABSTRACT
    2. INTRODUCTION
    3. THE COLLECTION OF QSPR/QSAR MODELS
    4. SOLUTIONS AND RECOMMENDATIONS
    5. FUTURE RESEARCH DIRECTIONS
    6. CONCLUSION
    7. ACKNOWLEDGMENT
    8. REFERENCES
    9. KEY TERMS AND DEFINITIONS
  24. Compilation of References
  25. About the Contributors