QSAR in Safety Evaluation and Risk Assessment provides comprehensive coverage on QSAR methods, tools, data sources, and models focusing on applications in products safety evaluation and chemicals risk assessment.Organized into five parts, the book covers almost all aspects of QSAR modeling and application. Topics in the book include methods of QSAR, from both scientific and regulatory viewpoints; data sources available for facilitating QSAR models development; software tools for QSAR development; and QSAR models developed for assisting safety evaluation and risk assessment. Chapter contributors are authored by a lineup of active scientists in this field. The chapters not only provide professional level technical summarizations but also cover introductory descriptions for all aspects of QSAR for safety evaluation and risk assessment. Provides comprehensive content about the QSAR techniques and models in facilitating the safety evaluation of drugs and consumer products and risk assesment of environmental chemicals Includes some of the most cutting-edge methodologies such as deep learning and machine learning for QSAR Offers detailed procedures of modeling and provides examples of each model's application in real practice
1. QSAR in Safety Evaluation and Risk Assessment - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright - p. iv 5. Table of Contents - p. iv 6. List of contributors - p. xv 7. Preface - p. xix 8. Chapter 1 QSAR facilitating safety evaluation and risk assessment - p. 1 9. Chapter 2 Development of QSAR models as reliable computational tools for regulatory assessment of chemicals for acute toxicity - p. 13 10. Chapter 3 Neural network-based descriptors as input for QSAR - p. 25 11. Chapter 4 Decision forest—a machine learning algorithm for QSAR modeling - p. 35 12. Chapter 5 Integrated modeling for compound efficacy and safety assessment - p. 49 13. Chapter 6 Deep learning quantitative structure–activity relationship methods for chemical toxicity prediction and risk assessment - p. 65 14. Chapter 7 Predictive modeling approaches for the risk assessment of persistent organic pollutants (POPs): from QSAR to machine learning–based models - p. 77 15. Chapter 8 Machine learning–based QSAR for safety evaluation of environmental chemicals - p. 89 16. Chapter 9 Advances in QSAR through artificial intelligence and machine learning methods - p. 101 17. Chapter 10 Advances of the QSAR approach as an alternative strategy in the environmental risk assessment - p. 117 18. Chapter 11 QSAR modeling based on graph neural networks - p. 139 19. Chapter 12 Modeling safety and risk assessment with VEGAHUB - p. 155 20. Chapter 13 Recent advancements in QSAR and machine learning approaches for risk assessment of organic chemicals - p. 167 21. Chapter 14 admetSAR—A valuable tool for assisting safety evaluation - p. 187 22. Chapter 15 QSAR tools for toxicity prediction in risk assessment—Comparative analysis - p. 203 23. Chapter 16 Fast and efficient implementation of computational toxicology solutions using the FlexFilters platform - p. 219 24. Chapter 17 DILIrank dataset for QSAR modeling of drug-induced liver injury - p. 235 25. Chapter 18 Application of QSAR models based on machine learning methods in chemical risk assessment and drug discovery - p. 245 26. Chapter 19 EADB—A database providing curated data for developing QSAR models to facilitate the assessment of endocrine activity - p. 259 27. Chapter 20 Centralized data sources and QSAR methods for the prediction of idiosyncratic adverse drug reaction - p. 273 28. Chapter 21 QSAR modeling for predicting drug-induced liver injury - p. 295 29. Chapter 22 The need of QSAR methods to assess safety of chemicals in food contact materials - p. 301 30. Chapter 23 QSAR models for predicting in vivo reproductive toxicity - p. 315 31. Chapter 24 Aryl hydrocarbon receptors and their ligands in human health management - p. 329 32. Chapter 25 Use of in silico protocols to evaluate drug safety - p. 339 33. Chapter 26 QSAR models for predicting cardiac toxicity of drugs - p. 351 34. Chapter 27 Curation of more than 10,000 Ames test data used in the Ames/QSAR International Challenge Projects - p. 365 35. Chapter 28 QSAR model of photolysis kinetic parameters in aquatic environment - p. 373 36. Chapter 29 (Q)SAR models on transthyretin disrupting effects of chemicals - p. 389 37. Chapter 30 QSAR models for toxicity assessment of multicomponent systems - p. 409 38. Chapter 31 Deploying QSAR to discriminate excess toxicity and identify the toxic mode of action of organic pollutants to aquatic organisms - p. 427 39. Chapter 32 Theoretical prediction for carrying capacity of microplastic toward organic pollutants - p. 447 40. Chapter 33 QSAR models on degradation rate constants of atmospheric pollutants - p. 459 41. Chapter 34 Significance of QSAR in cancer risk assessment of polycyclic aromatic compounds (PACs) - p. 475 42. Chapter 35 QSAR in risk assessment of nanomaterials - p. 483 43. Chapter 36 In silico and in vivo ecotoxicity—QSAR-based predictions and experimental assays for the aquatic environment - p. 495 44. Chapter 37 In vitro to in vivo extrapolation methods in chemical hazard identification and risk assessment - p. 511 45. Chapter 38 QSAR models in marine ecotoxicology and risk assessment - p. 523 46. Index - p. 533
QSAR in Safety Evaluation and Risk Assessment provides comprehensive coverage on QSAR methods, tools, data sources, and models focusing on applications in products safety evaluation and chemicals risk assessment.Organized into five parts, the book covers almost all aspects of QSAR modeling and application. Topics in the book include methods of QSAR, from both scientific and regulatory viewpoints; data sources available for facilitating QSAR models development; software tools for QSAR development; and QSAR models developed for assisting safety evaluation and risk assessment. Chapter contributors are authored by a lineup of active scientists in this field. The chapters not only provide professional level technical summarizations but also cover introductory descriptions for all aspects of QSAR for safety evaluation and risk assessment. Provides comprehensive content about the QSAR techniques and models in facilitating the safety evaluation of drugs and consumer products and risk assesment of environmental chemicals Includes some of the most cutting-edge methodologies such as deep learning and machine learning for QSAR Offers detailed procedures of modeling and provides examples of each model's application in real practice
1. QSAR in Safety Evaluation and Risk Assessment - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright - p. iv 5. Table of Contents - p. iv 6. List of contributors - p. xv 7. Preface - p. xix 8. Chapter 1 QSAR facilitating safety evaluation and risk assessment - p. 1 9. Chapter 2 Development of QSAR models as reliable computational tools for regulatory assessment of chemicals for acute toxicity - p. 13 10. Chapter 3 Neural network-based descriptors as input for QSAR - p. 25 11. Chapter 4 Decision forest—a machine learning algorithm for QSAR modeling - p. 35 12. Chapter 5 Integrated modeling for compound efficacy and safety assessment - p. 49 13. Chapter 6 Deep learning quantitative structure–activity relationship methods for chemical toxicity prediction and risk assessment - p. 65 14. Chapter 7 Predictive modeling approaches for the risk assessment of persistent organic pollutants (POPs): from QSAR to machine learning–based models - p. 77 15. Chapter 8 Machine learning–based QSAR for safety evaluation of environmental chemicals - p. 89 16. Chapter 9 Advances in QSAR through artificial intelligence and machine learning methods - p. 101 17. Chapter 10 Advances of the QSAR approach as an alternative strategy in the environmental risk assessment - p. 117 18. Chapter 11 QSAR modeling based on graph neural networks - p. 139 19. Chapter 12 Modeling safety and risk assessment with VEGAHUB - p. 155 20. Chapter 13 Recent advancements in QSAR and machine learning approaches for risk assessment of organic chemicals - p. 167 21. Chapter 14 admetSAR—A valuable tool for assisting safety evaluation - p. 187 22. Chapter 15 QSAR tools for toxicity prediction in risk assessment—Comparative analysis - p. 203 23. Chapter 16 Fast and efficient implementation of computational toxicology solutions using the FlexFilters platform - p. 219 24. Chapter 17 DILIrank dataset for QSAR modeling of drug-induced liver injury - p. 235 25. Chapter 18 Application of QSAR models based on machine learning methods in chemical risk assessment and drug discovery - p. 245 26. Chapter 19 EADB—A database providing curated data for developing QSAR models to facilitate the assessment of endocrine activity - p. 259 27. Chapter 20 Centralized data sources and QSAR methods for the prediction of idiosyncratic adverse drug reaction - p. 273 28. Chapter 21 QSAR modeling for predicting drug-induced liver injury - p. 295 29. Chapter 22 The need of QSAR methods to assess safety of chemicals in food contact materials - p. 301 30. Chapter 23 QSAR models for predicting in vivo reproductive toxicity - p. 315 31. Chapter 24 Aryl hydrocarbon receptors and their ligands in human health management - p. 329 32. Chapter 25 Use of in silico protocols to evaluate drug safety - p. 339 33. Chapter 26 QSAR models for predicting cardiac toxicity of drugs - p. 351 34. Chapter 27 Curation of more than 10,000 Ames test data used in the Ames/QSAR International Challenge Projects - p. 365 35. Chapter 28 QSAR model of photolysis kinetic parameters in aquatic environment - p. 373 36. Chapter 29 (Q)SAR models on transthyretin disrupting effects of chemicals - p. 389 37. Chapter 30 QSAR models for toxicity assessment of multicomponent systems - p. 409 38. Chapter 31 Deploying QSAR to discriminate excess toxicity and identify the toxic mode of action of organic pollutants to aquatic organisms - p. 427 39. Chapter 32 Theoretical prediction for carrying capacity of microplastic toward organic pollutants - p. 447 40. Chapter 33 QSAR models on degradation rate constants of atmospheric pollutants - p. 459 41. Chapter 34 Significance of QSAR in cancer risk assessment of polycyclic aromatic compounds (PACs) - p. 475 42. Chapter 35 QSAR in risk assessment of nanomaterials - p. 483 43. Chapter 36 In silico and in vivo ecotoxicity—QSAR-based predictions and experimental assays for the aquatic environment - p. 495 44. Chapter 37 In vitro to in vivo extrapolation methods in chemical hazard identification and risk assessment - p. 511 45. Chapter 38 QSAR models in marine ecotoxicology and risk assessment - p. 523 46. Index - p. 533
