Description

Book Synopsis


Table of Contents
1. QSAR facilitating safety evaluation and risk assessment Part I: Methods and Advances of QSAR 2. Development of QSAR models as reliable computational tools for regulatory assessment of chemicals for acute toxicity 3. Deep learning-based descriptors as input for QSAR 4. Decision Forest – A machine learning algorithms for QSAR modeling 5. Integrated modelling for compound efficacy and safety assessment 6. Deep learning QSAR methods for chemical toxicity prediction and risk assessment 7. Predictive modeling approaches for the risk assessment of persistent organic pollutants: Classical to Machine learning based QSAR Models 8. Machine learning based QSAR for safety evaluation 9. Advances in QSAR through Artificial Intelligence and Machine Learning methods 10. Advances of the QSAR approach as an alternative strategy in the Environmental Risk Assessment 11. QSAR modeling based on graph neural networks Part II: Tools and Data Sources for QSAR 12. Modeling safety and risk assessment with VEGA HUB 13. Recent advancements in QSAR and Machine Learning Approaches for risk assessment of organic chemicals 14. admetSAR - a valuable tool for assisting safety evaluation 15. QSAR tools for toxicity prediction in risk assessment – a comparative analysis 16. Fast and Efficient Implementation of Computational Toxicology Solutions Using the FlexFilters Platform 17. Annotate a standard dataset for drug-induced liver injury to support developing QSAR models 18. Application of QSAR Models Based on Machine Learning Methods in Chemical Risk Assessment and Drug Discovery 19. EADB – The database providing curated data for developing QSAR models to facilitate assessment of endocrine activity 20. Centralized data sources and QSAR methods for the prediction of idiosyncratic adverse drug reaction Part III: QSAR models for Safety Evaluation of Drugs and Consumer Products 21. QSAR modeling for predicting drug-induced liver injury 22. The need of QSAR methods to assess safety of chemicals in food contact materials 23. QSAR models for predicting in vivo reproductive toxicity 24. Aryl hydrocarbon receptors and their ligands in human health management 25. Use of in silico protocols to evaluate drug safety 26. QSAR models for predicting cardiac toxicity of dugs Part IV: QSAR models for Risk Assessment of Chemicals 27. Similarity-based analyses for the false-positive and false-negative chemicals on the second Ames/QSAR international challenge project 28. QSAR Model of Photolysis Kinetic Parameters in Aquatic Environment 29. QSAR models on transthyretin disrupting effects of chemicals 30. QSAR models for toxicity assessment of multicomponent systems 31. Deploying QSAR to discriminate excess toxicity and identify the toxic mode of action of organic pollutants to aquatic organisms 32. QSAR models for prediction of carrying capacity of microplastic towards organic pollutants 33. QSAR models on degradation rate constants of atmospheric pollutants Part V: QSAR models in Material Science and Other Areas 34. Significance of QSAR in cancer risk assessment of polycyclic aromatic compounds (PACs) 35. QSAR in risk assessment of nanomaterials 36. In silico and in vitro ecotoxicity - QSAR based predictions for the aquatic environment 37. In vitro to in vivo Extrapolation Methods in Chemical Hazard Identification and Risk Assessment 38. QSAR models in marine ecotoxicology

QSAR in Safety Evaluation and Risk Assessment

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      Publisher: Elsevier Science
      Publication Date: Publication Date: 8/22/2023 12:00:00 AM
      ISBN13: 9780443153396, 978-0443153396
      ISBN10: 0443153396

      Description

      Book Synopsis


      Table of Contents
      1. QSAR facilitating safety evaluation and risk assessment Part I: Methods and Advances of QSAR 2. Development of QSAR models as reliable computational tools for regulatory assessment of chemicals for acute toxicity 3. Deep learning-based descriptors as input for QSAR 4. Decision Forest – A machine learning algorithms for QSAR modeling 5. Integrated modelling for compound efficacy and safety assessment 6. Deep learning QSAR methods for chemical toxicity prediction and risk assessment 7. Predictive modeling approaches for the risk assessment of persistent organic pollutants: Classical to Machine learning based QSAR Models 8. Machine learning based QSAR for safety evaluation 9. Advances in QSAR through Artificial Intelligence and Machine Learning methods 10. Advances of the QSAR approach as an alternative strategy in the Environmental Risk Assessment 11. QSAR modeling based on graph neural networks Part II: Tools and Data Sources for QSAR 12. Modeling safety and risk assessment with VEGA HUB 13. Recent advancements in QSAR and Machine Learning Approaches for risk assessment of organic chemicals 14. admetSAR - a valuable tool for assisting safety evaluation 15. QSAR tools for toxicity prediction in risk assessment – a comparative analysis 16. Fast and Efficient Implementation of Computational Toxicology Solutions Using the FlexFilters Platform 17. Annotate a standard dataset for drug-induced liver injury to support developing QSAR models 18. Application of QSAR Models Based on Machine Learning Methods in Chemical Risk Assessment and Drug Discovery 19. EADB – The database providing curated data for developing QSAR models to facilitate assessment of endocrine activity 20. Centralized data sources and QSAR methods for the prediction of idiosyncratic adverse drug reaction Part III: QSAR models for Safety Evaluation of Drugs and Consumer Products 21. QSAR modeling for predicting drug-induced liver injury 22. The need of QSAR methods to assess safety of chemicals in food contact materials 23. QSAR models for predicting in vivo reproductive toxicity 24. Aryl hydrocarbon receptors and their ligands in human health management 25. Use of in silico protocols to evaluate drug safety 26. QSAR models for predicting cardiac toxicity of dugs Part IV: QSAR models for Risk Assessment of Chemicals 27. Similarity-based analyses for the false-positive and false-negative chemicals on the second Ames/QSAR international challenge project 28. QSAR Model of Photolysis Kinetic Parameters in Aquatic Environment 29. QSAR models on transthyretin disrupting effects of chemicals 30. QSAR models for toxicity assessment of multicomponent systems 31. Deploying QSAR to discriminate excess toxicity and identify the toxic mode of action of organic pollutants to aquatic organisms 32. QSAR models for prediction of carrying capacity of microplastic towards organic pollutants 33. QSAR models on degradation rate constants of atmospheric pollutants Part V: QSAR models in Material Science and Other Areas 34. Significance of QSAR in cancer risk assessment of polycyclic aromatic compounds (PACs) 35. QSAR in risk assessment of nanomaterials 36. In silico and in vitro ecotoxicity - QSAR based predictions for the aquatic environment 37. In vitro to in vivo Extrapolation Methods in Chemical Hazard Identification and Risk Assessment 38. QSAR models in marine ecotoxicology

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