Cheminformatic Modelling and Data Gap Filling for a Green and Sustainable Environment covers the theory and practices of chemical informatics, focusing on modeling various properties and endpoints related to chemicals for improved chemical management and the design of safer chemicals to promote environmental sustainability. Across four sections, the book outlines modeling techniques such as quantitative structure–property relationship (QSPR), read-across, and machine learning for modeling environmental endpoints of chemicals. OECD guidelines are discussed and considered for model development and validation, documentation using the QSAR modeling reporting format (QMRF), and regulatory requirements for result presentation.
The book offers full datasets, algorithm information, and real-world case studies for all models, along with worked examples. It will serve as an essential resource for chemists and environmental scientists working in green and sustainable chemistry, but will be a great resource for students and academics at graduate level and above studying cheminformatics. This book will also be of interest to researchers developing new and sustainable chemicals and for decision-makers looking to make industrial processes more sustainable.
1. Section I: Introduction - p. S1 2. Chapter 1 Chemicals strategy for a sustainable environment - p. 3 3. Chapter 2 Modern modeling approaches for data gap filling - p. 47 4. Chapter 3 Aquatic toxicology: Computational approaches and innovations - p. 69 5. Section II: Quantitative structure–property relationship modeling of physicochemical properties and environmental fate of chemicals - p. S2 6. Chapter 4 Quantitative structure–property relationship modeling of physicochemical properties of environmentally relevant chemicals - p. 91 7. Chapter 5 OPERA QSPR models for environmentally relevant physicochemical properties - p. 133 8. Chapter 6 The prediction of hydrolysis and biodegradation of organophosphorus-based chemical warfare agents (Novichoks, G-series, and V-series) using in silico toxicology methods - p. 165 9. Chapter 7 Machine learning models as alternative methods to predict the bioconcentration factor - p. 191 10. Chapter 8 Quantitative structure–property relationship modeling of adsorption capacity of microplastics - p. 207 11. Chapter 9 Simulation of physicochemical and biochemical behavior of nanoparticles under various experimental conditions - p. 267 12. Chapter 10 Modeling of physicochemical properties of nanoparticles using QSPR analysis - p. 281 13. Chapter 11 QSPR modeling of physicochemical properties of nanoparticles - p. 325 14. Section III: Computational modeling of toxicity and ecotoxicity of chemicals - p. S3 15. Chapter 12 Computational modeling of acute toxicity of pharmaceuticals and related chemicals - p. 349 16. Chapter 13 Computational modeling of aquatic toxicity of nanoparticles - p. 385 17. Chapter 14 Computational modeling of acute and chronic toxicities of organic solvents - p. 425 18. Chapter 15 Computational modeling of acute and chronic toxicities of chemicals of emerging concern - p. 459 19. Chapter 16 Computational approaches in toxicity prediction: The role of quantitative structure–activity relationship (QSAR) in modern chemical risk assessment in the water ecosystems - p. 497 20. Chapter 17 Computational modeling of avian toxicities: Risk assessment of chemicals - p. 531 21. Chapter 18 Computational modeling of the genotoxicity and carcinogenicity of chemicals - p. 559 22. Chapter 19 Computational modeling of skin sensitization of chemicals - p. 589 23. Chapter 20 Emerging advances in modeling chemical mutagenicity and carcinogenicity - p. 607 24. Chapter 21 Computational modeling of genotoxic chemicals - p. 633 25. Section IV: Additional topics - p. S4 26. Chapter 22 Databases for chemical toxicity and ecotoxicity - p. 663 27. Chapter 23 Open-source modeling tools for chemical toxicity and ecotoxicity - p. 695 28. Chapter 24 Chemical language models for chemical toxicity and ecotoxicity prediction - p. 737 29. Chapter 25 Application of artificial intelligence/machine learning in modeling chemical toxicity and ecotoxicity - p. 761 30. Chapter 26 Multitask learning and transfer learning approaches in target-based chemical toxicity modeling: G-protein-coupled receptors as an example - p. 815 31. Chapter 27 In silico modeling of properties and toxicities of chemical mixtures - p. 837 32. Chapter 28 Chemical and physical properties databases - p. 865 33. Chapter 29 Advanced cheminformatics models for predicting PFAS potency and environmental impact in sustainable chemistry, powered by Enalos Cloud Platform - p. 893 34. Chapter 30 Applying partial ordering methodology to the study of environmental pollutants - p. 921 35. Chapter 31 Cheminformatics in life cycle assessment: Advancing solvent, toxicology, and chemical synthesis for sustainable innovation - p. 951 36. Chapter 32 The virtual extensive read-across (VERA) tool for read-across: A flexible approach - p. 975 37. Chapter 33 MetaQSAR: A comprehensive tool for automated QSAR modeling - p. 993 38. Chapter 34 ProtoPRED: a versatile, user-friendly platform for in silico predictions of physicochemical, eco(toxicological), and pharmacokinetic parameters in a regulatory context - p. 1021 39. Index - p. 1049
Cheminformatic Modelling and Data Gap Filling for a Green and Sustainable Environment covers the theory and practices of chemical informatics, focusing on modeling various properties and endpoints related to chemicals for improved chemical management and the design of safer chemicals to promote environmental sustainability. Across four sections, the book outlines modeling techniques such as quantitative structure–property relationship (QSPR), read-across, and machine learning for modeling environmental endpoints of chemicals. OECD guidelines are discussed and considered for model development and validation, documentation using the QSAR modeling reporting format (QMRF), and regulatory requirements for result presentation.
The book offers full datasets, algorithm information, and real-world case studies for all models, along with worked examples. It will serve as an essential resource for chemists and environmental scientists working in green and sustainable chemistry, but will be a great resource for students and academics at graduate level and above studying cheminformatics. This book will also be of interest to researchers developing new and sustainable chemicals and for decision-makers looking to make industrial processes more sustainable.
1. Section I: Introduction - p. S1 2. Chapter 1 Chemicals strategy for a sustainable environment - p. 3 3. Chapter 2 Modern modeling approaches for data gap filling - p. 47 4. Chapter 3 Aquatic toxicology: Computational approaches and innovations - p. 69 5. Section II: Quantitative structure–property relationship modeling of physicochemical properties and environmental fate of chemicals - p. S2 6. Chapter 4 Quantitative structure–property relationship modeling of physicochemical properties of environmentally relevant chemicals - p. 91 7. Chapter 5 OPERA QSPR models for environmentally relevant physicochemical properties - p. 133 8. Chapter 6 The prediction of hydrolysis and biodegradation of organophosphorus-based chemical warfare agents (Novichoks, G-series, and V-series) using in silico toxicology methods - p. 165 9. Chapter 7 Machine learning models as alternative methods to predict the bioconcentration factor - p. 191 10. Chapter 8 Quantitative structure–property relationship modeling of adsorption capacity of microplastics - p. 207 11. Chapter 9 Simulation of physicochemical and biochemical behavior of nanoparticles under various experimental conditions - p. 267 12. Chapter 10 Modeling of physicochemical properties of nanoparticles using QSPR analysis - p. 281 13. Chapter 11 QSPR modeling of physicochemical properties of nanoparticles - p. 325 14. Section III: Computational modeling of toxicity and ecotoxicity of chemicals - p. S3 15. Chapter 12 Computational modeling of acute toxicity of pharmaceuticals and related chemicals - p. 349 16. Chapter 13 Computational modeling of aquatic toxicity of nanoparticles - p. 385 17. Chapter 14 Computational modeling of acute and chronic toxicities of organic solvents - p. 425 18. Chapter 15 Computational modeling of acute and chronic toxicities of chemicals of emerging concern - p. 459 19. Chapter 16 Computational approaches in toxicity prediction: The role of quantitative structure–activity relationship (QSAR) in modern chemical risk assessment in the water ecosystems - p. 497 20. Chapter 17 Computational modeling of avian toxicities: Risk assessment of chemicals - p. 531 21. Chapter 18 Computational modeling of the genotoxicity and carcinogenicity of chemicals - p. 559 22. Chapter 19 Computational modeling of skin sensitization of chemicals - p. 589 23. Chapter 20 Emerging advances in modeling chemical mutagenicity and carcinogenicity - p. 607 24. Chapter 21 Computational modeling of genotoxic chemicals - p. 633 25. Section IV: Additional topics - p. S4 26. Chapter 22 Databases for chemical toxicity and ecotoxicity - p. 663 27. Chapter 23 Open-source modeling tools for chemical toxicity and ecotoxicity - p. 695 28. Chapter 24 Chemical language models for chemical toxicity and ecotoxicity prediction - p. 737 29. Chapter 25 Application of artificial intelligence/machine learning in modeling chemical toxicity and ecotoxicity - p. 761 30. Chapter 26 Multitask learning and transfer learning approaches in target-based chemical toxicity modeling: G-protein-coupled receptors as an example - p. 815 31. Chapter 27 In silico modeling of properties and toxicities of chemical mixtures - p. 837 32. Chapter 28 Chemical and physical properties databases - p. 865 33. Chapter 29 Advanced cheminformatics models for predicting PFAS potency and environmental impact in sustainable chemistry, powered by Enalos Cloud Platform - p. 893 34. Chapter 30 Applying partial ordering methodology to the study of environmental pollutants - p. 921 35. Chapter 31 Cheminformatics in life cycle assessment: Advancing solvent, toxicology, and chemical synthesis for sustainable innovation - p. 951 36. Chapter 32 The virtual extensive read-across (VERA) tool for read-across: A flexible approach - p. 975 37. Chapter 33 MetaQSAR: A comprehensive tool for automated QSAR modeling - p. 993 38. Chapter 34 ProtoPRED: a versatile, user-friendly platform for in silico predictions of physicochemical, eco(toxicological), and pharmacokinetic parameters in a regulatory context - p. 1021 39. Index - p. 1049
