Cheminformatic Modeling and Data Gap Filling for a Green and Sustainable Environment
Published on 5. June 2026
350 pages
978-0-443-36475-4 (ISBN)
Description
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.
- Presents multiple algorithms for QSPR models and machine learning methods for modeling environmental endpoints
- Discusses crucial emerging topics in sustainable chemistry, such as mixture property modeling, microplastic toxicity modeling, and natural language models for toxicity and ecotoxicity prediction
- Provides a comprehensive framework for modeling physicochemical properties, environmental thresholds, and acute and chronic toxicity endpoints
- Includes more than 20 real-world case studies, featuring datasets for environmental endpoints, with examples of model development and methodology
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Additional editions
Kunal Roy | Arkaprava Banerjee
Cheminformatic Modeling and Data Gap Filling for a Green and Sustainable Environment
Book
05/2026
Elsevier
€186.50
Shipment within 15-20 days
Content
Section I: Introduction1. Chemicals strategy for a sustainable environment2. Modern modeling approaches for data gap filling3. Aquatic toxicology: Computational approaches and innovationsSection II: QSPR modeling of physicochemical properties and environmental fate of chemicals4. Quantitative structure-property relationship modeling of physicochemical properties of environmentally relevant chemicals5. OPERA QSPR models for environmentally relevant physicochemical properties6. The prediction of hydrolysis and biodegradation of organophosphorus-based chemical warfare agents (Novichoks, G-series, and V-series) using in silico toxicology methods7. Machine learning models as alternative methods to predict the bioconcentration factor8. Quantitative structure-property relationship modeling of adsorption capacity of microplastics9. Simulation of physicochemical and biochemical behavior of nanoparticles under various experimental conditions10. Modeling of physicochemical properties of nanoparticles using QSPR analysis11. QSPR modeling of physicochemical properties of nanoparticlesSection III: Computational modeling of toxicity and ecotoxicity of chemicals12. Computational modeling of acute toxicity of pharmaceuticals and related chemicals13. Computational modeling of aquatic toxicity of nanoparticles14. Computational modeling of acute and chronic toxicities of organic solvents15. Computational modeling of acute and chronic toxicities of chemicals of emerging concern16. Computational approaches in toxicity prediction: The role of QSAR in modern chemical risk assessment in the water ecosystems17. Computational modeling of avian toxicities: Risk assessment of chemicals18. Computational modeling of the genotoxicity and carcinogenicity of chemicals19. Computational modeling of skin sensitization of chemicals20. Recent advances in modeling chemical mutagenicity and carcinogenicity21. Computational modeling of genotoxic chemicalsSection IV: Additional topics22. Databases for chemical toxicity and ecotoxicity23. Open-source modeling tools for chemical toxicity and ecotoxicity24. Chemical language models for chemical toxicity and ecotoxicity prediction25. Application of artificial intelligence/machine learning in modeling chemical toxicity and ecotoxicity26. Multitask learning and transfer learning approaches in target-based chemical toxicity modeling: G-protein-coupled receptors as an example27. In silico modeling of properties and toxicities of chemical mixtures28. Chemical and physical properties databases29. Advanced cheminformatics models for predicting PFAS potency and environmental impact in sustainable chemistry, powered by Enalos Cloud Platform30. Applying partial ordering methodology to the study of environmental pollutants31. Cheminformatics in life cycle assessment: Advancing solvent, toxicology, and chemical synthesis for sustainable innovation32. The VERA tool for read-across: A flexible approach33. MetaQSAR: A comprehensive tool for automated QSAR modeling34. ProtoPRED: a versatile, user-friendly platform for in silico predictions of physicochemical, eco(toxicological), and pharmacokinetic parameters in a regulatory context
