Cheminformatic Modeling and Data Gap Filling for a Green and Sustainable Environment

Erscheinungsjahr 2026
ISBN: 978-0-443-36474-7
Verlag: Elsevier Science

Buch, Englisch, 1104 Seiten, Format (B × H): 148 mm x 229 mm, Gewicht: 1610 g

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.

Autoren/Hrsg.

Roy, Kunal

Fachgebiete

• Naturwissenschaften Chemie Chemie Allgemein Chemometrik, Chemoinformatik
• Naturwissenschaften Chemie Analytische Chemie Umweltchemie, Lebensmittelchemie

Inhaltsverzeichnis

Section I: Introduction
1. Chemicals strategy for a sustainable environment
2. Modern modeling approaches for data gap filling
3. Aquatic toxicology: Computational approaches and innovations

Section II: QSPR modeling of physicochemical properties and environmental fate of chemicals
4. Quantitative structure–property relationship modeling of physicochemical properties of environmentally relevant chemicals
5. OPERA QSPR models for environmentally relevant physicochemical properties
6. The prediction of hydrolysis and biodegradation of organophosphorus-based chemical warfare agents (Novichoks, G-series, and V-series) using in silico toxicology methods
7. Machine learning models as alternative methods to predict the bioconcentration factor
8. Quantitative structure–property relationship modeling of adsorption capacity of microplastics
9. Simulation of physicochemical and biochemical behavior of nanoparticles under various experimental conditions
10. Modeling of physicochemical properties of nanoparticles using QSPR analysis
11. QSPR modeling of physicochemical properties of nanoparticles

Section III: Computational modeling of toxicity and ecotoxicity of chemicals
12. Computational modeling of acute toxicity of pharmaceuticals and related chemicals
13. Computational modeling of aquatic toxicity of nanoparticles
14. Computational modeling of acute and chronic toxicities of organic solvents
15. Computational modeling of acute and chronic toxicities of chemicals of emerging concern
16. Computational approaches in toxicity prediction: The role of QSAR in modern chemical risk assessment in the water ecosystems
17. Computational modeling of avian toxicities: Risk assessment of chemicals
18. Computational modeling of the genotoxicity and carcinogenicity of chemicals
19. Computational modeling of skin sensitization of chemicals
20. Recent advances in modeling chemical mutagenicity and carcinogenicity
21. Computational modeling of genotoxic chemicals

Section IV: Additional topics
22. Databases for chemical toxicity and ecotoxicity
23. Open-source modeling tools for chemical toxicity and ecotoxicity
24. Chemical language models for chemical toxicity and ecotoxicity prediction
25. Application of artificial intelligence/machine learning in modeling chemical toxicity and ecotoxicity
26. Multitask learning and transfer learning approaches in target-based chemical toxicity modeling: G-protein-coupled receptors as an example
27. In silico modeling of properties and toxicities of chemical mixtures
28. Chemical and physical properties databases
29. Advanced cheminformatics models for predicting PFAS potency and environmental impact in sustainable chemistry, powered by Enalos Cloud Platform
30. Applying partial ordering methodology to the study of environmental pollutants
31. Cheminformatics in life cycle assessment: Advancing solvent, toxicology, and chemical synthesis for sustainable innovation
32. The VERA tool for read-across: A flexible approach
33. MetaQSAR: A comprehensive tool for automated QSAR modeling
34. ProtoPRED: a versatile, user-friendly platform for in silico predictions of physicochemical, eco(toxicological), and pharmacokinetic parameters in a regulatory context

Über Autor(innen)

Roy, Kunal
Dr. Kunal Roy is Professor & Ex-Head in the Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India (https://sites.google.com/site/kunalroyindia). He has been a recipient of Commonwealth Academic Staff Fellowship (University of Manchester, 2007) and Marie Curie International Incoming Fellowship (University of Manchester, 2013) and a former visiting scientist of Istituto di Ricerche Farmacologiche "Mario Negri" IRCCS, Milano. Italy. The field of his research interest is Quantitative Structure-Activity Relationship (QSAR) and Molecular Modeling with application in Drug Design, Property Modeling and Predictive Ecotoxicology. Dr. Roy has published more than 450 research articles (ORCID: http://orcid.org/0000-0003-4486-8074) in refereed journals (current SCOPUS h index 57; total citations to date more than 17500). He has also coauthored three QSAR-related books (Academic Press and Springer), edited thirteen QSAR books (Springer, Academic Press, and IGI Global), and published twenty five book chapters. Dr. Roy is the Co-Editor-in-Chief of Molecular Diversity (Springer Nature) and an Associate Editor of Computational and Structural Biotechnology Journal (Elsevier). Dr. Roy serves on the Editorial Boards of several International Journals including (1) European Journal of Medicinal Chemistry (Elsevier); (2) Journal of Molecular Graphics and Modelling (Elsevier); (3) Chemical Biology and Drug Design (Wiley); (4) Expert Opinion on Drug Discovery (Informa). Apart from this, Prof. Roy is a regular reviewer for QSAR papers in different journals. Prof. Roy has been a participant in the EU funded projects nanoBRIDGES and IONTOX apart from several national Government funded projects (UGC, AICTE, CSIR, ICMR, DBT, DAE). Prof. Roy has recently been placed in the list of the World's Top 2% science-wide author database (whole career data) (World rank 52 in the subfield of Medicinal & Biomolecular Chemistry) (Ioannidis, John P.A. (2025), "August 2025 data-update for "Updated science-wide author databases of standardized citation indicators", Elsevier Data Repository, V8, link: http://doi.org/10.17632/btchxktzyw.8).