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

Cheminformatic Modelling and Data Gap Filling for a Green and Sustainable Environment covers the theory and practices of chemical informatics, focusing on modelling various properties and endpoints related to chemicals for improved chemical management and the design of safer chemicals to promote environmental sustainability. Across four sections, this book outlines modelling techniques such as quantitative structure-property relationship (QSPR), read-across, and machine learning for modelling environmental endpoints of chemicals. OECD guidelines are discussed and considered for model development and validation, documentation using QSAR modelling reporting format (QMRF), and regulatory requirements for result presentation. This book offers full datasets, algorithm information and real-world case studies for all models and worked examples. This book will serve as an essential resource for chemists and environmental scientists working in green and sustainable chemistry, as well as students and academics at graduate level and above studying cheminformatics. This book will also be of interests for researchers working on developing new and sustainable chemicals and decision makers looking to make industrial processes more sustainable.

List of contents

Section I: Introduction
1. Chemicals Strategy for a Sustainable Environment
2. Modern Modelling Approaches for Data Gap Filling
3. Aquatic Toxicology: Computational Approaches and Innovations

Section II: QSPR Modelling of Physicochemical Properties and Environmental Fate of Chemicals
4. QSPR Modelling of Physicochemical Properties of Environmentally Relevant Chemicals
5. OPERA QSPR Models for Environmentally Relevant Physicochemical Properties
6. 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 for Predicting Bioconcentration Factors
8. QSPR Modelling of Adsorption Capacity of Microplastics
9. Simulation of Physicochemical and Biochemical Behaviour of Nanoparticles Under Various Experimental Conditions
10. Modelling of Physicochemical Properties of Nanoparticles Using QSPR Analysis
11. Chemometric Modelling of Physicochemical Properties of Nanoparticles

Section III: Computational Modelling of Toxicity and Ecotoxicity of Chemicals
12. Computational Modelling of Acute Toxicity of Pharmaceuticals and Related Chemicals
13. Computational Modelling of Acute Toxicity of Nanoparticles
14. Computational Modelling of Acute and Chronic Toxicities of Organic Solvents
15. Computational Modelling 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 for Water Ecosystems
17. Computational Modelling of Avian Toxicities: Risk Assessment of Chemicals
18. Computational Modelling of Genotoxicity and Carcinogenicity of Chemicals
19. Computational Modelling of Skin Sensitisation of Chemicals
20. Recent Advances in Modelling Chemical Mutagenicity and Carcinogenicity
21. Computational Modelling of Genotoxic Chemicals

Section IV: Additional Topics
22. Databases for Chemical Toxicity and Ecotoxicity
23. Open-Source Modelling Tools for Chemical Toxicity and Ecotoxicity
24. Chemical Language Models for Chemical Toxicity and Ecotoxicity Prediction
25. Application of AI/ML in Modelling Chemical Toxicity and Ecotoxicity
26. Multitask Learning and Transfer Learning Approaches in Target-Based Chemical Toxicity Modelling: GPCRs as an Example
27. In silico Modelling of Properties and Toxicities of Chemical Mixtures
28. Databases for Chemical and Physical Properties
29. Advanced Cheminformatics Models for Predicting PFAS Potency and Environmental Impact in Sustainable Chemistry, Powered by the 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: A Flexible Approach
33. MetaQSAR: A Comprehensive Tool for Automated QSAR Modelling
34. ProtoPRED, a Versatile, User-Friendly Platform for In Silico Predictions of Physicochemical, Eco(toxicological) and Pharmacokinetic Parameters in a Regulatory Context

About the author

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).
Arkaprava Banerjee is a Researcher (funded by the Life Sciences Research Board, DRDO, Govt. of India) working at the Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata. Mr. Banerjee has twenty-nine research articles published in reputed journals and four book chapters with overall citations of 763 and an h-index of 17 (Scopus). His ORCID identifier is 0000-0001-8468-0784, His expertise lies in the similarity-based cheminformatic approaches like Read-Across and Read-Across Structure-Activity Relationship (RASAR) – a novel method that combines the concept of QSAR and Read-Across. Mr. Banerjee is also a Java programmer, who has developed various cheminformatic tools based on QSAR, Read-Across, and RASAR, and the tools are freely available from the DTC Laboratory Supplementary Website. Together with Prof. Kunal Roy, he has been one of the first researchers to develop quantitative models using similarity and error-based descriptors (quantitative/classification Read-Across Structure-Activity Relationship: q-RASAR/c-RASAR models) with applications in drug design, materials science, and property modeling. Recently, he coauthored a book on “q-RASAR,” which was published by Springer. He has also co-edited three volumes of “Materials Informatics” published by Springer. He has recently been placed in the list of the World's Top 2% science-wide author database (Single-year data 2024) (World rank 769 in the subfield of Toxicology) (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).

Product details

• Assisted by: Arkaprava Banerjee (Editor), Roy Kunal (Editor)
• Publisher: Elsevier
• Languages: English
• Product format: Paperback / Softback
• Release: 01.05.2026
• EAN: 9780443364747
• ISBN: 978-0-443-36474-7
• Weight: 450 g
• Series: Advances in Green and Sustainable Chemistry
• Subjects: Natural sciences, medicine, IT, technology > Chemistry > Physical chemistry; SCIENCE / Chemistry / General, SCIENCE / Chemistry / Analytic, SCIENCE / Chemistry / Physical & Theoretical, The environment, chemistry, Science / Environmental Science, Environmental Chemistry, Quantum & theoretical chemistry, Analytical Chemistry, Computational Chemistry, SCIENCE / Chemistry / Environmental