Cheminformatic Modelling and Data Gap Filling for a Green and Sustainable Environment
- 1st Edition - May 1, 2026
- Latest edition
- Editors: Kunal Roy, Arkaprava Banerjee
- Language: English
Cheminformatic Modelling and Data Gap Filling for a Green and Sustainable Environment covers the theory and practices of chemical informatics, focusing on modelling various… Read more
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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.
• Includes multiple algorithms for QSPR models and machine learning methods for modeling environmental endpoints
• Discusses crucial emerging topics for sustainable chemistry, such as mixture property modeling, microplastic toxicity modeling, natural language models for toxicity and ecotoxicity prediction
• Provides framework for modelling of both chemo-physical properties, Environmental thresholds, and acute and chronic toxicity endpoints
• Includes over twenty real-world case studies, including datasets for environmental endpoints, with examples of model development and methodology
• Discusses crucial emerging topics for sustainable chemistry, such as mixture property modeling, microplastic toxicity modeling, natural language models for toxicity and ecotoxicity prediction
• Provides framework for modelling of both chemo-physical properties, Environmental thresholds, and acute and chronic toxicity endpoints
• Includes over twenty real-world case studies, including datasets for environmental endpoints, with examples of model development and methodology
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.
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
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
- Edition: 1
- Latest edition
- Published: May 1, 2026
- Language: English
KR
Kunal Roy
Dr. Kunal Roy is a Professor and Ex-Head in the Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India. He has been a recipient of Commonwealth Academic Staff Fellowship (University of Manchester, 2007) and Marie Curie International Incoming Fellowship (University of Manchester, 2013). The field of his research interest is QSAR and Molecular Modeling with application in Drug Design and Ecotoxicological Modeling. Dr. Roy has published more than 350 research articles in refereed journals (current SCOPUS h index 49). He has also coauthored two QSAR-related books, edited six QSAR books and published more than ten book chapters. Dr. Roy is a Co-Editor-in-Chief of Molecular Diversity (Springer Nature). He also serves as a member of the Editorial Boards of several International Journals.
Affiliations and expertise
Professor, Drug Theoretics and Cheminformatics Lab, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, IndiaAB
Arkaprava Banerjee
Mr. 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 fifteen research articles published in reputed journals and one book chapter with overall citations of 206 and an h-index of 9 (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. He received the Prof. Anupam Sengupta Bronze medal from Jadavpur University for securing the highest marks in Pharmaceutical Chemistry in the MPharm Examination. He has also received a special diploma awarded by the Institute of Biomedical Chemistry, Moscow, Russia, and the ASCCT Travel Award from the American Society for Cellular and Computation Toxicology. 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 has coauthored one book on “q-RASAR” published by Springer.
Affiliations and expertise
Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology