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

Nyhet

Häftad, Engelska, 2026

2 469 kr

Kommande

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

Produktinformation

Utgivningsdatum2026-05-01
Mått152 x 229 x undefined mm
Vikt450 g
FormatHäftad
SpråkEngelska
SerieAdvances in Green and Sustainable Chemistry
Antal sidor350
FörlagElsevier Science
ISBN9780443364747

Tillhör följande kategorier

Kemi inom Naturvetenskap och teknik
Analytisk kemi inom Naturvetenskap och teknik
Fysikalisk kemi inom Naturvetenskap och teknik

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. 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.

Section I: Introduction1. Chemicals Strategy for a Sustainable Environment2. Modern Modelling Approaches for Data Gap Filling3. Aquatic Toxicology: Computational Approaches and InnovationsSection II: QSPR Modelling of Physicochemical Properties and Environmental Fate of Chemicals4. QSPR Modelling of Physicochemical Properties of Environmentally Relevant Chemicals5. OPERA QSPR Models for Environmentally Relevant Physicochemical Properties6. 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 for Predicting Bioconcentration Factors8. QSPR Modelling of Adsorption Capacity of Microplastics9. Simulation of Physicochemical and Biochemical Behaviour of Nanoparticles Under Various Experimental Conditions10. Modelling of Physicochemical Properties of Nanoparticles Using QSPR Analysis11. Chemometric Modelling of Physicochemical Properties of NanoparticlesSection III: Computational Modelling of Toxicity and Ecotoxicity of Chemicals12. Computational Modelling of Acute Toxicity of Pharmaceuticals and Related Chemicals13. Computational Modelling of Acute Toxicity of Nanoparticles14. Computational Modelling of Acute and Chronic Toxicities of Organic Solvents15. Computational Modelling of Acute and Chronic Toxicities of Chemicals of Emerging Concern16. Computational Approaches in Toxicity Prediction: The Role of QSAR in Modern Chemical Risk Assessment for Water Ecosystems17. Computational Modelling of Avian Toxicities: Risk Assessment of Chemicals18. Computational Modelling of Genotoxicity and Carcinogenicity of Chemicals19. Computational Modelling of Skin Sensitisation of Chemicals20. Recent Advances in Modelling Chemical Mutagenicity and Carcinogenicity21. Computational Modelling of Genotoxic ChemicalsSection IV: Additional Topics22. Databases for Chemical Toxicity and Ecotoxicity23. Open-Source Modelling Tools for Chemical Toxicity and Ecotoxicity24. Chemical Language Models for Chemical Toxicity and Ecotoxicity Prediction25. Application of AI/ML in Modelling Chemical Toxicity and Ecotoxicity26. Multitask Learning and Transfer Learning Approaches in Target-Based Chemical Toxicity Modelling: GPCRs as an Example27. In silico Modelling of Properties and Toxicities of Chemical Mixtures28. Databases for Chemical and Physical Properties29. Advanced Cheminformatics Models for Predicting PFAS Potency and Environmental Impact in Sustainable Chemistry, Powered by the 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: A Flexible Approach33. MetaQSAR: A Comprehensive Tool for Automated QSAR Modelling34. ProtoPRED, a Versatile, User-Friendly Platform for In Silico Predictions of Physicochemical, Eco(toxicological) and Pharmacokinetic Parameters in a Regulatory Context