Cheminformatic Modeling and Data Gap Filling for a Green and Sustainable Environment
- 1st Edition - May 15, 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 modeling various proper… Read more
Description
Description
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.
Key features
Key features
- 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
Readership
Readership
Table of contents
Table of contents
1. Chemicals strategy for a sustainable environment
Bianca Maranescu, Adriana Popa, Nicoleta Plesu, Gheorghe Ilia, Aurelia Visa
2. Modern modeling approaches for data gap filling
Alexandre Borrel
3. Aquatic toxicology: Computational approaches and innovations
Adrian J. Green, Arpit Tandon, Ayse Oktay, Alexandre Borrel, Brian E. Howard
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
Shanti Gopal Patra, Chhanda Paul, Pratim Kumar Chattaraj
5. OPERA QSPR models for environmentally relevant physicochemical properties
José Teófilo Moreira‑Filho, Nicole Kleinstreuer, Kamel Mansouri
6. The prediction of hydrolysis and biodegradation of organophosphorus‑based chemical warfare agents (Novichoks, G‑series, and V‑series) using in silico toxicology methods
Kamil Jurowski, Maciej Noga
7. Machine learning models as alternative methods to predict the bioconcentration factor
Fabrizio Mastrolorito, Vincenzo Amenduni, Nicola Gambacorta, Valentina Belgiovine, Francesca Cutropia, Anna Rita Tondo, Maria Vittoria Togo, Daniela Trisciuzzi, Lydia Siragusa, Lorenzo Ronchi, Nicola Amoroso, Fulvio Ciriaco, Orazio Nicolotti
8. Quantitative structure–property relationship modeling of adsorption capacity of microplastics
Suat Vardar, Melek Türker Saçan
9. Simulation of physicochemical and biochemical behavior of nanoparticles under various experimental conditions
Alla P. Toropova, Andrey A. Toropov, Emilio Benfenati
10. Modeling of physicochemical properties of nanoparticles using QSPR analysis
Kabiruddin Khan, Agnieszka Gajewicz‑Skretna
11. QSPR modeling of physicochemical properties of nanoparticles
Mohammad Hossein Fatemi, Kimia Jafari
Section III: Computational modeling of toxicity and ecotoxicity of chemicals
12. Computational modeling of acute toxicity of pharmaceuticals and related chemicals
Guohui Sun
13. Computational modeling of aquatic toxicity of nanoparticles
Gulcin Tugcu, Natalja Fjodorova, Melek Türker Saçan
14. Computational modeling of acute and chronic toxicities of organic solvents
Laura Lomba, Estefania Zuriaga, Beatriz Giner
15. Computational modeling of acute and chronic toxicities of chemicals of emerging concern
Anshika Gupta, Tanya Jamal, Shweta Singh Chauhan, Anurag Singh, Siddegowda Gopalapura Shivanne Gowda, Ramakrishnan Parthasarathi
16. Computational approaches in toxicity prediction: The role of QSAR in modern chemical risk assessment in the water ecosystems
Mahdi Banaee
17. Computational modeling of avian toxicities: Risk assessment of chemicals
Shubha Das, Probir Kumar Ojha
18. Computational modeling of the genotoxicity and carcinogenicity of chemicals
Jakob Menz
19. Computational modeling of skin sensitization of chemicals
Ryung Kyung Lee, Baeckkyoung Sung
20. Recent advances in modeling chemical mutagenicity and carcinogenicity
Vijay Gombar, Alexander Sedykh, Alexandre Borrel
21. Computational modeling of genotoxic chemicals
Asmae Saih, Samir Chtita, Salsabil Hamdi, Rachid Saile, Lahcen Wakrim, Anass Kettani
Section IV: Additional topics
22. Databases for chemical toxicity and ecotoxicity
Ramona Curpan, Alina Bora
23. Open‑source modeling tools for chemical toxicity and ecotoxicity
Luminita Crisan, Alina Bora
24. Chemical language models for chemical toxicity and ecotoxicity prediction
Khalid Bouhedjar
25. Application of artificial intelligence/machine learning in modeling chemical toxicity and ecotoxicity
Gul Karaduman, Feyza Kelleci Çelik
26. Multitask learning and transfer learning approaches in target‑based chemical toxicity modeling: G‑protein‑coupled receptors as an example
Chun‑Wei Tung, Wei‑Cheng Huang
27. In silico modeling of properties and toxicities of chemical mixtures
Silvina Fioressi, Daniel E. Bacelo, Pablo R. Duchowicz
28. Chemical and physical properties databases
Shanmuga Priya Baskaran, Rahul Tiwari, Areejit Samal
29. Advanced cheminformatics models for predicting PFAS potency and environmental impact in sustainable chemistry, powered by Enalos Cloud Platform
Maria Antoniou, Dimitra‑Danai Varsou, Anastasios G. Papadiamantis, Dimitrios Zouraris, Konstantinos D. Papavasileiou, Georgia Melagraki, Antreas Afantitis
30. Applying partial ordering methodology to the study of environmental pollutants
Lars Carlsen
31. Cheminformatics in life cycle assessment: Advancing solvent, toxicology, and chemical synthesis for sustainable innovation
Chonny Herrera‑Acevedo, Renata P.B. Menezes, Luciana Scotti, Marcus Tullius Scotti
32. The VERA tool for read‑across: A flexible approach
Edoardo Luca Viganò, Erika Colombo, Emilio Benfenati
33. MetaQSAR: A comprehensive tool for automated QSAR modeling
Pravin Ambure, Eva Serrano‑Candelas, Jyotsna Bhat‑Ambure, Rafael Gozalbes
34. ProtoPRED: a versatile, user‑friendly platform for in silico predictions of physicochemical, eco(toxicological), and pharmacokinetic parameters in a regulatory context
E. Serrano‑Candelas, S. Moncho, J.L. Vallés‑Pardo, A. Ambit‑Álvarez, L.E. Carpio, J. Coto‑Palacios, A. Goya, Á. Llobet‑Mut, Enrique Llobet‑Serra, A. Luttenauer, A. Manganaro, R. Ortega‑Vallbona, S. Perera‑Del‑Rosario, D. Talavera‑Cortés, Tarazona‑Díez Palomino‑Schätzlein, R. Gozalbes
Product details
Product details
- Edition: 1
- Latest edition
- Published: May 15, 2026
- Language: English
About the editors
About the editors
KR
Kunal Roy
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).
AB
Arkaprava Banerjee
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).