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, this 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. This book offers full datasets, algorithm information, and real-world case studies for all models, along with worked examples.

This book will serve as an essential resource for chemists and environmental scientists working in green and sustainable chemistry, as well as 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 to decision-makers looking to make industrial processes more sustainable.