As in many fields of scientific endeavor, computational toxicology represents a broad and expanding group of activities. This chapter attempts to summarize ongoing efforts for a number of computational approaches and suggest ways in which these methods could be applied effectively for improving risk assessment practice going forward in time. Generic issues include QA/QC of data used for computational modeling, graduate education programs for training the next generation of computational modelers with a common language among themselves, and the training in translation of computational toxicology terms for scientists in other related fields and the lay public so that effective communication of modeling data is achieved. Communication with scientists involved in systems biology approaches will be of particular importance. In this regard, it will also be essential to integrate artificial intelligence (AI) programs into future risk assessment programs for the evolution of this field in order to more fully integrate systems biology into mode of action risk analysis. Expanded use of data mining programs for development of testable hypotheses and to facilitate the incorporation of “green chemistry” approaches will reduce the number of chemicals in need of post-manufacture toxicology testing and risk assessment. In summary, it is hoped that the key elements identified in this chapter will help this field to continue to develop in a robust manner and provide the risk assessment community with a much needed set of modern scientific tools.
A key resource for toxicologists across a broad spectrum of fields, this book offers a comprehensive analysis of molecular modelling approaches and strategies applied to risk assessment for pharmaceutical and environmental chemicals.
This book is authored by leading international investigators who have real-world experience in relating computational toxicology methods to risk assessment.
This book provides a comprehensive review of both traditional and cutting-edge methodologies that are currently used in computational toxicology and specifically features its application in regulatory decision making.
The book offers a complete systems perspective to risk assessment prediction, discussing experimental and computational approaches in detail, with: An introduction to toxicology methods and an explanation of computational methods In-depth ...
Final chapters include case studies and additionally discuss interactions between phytochemicals and Western therapeutics. This book will be useful for scientists involved in environmental research and risk assessment.
Opening with an introduction to the field of computational toxicology and its current and potential applications, the volume continues with ’best practices’ in mathematical and computational modeling, followed by chemoinformatics and ...
Systems toxicology approaches are also introduced. The volume closes with primers and background on some of the key mathematical and statistical methods covered earlier, as well as a list of other resources.
This book is a collection of machine learning and deep learning algorithms, methods, architectures, and software tools that have been developed and widely applied in predictive toxicology.
This volume explores techniques that are currently used to understand solid target-specific models in computational toxicology.
Opening with an introduction to the field of computational toxicology and its current and potential applications, the volume continues with ’best practices’ in mathematical and computational modeling, followed by chemoinformatics and ...