Since the publication of Computational Toxicology: Risk Assessment for Pharmaceutical and Environmental Chemicals in 2007 a lot has happened both in the career of the editor and in science in general. For one, my focus has expanded towards many computational applications to drug discovery rather than solely focused on ADME/Tox. I have also garnered new collaborators some of whom have very graciously agreed to contribute to this volume. Science is changing. Publishing may be adjusting slowly too. This book will likely be read as much on mobile devices or computers as in physical hard copies. Computational toxicology has also evolved in the past decade with the dramatic increase in public data availability. There have also been a number of more collaborative projects in Europe around toxicology (e.g. e-Tox and OpenTox), in addition we have seen a growth in open computational tools and model sharing (QSAR toolbox, Chembench, CDD, Bioclipse etc.). Groups like the EPA have developed and expanded ToxCast which represents a valuable resource for toxicology modeling. We are now therefore in the age of truly Big Data compared with a decade ago and there have been several efforts to combine different types of data for toxicology. To round this off, the growth in nanotechnology has seen the emergence of computational nanotoxicology which would not have been predicted my earlier book.

This book is therefore aimed at this next generation of computational toxicology scientist, comprehensively discussing the state-of-the-art of currently available molecular-modelling tools and the role of these in testing strategies for different types of toxicity. The overall role of these computational approaches in addressing environmental and occupational toxicity is also covered. These chapters before you aim to describe topics in an accessible manner especially for those who are not experts in the field. My goal with this book was to not cover too much of the same ground as the earlier book because much of what we published then is still generally valid, but to make the book focused on newer topics. I hope this book also serves to introduce some of the younger scientists from around the world who will likely drive this next generation of computational toxicology for many years to come. Finally, I hope this book inspires scientists to pursue computational toxicology so that it continues to expand across different industries from pharmaceutical to consumer products and its importance increases, as it has over the past decade.

November 12, 2017

Sean Ekins
Fuquay Varina, NC, USA