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Over the past few years, research in the field of quantitative structure-activity relationships (QSAR) in chemistry, biology, pharmacology, toxicology, and environmental sciences has seen strong growth. New journals and books have appeared in each of these fields, however, the combination of QSAR and environmental sciences is still in its infancy. After the success of the Workshop on Quantitative Structure-Activity Relationships (QSAR) in Environmental Toxicology, held at McMaster University, Aug ust 16-18, 1983, with the proceedings publ ished in 1984 [QSA R in Environmental Toxicology, Kaiser, K. L. E. (Ed. ), D. Reidel Pub!. Co. , Dordrecht, 406 p. , ISBN 90-277 -1776-1], it was time to hold another workshop in 1986. Indeed, the response by the scientific community was excellent with over 50 participants from Canada, Germany, Great Britain, Hungary, India, Japan, The Netherlands, United States, and Yugoslavia. Moreover, both breadth and depth of papers given were significantly improved and the workshop discussions were intense and frank. Regrettably, the number of participants, number of papers given and submitted for these proceedings made it impossible to include the workshop discussions of these papers. However, several manuscripts were revised on the basis of these discussions and, therefore, do reflect this very interactive workshop. This volume presents the majority of papers given at the 2nd International Workshop on QSAR in Environmental Toxicology, held at McMaster University, June 9-13, 1986. Many of these papers contain primary, new scientific data, equations and results which will not appear elsewhere.
On cover: TemaNord Environment. Diskettes in plastic pockets inside back cover.
As the 21st century approaches, there is little doubt that the tools and resources are available to unlock all the secrets of Quantitative Structure-Activity Relationships (QSAR) in order to design more efficient drugs and safer chemicals. The comparison QSAR models provide are a key to reach a deep understanding of the foundation and a better optimisation of the use of these statistical tools. Seeking out the similarities and differences among QSAR Models allows the user to estimate their simulation performances, find chemo-taxonomical links, and uncover In vivo/In Vitro relationships. The purpose of this book is to highlight the multifaceted aspect of the term "comparative QSAR" by bringing together QSAR experts of various origins and allowing them to offer their views on this diverse subject.