Comprehensive and impeccably edited, Neural Networks in QSAR and Drug Design is the first book to present an all-inclusive coverage of the topic. The book provides a practice-oriented introduction to the different neural network paradigms, allowing the reader to easily understand and reproduce the results demonstrated. Numerous examples are detailed, demonstrating a variety of applications to QSAR and drug design.
The contributors include some of the most distinguished names in the field, and the book provides an exhaustive bibliography, guiding readers to all the literature related to a particular type of application or neural network paradigm. The extensive index acts as a guide to the book, and makes retrieving information from chapters an easy task. A further research aid is a list of software with indications of availablility and price, as well as the editors scale rating the ease of use and interest/price ratio of each software package. The presentation of new, powerful tools for modeling molecular properties and the inclusion of many important neural network paradigms, coupled with extensive reference aids, makes Neural Networks in QSAR and Drug Design an essential reference source for those on the frontiers of this field.
* Presents the first coverage of neural networks in QSAR and Drug Design
* Allows easy understanding and reproduction of the results described within
* Includes an exhaustive bibliography with more than 200 references
* Provides a list of applicable software packages with availability and price
"synopsis" may belong to another edition of this title.
Here is a unique book that enables you to use neural networks in molecular modeling. Not just another book about neural networks, Neural Networks in QSAR and Drug Design offers:
A clear and simple description of the main
neural network paradigms
Access to more than 10,000 bibliographical
references on neural networks
Actual case studies showing how supervised and
unsupervised neural networks can be used to
solve real-world structure-activity problems
in pharmacology, toxicology, agrochemistry,
and environmental chemistry
A wealth of practical hints to use neural
networks in QSAR and drug design
Information on the strengths and weaknesses
of neural networks in the discipline
A useful companion volume also published in the series Principles of QSAR and Drug Design:
GENETIC ALGORITHMS IN MOLECULAR MODELING
Edited by J Devillers
352 pages and 4pp colour plate section,
James Devillers is currently a Professor in Ecology, Zoology, Ecotoxicology, and Phytopathology at an Agricultural School of Graduate Engineers (ISARA) in Lyon, France,since 1983. Devillers is also a Professor in Environmental Chemistry at a Chemical School of Graduate Engineers (ICPI), and a Senior Lecturer in QSAR and Ecotoxicology for the Centre of Environmental Sciences (Metz), Private Institutes, and the EEC. He is also the President of the Centre de Traitement de l'Information Scientifique, which is a private company specializing in QSAR studies, drug design, statistical analysis, and data validation. Devillers has published many articles, six books, and is a member of various societies and institutions including: The International QSAR Society, the European Group for the QSAR Studies, the International Neural Network Society, the Institute of Electrical and Electronics Engineers (IEEE), the American Chemical Society (ACS), the Society of Environmental Toxicology and Chemistry (SETAC), the Societe d'Ecotoxicologie Fondamentale et Appliquee (SEFA), and the Societe Linneenne de Lyon (SLL). He is Editor-in-Chief of two journals: SAR and QSAR in Environmental Research (Gordon and Breach Science Publishers), and Toxicology Modeling (Carfax Publishing Company); as well as a series of books called Handbooks of Ecotoxicological Data (Gordon and Breach Science Publishers). Devillers is also a member of the editorial board of three journals, these are: Ecological Modelling (Elsevier), Xenobiotica (Taylor & Francis), and Journal of Biological Systems (World Scientific).
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Book Description Academic Press, 1996. Hardcover. Book Condition: New. book. Bookseller Inventory # M0122138155
Book Description Elsevier. Book Condition: New. pp. 284. Bookseller Inventory # 7424495