Neural Networks in QSAR and Drug Design
Published on 9. August 1996
284 pages
978-0-08-053738-2 (ISBN)
Description
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
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James Devillers
Neural Networks in QSAR and Drug Design
Book
08/1996
Academic Press
€136.18
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Person
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).
Content
J. Devillers, Preface. J. Devillers, Strengths and Weaknesses of the Backpropagation Neural Network in QSAR and QSPR Studies. D. Domine, J. Devillers, and W. Karcher, AUTOLOGP Versus Neural Network Estimationof n-Octanol/Water Partition Coefficients. J. Devillers, D. Domine, and R.S. Boethling, Use of a Backpropagation Neural Network and Autocorrelation Descriptors for Predicting the Biodegradation of Organic Chemicals. M. Chastrette and C. ElAidi, Structure-Bell-Pepper Odor Relationships for Pyrazines and Pyridines. J. Devillers, C. Guillon, and D. Domine, A Neural Structure-Odor Threshold Model for Chemicals of Environmental and Industrial Concern. D. Wienke, D. Domine, L. Buydens, and J. Devillers, Adaptive Resonance Theory Based Neural Networks Explored for Pattern Recognition Analysis of QSAR Data. D.J. Livingstone, Multivariate Data Display Using Neural Networks. D.T. Manallack, T. Gallagher, and D.J. Livingstone, Quantitative Structure-Activity Relationships of Nicotinic Agonists. S. Anzali, G. Barnickel, M. Krug, J. Sadowski, M. Wagener, and J. Gasteiger, Evaluation of Molecular Surface Properties Using a Kohonen Neural Network. D. Domine, D. Wienke,J. Devillers, and L. Buydens, A New Nonlinear Neural Mapping Technique for Visual Exploration of QSAR Data. G.M. Maggiora, C.T. Zhang, K.C. Chou, and D.W. Elrod, Combining Fuzzy Clustering and Neural Networks to Predict Protein Structural Classes. Index.
