Genetic Algorithms in Molecular Modeling

About this book

Genetic Algorithms in Molecular Modeling is the first book available on the use of genetic algorithms in molecular design. This volume marks the beginning of an new series of books, "Principles in Qsar and Drug Design", which will be an indispensible reference for students and professionals involved in medicinal chemistry, pharmacology, (eco)toxicology, and agrochemistry. Each comprehensive chapter is written by a distinguished researcher in the field. Through its up to the minute content, extensive bibliography, and essential information on software availability, this book leads the reader from the theoretical aspects to the practical applications. It enables the uninitiated reader to apply genetic algorithms for modeling the biological activities and properties of chemicals, and provides the trained scientist with the most up to date information on the topic. It is extremely topical and timely. It sets the foundations for the development of computer-aided tools for solving numerous problems in QSAR and drug design. It is written to be accessible without prior direct experience in genetic algorithms.

Contents

Genetic Algorithms in Computer-Aided Molecular Design. An Overviewe of Genetic Methods. Genetic Algorithms in Feature Selection. Some Theory and Examples of Genetic Function Approximation with Comparision to Evolutionary Techniques. Genetic Partial Least Squares in QSAR. Application of Genetic Algorithms to the General QSAR Problem and to Guiding Molecular Diversity Experiments. Prediction of the Progesterone Receptor Binding of Steroids Using a Combination of Genetic Algorithms and Neural Networks. Genetically Evolved Receptor Models (GERM): A Procedure for Construction of Atomic-Level Receptor Site Models in the Absence of a Receptor Crystal Structure. Genetic Algorithms for Chemical Structure Handling and Molecular Recognition. Genetic Selection of Aromatic Substituents for Designing Test Series. Computer-Aided Molecular Design Using Nerual Networks and Genetic Algorithms. Designing Biodegradable Molecules from the Combined Use of a Backpropagation Neural Network and a Genetic Algorithm.

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Biography

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).