Genetic algorithms in molecular modeling
Author(s)
Bibliographic Information
Genetic algorithms in molecular modeling
（Principles of QSAR and drug design, 1）
Academic Press, c1996
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Description and Table of Contents
Description
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 ew 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.
Table of 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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