Genetic Algorithms in Molecular Modeling ( Principles of QSAR and Drug Design )

Publication series ：Principles of QSAR and Drug Design

Author： Devillers   James  

Publisher： Elsevier Science‎

Publication year： 1996

E-ISBN: 9780080532387

P-ISBN(Paperback): 9780122138102

P-ISBN(Hardback):  9780122138102

Subject： Q3 Genetics

Language： ENG

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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.

  • Extremely topical and timely
  • Sets the foundations for the development of computer-aided tools for solving numerous problems in QSAR and drug design
  • Written to be accessible without prior direct experience in genetic algorithms

Chapter

Front Cover

pp.：  1 – 4

Genetic Algorithms in Molecular Modeling

pp.：  4 – 5

Copyrigh Page

pp.：  5 – 6

Contents

pp.：  6 – 10

Contributors

pp.：  10 – 12

Preface

pp.：  12 – 14

Chapter 1. Genetic Algorithms in Computer-Aided Molecular Design

pp.：  14 – 48

Chapter 2. An Overview of Genetic Methods

pp.：  48 – 80

Chapter 3. Genetic Algorithms in Feature Selection

pp.：  80 – 100

Chapter 4. Some Theory and Examples of Genetic Function Approximation with Comparison to Evolutionary Techniques

pp.：  100 – 122

Chapter 5. Genetic Partial Least Squares in QSAR

pp.：  122 – 144

Chapter 6. Application of Genetic Algorithms to the General QSAR Problem and to Guiding Molecular Diversity Experiments

pp.：  144 – 172

Chapter 7. Prediction of the Progesterone Receptor Binding of Steroids Using a Combination of Genetic Algorithms and Neural Networks

pp.：  172 – 206

Chapter 8. Genetically Evolved Receptor Models (GERM): A Procedure for Construction of Atomic-Level Receptor Site Models in the Absence of a Receptor Crystal Structure

pp.：  206 – 224

Chapter 9. Genetic Algorithms for Chemical Structure Handling an d Molecular Recognition

pp.：  224 – 256

Chapter 10. Genetic Selection of Aromatic Substituents for Designing Test Series

pp.：  256 – 284

Chapter 11. Computer-Aided Molecular Design Using Neural Networks and Genetic Algorithms

pp.：  284 – 316

Chapter 12. Designing Biodegradable Molecules from the Combined Us e of a Backpropagation Neural Network and a Genetic Algorithm

pp.：  316 – 328

Annexe

pp.：  328 – 338

Index

pp.：  338 – 342

Color Plate Section

pp.：  342 – 346

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