Genetic Algorithms in Molecular Modeling
eBook - PDF

Genetic Algorithms in Molecular Modeling

  1. 327 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

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 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

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Yes, you can access Genetic Algorithms in Molecular Modeling by James Devillers in PDF and/or ePUB format, as well as other popular books in Biological Sciences & Genetics & Genomics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Front Cover
  2. Genetic Algorithms in Molecular Modeling
  3. Copyrigh Page
  4. Contents
  5. Contributors
  6. Preface
  7. Chapter 1. Genetic Algorithms in Computer-Aided Molecular Design
  8. Chapter 2. An Overview of Genetic Methods
  9. Chapter 3. Genetic Algorithms in Feature Selection
  10. Chapter 4. Some Theory and Examples of Genetic Function Approximation with Comparison to Evolutionary Techniques
  11. Chapter 5. Genetic Partial Least Squares in QSAR
  12. Chapter 6. Application of Genetic Algorithms to the General QSAR Problem and to Guiding Molecular Diversity Experiments
  13. Chapter 7. Prediction of the Progesterone Receptor Binding of Steroids Using a Combination of Genetic Algorithms and Neural Networks
  14. 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
  15. Chapter 9. Genetic Algorithms for Chemical Structure Handling an d Molecular Recognition
  16. Chapter 10. Genetic Selection of Aromatic Substituents for Designing Test Series
  17. Chapter 11. Computer-Aided Molecular Design Using Neural Networks and Genetic Algorithms
  18. Chapter 12. Designing Biodegradable Molecules from the Combined Us e of a Backpropagation Neural Network and a Genetic Algorithm
  19. Annexe
  20. Index
  21. Color Plate Section