Data Mining in Drug Discovery

Data Mining in Drug Discovery

Editor(s): Rémy D. Hoffmann, Arnaud Gohier, Pavel Pospisil

Published Online: 30 SEP 2013 09:45AM EST

Print ISBN: 9783527329847

Online ISBN: 9783527655984

DOI: 10.1002/9783527655984

About this Book

Written for drug developers rather than computer scientists, this monograph adopts a systematic approach to mining scientifi c data sources, covering all key steps in rational drug discovery, from compound screening to lead compound selection and personalized medicine. Clearly divided into four sections, the first part discusses the different data sources available, both commercial and non-commercial, while the next section looks at the role and value of data mining in drug discovery. The third part compares the most common applications and strategies for polypharmacology, where data mining can substantially enhance the research effort. The final section of the book is devoted to systems biology approaches for compound testing.
Throughout the book, industrial and academic drug discovery strategies are addressed, with contributors coming from both areas, enabling an informed decision on when and which data mining tools to use for one's own drug discovery project.

Table of contents

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  1. Part One: Data Sources

  2. Part Two: Analysis and Enrichment

    1. Chapter 6

      The Role of Data Mining in the Identification of Bioactive Compounds via High-Throughput Screening (pages 131–154)

      Kamal Azzaoui, John P. Priestle, Thibault Varin, Ansgar Schuffenhauer, Jeremy L. Jenkins, Florian Nigsch, Allen Cornett, Maxim Popov and Edgar Jacoby

  3. Part Three: Applications to Polypharmacology

  4. Part Four: System Biology Approaches

    1. Chapter 13

      Systems Biology Approaches for Compound Testing (pages 291–316)

      Alain Sewer, Julia Hoeng, Renée Deehan, Jurjen W. Westra, Florian Martin, Ty M. Thomson, David A. Drubin and Manuel C. Peitsch

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