In Silico Technologies in Drug Target Identification and Validation

by Darryl Leon (Editor), Scott Markel (Editor)

Overview

The pharmaceutical industry relies on numerous well-designed experiments involving high-throughput techniques and in silico approaches to analyze potential drug targets. These in silico methods are often predictive, yielding faster and less expensive analyses than traditional in vivo or in vitro procedures.

In Silico Technologies in Drug Target Identification and Validation addresses the challenge of testing a growing number of new potential targets and reviews currently available in silico approaches for identifying and validating these targets. The book emphasizes computational tools, public and commercial databases, mathematical methods, and software for interpreting complex experimental data. The book describes how these tools are used to visualize a target structure, identify binding sites, and predict behavior. World-renowned researchers cover many topics not typically found in most informatics books, including functional annotation, siRNA design, pathways, text mining, ontologies, systems biology, database management, data pipelining, and pharmacogenomics.

Covering issues that range from prescreening target selection to genetic modeling and valuable data integration, In Silico Technologies in Drug Target Identification and Validation is a self-contained and practical guide to the various computational tools that can accelerate the identification and validation stages of drug target discovery and determine the biological functionality of potential targets more effectively.

Daniel E. Levy, editor of the Drug Discovery Series, is the founder of DEL BioPharma, a consulting service for drug discovery programs. He also maintains a blog that explores organic chemistry.

Product Details

ISBN-13:	9781574444780
Publisher:	Taylor & Francis
Publication date:	06/13/2006
Series:	Drug Discovery Series , #6
Edition description:	New Edition
Pages:	508
Product dimensions:	6.12(w) x 9.19(h) x (d)

About the Author

Darryl Leon, Scott Markel

Table of Contents

Foreword. Preface. Introduction.TARGET IDENTIFICATION: Pattern Matching. Tools for Computational Protein Annotation and Functional Assignment. The Impact of Genetic Variation on Drug Discovery and Development. Mining of Gene Expression Data. TARGET VALIDATION: Text Mining. Pathways and Networks. Molecular Interactions: Learning from Protein Complexes. In Silico siRNA Design. Predicting Protein Subcellular Localization Using Intelligent Systems. Three-Dimensional Structures in Target Discovery and Validation. RECENT TRENDS:Comparative Genomics. Pharmacogenomics. Target Identification and Validation Using Human Simulation Models. Using Protein Targets for In Silico Structure-Based Drug Discovery.  COMPUTATIONAL INFRASTRUCTURE: Database Management. BioIT Hardware Configuration. BioIT Architecture: Software Architecture for Bioinformatics Research; Workflows and Data Pipelines. Ontologies. Index

Editorial Reviews

The more chemists know about how biologists identify and validate drug targets, the more they can help ensure a successful project out come. The first 300 pages of this volume are dedicated to just these issues, reviewed from a practicing biologist’s perspective but described completely enough to provide useful insight for the medicinal chemist and pointing to online databases and tools that enterprising chemists might try using themselves. ... The remainder of the book covers some interesting emerging technologies, as well as computational infrastructure issues. ...
“...The book is wide-ranging and yet practical in its review of in silico technologies, and the medicinal chemist will pick up useful background information in important target identification and validation techniques. The authors are clearly knowledgeable about their fields, and the editors have done an excellent job of melding this multi-authored book into a cohesive whole. ...”
— Peter Gund, IBM Federal Healthcare Practice, Germantown, Maryland, USA (in the Journal of Medicinal Chemistry, 2007, Vol. 30, No. 9)

From the Publisher