In Silico Technologies in Drug Target Identification and Validation

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.

1101529602
In Silico Technologies in Drug Target Identification and Validation

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.

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In Silico Technologies in Drug Target Identification and Validation

In Silico Technologies in Drug Target Identification and Validation

In Silico Technologies in Drug Target Identification and Validation

In Silico Technologies in Drug Target Identification and Validation

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$290.00 

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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.


Product Details

ISBN-13: 9781040070604
Publisher: CRC Press
Publication date: 06/13/2006
Series: Drug Discovery Series
Sold by: Barnes & Noble
Format: eBook
Pages: 504
File size: 4 MB

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

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