Literature DB >> 12546953

Function first: a powerful approach to post-genomic drug discovery.

Stephen F Betz1, Susan M Baxter, Jacquelyn S Fetrow.   

Abstract

In the post-genomic era, pharmaceutical researchers must evaluate vast numbers of protein sequences and formulate novel, intelligent strategies for identifying valid targets and discovering leads against them. The identification of small molecules that selectively target proteins or protein families will be aided by knowing the function and/or the structure of the target(s). By identifying protein function first, efficiencies are gained that allow subsequent focus of resources on particular protein families of interest. This article reviews current proteomic-scale approaches to identifying function as a way of accelerating lead discovery.

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Year:  2002        PMID: 12546953     DOI: 10.1016/s1359-6446(02)02398-x

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  5 in total

1.  Computational approaches to protein-protein interaction.

Authors:  Giacomo Franzot; Oliviero Carugo
Journal:  J Struct Funct Genomics       Date:  2003

2.  FINDSITE-metal: integrating evolutionary information and machine learning for structure-based metal-binding site prediction at the proteome level.

Authors:  Michal Brylinski; Jeffrey Skolnick
Journal:  Proteins       Date:  2010-12-06

Review 3.  FINDSITE: a combined evolution/structure-based approach to protein function prediction.

Authors:  Jeffrey Skolnick; Michal Brylinski
Journal:  Brief Bioinform       Date:  2009-03-26       Impact factor: 11.622

4.  INTEGRATING COMPUTATIONAL PROTEIN FUNCTION PREDICTION INTO DRUG DISCOVERY INITIATIVES.

Authors:  Marianne A Grant
Journal:  Drug Dev Res       Date:  2011-02       Impact factor: 4.360

Review 5.  Pharmaceutical research in the Kingdom of Saudi Arabia: A scientometric analysis during 2001-2010.

Authors:  Ibrahim Alhaider; K K Mueen Ahmed; B M Gupta
Journal:  Saudi Pharm J       Date:  2013-08-20       Impact factor: 4.330

  5 in total

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