Literature DB >> 15763566

Illuminating drug discovery with biological pathways.

Gordana Apic1, Tijana Ignjatovic, Scott Boyer, Robert B Russell.   

Abstract

Systems biology promises to impact significantly on the drug discovery process. One of its ultimate goals is to provide an understanding of the complete set of molecular mechanisms describing an organism. Although this goal is a long way off, many useful insights can already come from currently available information and technology. One of the biggest challenges in drug discovery today is the high attrition rate: many promising candidates prove ineffective or toxic owing to a poor understanding of the molecular mechanisms of biological systems they target. A "systems" approach can help identify pathways related to a disease and can suggest secondary effects of drugs that might cause these problems and thus ultimately improve the drug discovery pipeline.

Mesh:

Year:  2005        PMID: 15763566     DOI: 10.1016/j.febslet.2005.02.023

Source DB:  PubMed          Journal:  FEBS Lett        ISSN: 0014-5793            Impact factor:   4.124


  24 in total

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2.  A computational framework for the topological analysis and targeted disruption of signal transduction networks.

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Review 3.  Mechanisms of drug combinations: interaction and network perspectives.

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Review 6.  Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review.

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7.  Kinase inhibition-related adverse events predicted from in vitro kinome and clinical trial data.

Authors:  Xinan Yang; Yong Huang; Matthew Crowson; Jianrong Li; Michael L Maitland; Yves A Lussier
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Review 8.  Mechanistic systems biology of inflammatory gene expression in airway smooth muscle as tool for asthma drug development.

Authors:  Chi-Ming Hai
Journal:  Curr Drug Discov Technol       Date:  2008-12

9.  Inferring branching pathways in genome-scale metabolic networks.

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Journal:  BMC Syst Biol       Date:  2009-10-29

10.  Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model.

Authors:  Keren Yizhak; Tomer Benyamini; Wolfram Liebermeister; Eytan Ruppin; Tomer Shlomi
Journal:  Bioinformatics       Date:  2010-06-15       Impact factor: 6.937

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