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Computational chemogenomics approaches to systematic knowledge-based drug discovery.

Jordi Mestres1

  • 1Chemogenomics Laboratory, Research Unit on Biomedical Informatics, Institut Municipal d'Investigació Mèdica, Universitat Pompeu Fabra, Passeig Marítim de la Barceloneta 37-49, Barcelona 08003, Spain. jmestres@imim.es

Current Opinion in Drug Discovery & Development
|June 26, 2004
PubMed
Summary

Chemogenomics aims to find drugs for all targets by reusing knowledge. Integrative computational tools are key for this drug discovery paradigm, improving compound design and screening.

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Area of Science:

  • Computational chemistry and cheminformatics
  • Drug discovery and development
  • Systems biology and bioinformatics

Background:

  • Chemogenomics offers a new paradigm for drug discovery by identifying all potential drugs for all biological targets.
  • Efficiency in drug design and optimization is achieved through the acquisition and reuse of targeted knowledge.
  • Targeted knowledge integrates chemical and biological information, necessitating computational tools.

Purpose of the Study:

  • To review recent advancements in integrative computational approaches for chemogenomics.
  • To highlight methods for data annotation and knowledge generation in this field.
  • To discuss the application of these approaches in the systematic, knowledge-based design and screening of chemical libraries.

Main Methods:

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  • Review of literature on integrative computational approaches in chemogenomics.
  • Analysis of data annotation strategies for chemical and biological data.
  • Examination of knowledge generation techniques for drug discovery.

Main Results:

  • Significant progress has been made in developing computational tools that integrate chemical and biological data.
  • These tools facilitate systematic knowledge generation for improved compound design.
  • Advances enable more efficient screening of chemical libraries based on targeted knowledge.

Conclusions:

  • Integrative computational approaches are crucial for advancing chemogenomics.
  • Systematic knowledge generation through data annotation and integration enhances drug discovery efficiency.
  • The review underscores the potential of chemogenomics for future pharmaceutical research.