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Related Experiment Videos

Combinatorial informatics in the post-genomics ERA.

Dimitris K Agrafiotis1, Victor S Lobanov, F Raymond Salemme

  • 13-Dimensional Pharmaceuticals, Inc., 665 Stockton Drive, Exton, Pennsylvania 19341, USA. agrafiotis@3dp.com

Nature Reviews. Drug Discovery
|July 18, 2002
PubMed
Summary
This summary is machine-generated.

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Chemogenomics offers an industrialized approach to drug discovery by creating small molecules for biological probing and drug optimization. Success relies on integrated cheminformatics tools for rapid, large-scale compound optimization.

Area of Science:

  • Drug Discovery
  • Genomics
  • Cheminformatics

Background:

  • Genome sequencing reveals numerous potential drug targets, necessitating novel drug discovery methods.
  • Traditional drug discovery is often slow and resource-intensive.
  • A parallel, industrialized approach is needed to accelerate the identification of viable drug candidates.

Purpose of the Study:

  • To introduce a chemogenomics strategy for efficient drug discovery.
  • To highlight the importance of integrated cheminformatics tools in this process.
  • To outline the requirements for computational and data-mining tools for large-scale application.

Main Methods:

  • Employing a chemogenomics strategy for small-molecule compound generation.
  • Utilizing integrated cheminformatics applications for rapid optimization.

Related Experiment Videos

  • Leveraging 'just-in-time' combinatorial chemical synthesis.
  • Developing new computational and data-mining tools for library generation and compound selection.

    • Main Results:

    • The chemogenomics strategy provides a highly parallel and industrialized solution for drug discovery.
    • Integrated cheminformatics enables rapid and directed optimization of compounds with drug-like properties.
    • Effective implementation requires scalable computational tools for all stages of the process.

    Conclusions:

    • Chemogenomics, supported by advanced cheminformatics, is a promising strategy for accelerating drug discovery.
    • The development of robust computational and data-mining tools is crucial for large-scale implementation.
    • This approach facilitates the efficient identification and optimization of small-molecule drug leads.