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Related Experiment Videos

Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures

P S Charifson1, J J Corkery, M A Murcko

  • 1Vertex Pharmaceuticals, 130 Waverly Street, Cambridge, Massachusetts 02139-4242, USA. paulc@vpharm.com

Journal of Medicinal Chemistry
|December 22, 1999
PubMed
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Consensus scoring, combining multiple computational methods, significantly improves the identification of active enzyme inhibitors. This approach enhances accuracy and reduces false positives in drug discovery.

Area of Science:

  • Computational chemistry
  • Drug discovery
  • Biochemistry

Background:

  • Identifying effective enzyme inhibitors is crucial for drug development.
  • Individual computational scoring functions have limitations in accurately predicting inhibitor activity.
  • Enzyme targets include p38 MAP kinase, inosine monophosphate dehydrogenase, and HIV protease.

Purpose of the Study:

  • To evaluate the effectiveness of combining multiple scoring functions using a consensus approach.
  • To assess the ability of consensus scoring to discriminate between active and inactive enzyme inhibitors.
  • To analyze the performance of different docking methods and scoring functions.

Main Methods:

  • Extensive computational study involving docking 3D structures.

Related Experiment Videos

  • Utilized two docking methods and thirteen scoring functions.
  • Employed an intersection-based consensus approach for scoring function combination.
  • Main Results:

    • Consensus scoring significantly enhanced the discrimination between active and inactive enzyme inhibitors.
    • The study identified specific scoring functions that perform well individually and in combination.
    • Consensus scoring dramatically reduced false positives compared to individual scoring functions.

    Conclusions:

    • Combining scoring functions via consensus approach is a powerful strategy for improving hit-rates in drug discovery.
    • This method offers a more reliable way to identify potential drug candidates.
    • The findings provide valuable insights for optimizing computational drug screening protocols.