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Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures.

Jérôme Hert1, Peter Willett, David J Wilton

  • 1Krebs Institute for Biomolecular Research and Department of Information Studies, University of Sheffield, Western Bank, Sheffield S10 2TN, UK.

Journal of Chemical Information and Computer Sciences
|May 25, 2004
PubMed
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This study explores virtual screening using multiple reference structures. Data fusion of similarity rankings proved most effective for identifying bioactive compounds, outperforming merged fingerprints and substructural analysis.

Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Drug discovery

Background:

  • Fingerprint-based similarity searching is crucial for virtual screening with single reference structures.
  • Identifying bioactive compounds often requires considering multiple reference structures.

Purpose of the Study:

  • To review and evaluate methods for virtual screening using multiple bioactive reference structures.
  • To determine the most effective approach for similarity searching with multiple reference fingerprints.

Main Methods:

  • Reviewed three methods: merging fingerprints, data fusion of similarity rankings, and substructural analysis approximations.
  • Performed extended searches on the MDL Drug Data Report database.

Main Results:

Related Experiment Videos

  • Data fusion of similarity rankings emerged as the most effective general strategy.
  • The binary kernel discrimination technique yielded the best individual search results.

Conclusions:

  • Fusing similarity scores is a superior approach for multi-reference virtual screening.
  • Specific techniques like binary kernel discrimination enhance search performance.