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SeleX-CS: a new consensus scoring algorithm for hit discovery and lead optimization.

Shay Bar-Haim1, Ayelet Aharon, Tal Ben-Moshe

  • 1Epix Pharmaceuticals Ltd., 3 Hayetzira Street, Ramat Gan 52521, Israel. sbar-haim@epixpharma.com

Journal of Chemical Information and Modeling
|February 24, 2009
PubMed
Summary
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A new consensus scoring algorithm, SeleX-Consensus Scoring (SeleX-CS), improves drug discovery by optimizing ligand ranking for biological targets. This method enhances hit identification and aids in lead optimization, outperforming traditional scoring functions.

Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Structure-based virtual screening is crucial for identifying drug candidates.
  • Accurate scoring functions are essential but challenging for ranking ligand binding affinities.
  • Current consensus scoring methods have limitations, including parameter dependence and incomplete treatment of inactive compounds.

Purpose of the Study:

  • To develop a novel consensus scoring algorithm, SeleX-Consensus Scoring (SeleX-CS), to address limitations in virtual screening.
  • To improve the ranking of screening libraries and the identification of active compounds.
  • To enhance the efficiency of drug lead discovery and optimization.

Main Methods:

  • Developed SeleX-Consensus Scoring (SeleX-CS) with an optimized subset of scoring functions.

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  • Incorporated Monte Carlo/Simulated Annealing for subset optimization.
  • Implemented rank redundancy removal and explicit consideration of inactive compounds.

    • Main Results:

    • SeleX-CS achieved excellent enrichment factors for G-protein coupled receptors (GPCRs), outperforming single scores.
    • Achieved 41-fold enrichment for cannabinoid receptor 1 (CB1) and 72-fold for chemokine receptor type 2 (CCR2) at 1% screening library.
    • Demonstrated high success rates (67-73%) on external test sets and effective derichment of inactive compounds.

    Conclusions:

    • SeleX-CS is a powerful tool for ranking screening libraries in lead discovery.
    • The algorithm shows promise as a lead optimization tool, evidenced by strong correlation with experimental data for Kv4.3 potassium channel.
    • SeleX-CS offers a robust and improved approach to virtual screening and drug design.