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Related Experiment Videos

Consensus scoring with feature selection for structure-based virtual screening.

Reiji Teramoto1, Hiroaki Fukunishi

  • 1Bio-IT Center, NEC Corporation, 34, Miyukigaoka, Tsukuba, Ibaraki 305-8501, Japan. r-teramoto@bq.jp.nec.com

Journal of Chemical Information and Modeling
|January 31, 2008
PubMed
Summary
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Feature selection-based consensus scoring (FSCS) improves drug screening by selecting complementary scoring functions. This method enhances ligand enrichment and outperforms existing techniques, even with limited active compound data.

Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Accurate ligand conformation evaluation is vital for structure-based virtual screening.
  • Consensus scoring (CS) enhances screening but selecting optimal scoring functions is challenging with limited active compounds.

Purpose of the Study:

  • To introduce feature selection-based consensus scoring (FSCS) for improved virtual screening.
  • To address the challenge of selecting complementary scoring functions when few active compounds are known.

Main Methods:

  • FSCS utilizes supervised feature selection with docked native ligand conformations to identify complementary scoring functions.
  • Evaluated five scoring functions, FSCS, and rank-by-rank consensus scoring (RCS) against four target proteins: AChE, thrombin, PDE5, and PPARgamma.

Related Experiment Videos

Main Results:

  • FSCS successfully selected complementary scoring functions, enhancing ligand enrichment.
  • FSCS outperformed RCS and individual scoring functions across all tested targets.
  • Single scoring function performance varied significantly depending on the target protein.
  • FSCS demonstrated effectiveness even with only one known protein-ligand complex structure.

Conclusions:

  • FSCS is a robust method for improving virtual screening enrichment by selecting complementary scoring functions.
  • The study highlights the target-specific nature of individual scoring functions.
  • Feature selection can predict effective scoring functions prior to docking, offering practical advantages for drug screening.