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Supervised scoring models with docked ligand conformations for structure-based virtual screening.

Reiji Teramoto1, Hiroaki Fukunishi

  • 1Fundamental and Environmental Research Laboratories, NEC Corporation, 34, Miyukigaoka, Tsukuba, Ibaraki 305-8501, Japan. r-teramoto@bq.jp.nec.com

Journal of Chemical Information and Modeling
|August 10, 2007
PubMed
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A new supervised scoring model (SSM) improves protein-ligand docking by optimizing scoring functions for specific target proteins. This method enhances virtual screening enrichment, particularly for top-ranked compounds, aiding drug discovery.

Area of Science:

  • Computational Chemistry
  • Drug Discovery
  • Bioinformatics

Background:

  • Protein-ligand docking is crucial for discovering novel drug candidates.
  • Current scoring functions often lack target-specific optimization, limiting their effectiveness.
  • Improved scoring methods are needed for accurate prediction of binding affinities and virtual screening enrichment.

Purpose of the Study:

  • To develop a supervised scoring model (SSM) for optimizing scoring functions in protein-ligand docking.
  • To enhance the enrichment of virtual screening by tailoring scoring functions to specific target proteins.
  • To improve the prediction of binding energy and facilitate practical drug screening.

Main Methods:

  • Developed the supervised scoring model (SSM) utilizing supervised learning for scoring function optimization.

Related Experiment Videos

  • Incorporated the protein-ligand binding process and docked ligand conformations into the model.
  • Applied SSM to the FlexX scoring function (F-Score) against five target proteins: TK, ER, AChE, PDE5, and PPARgamma.
  • Utilized a linear correlation between binding free energy and root mean square deviation of native ligands for energy prediction.

    • Main Results:

    • SSM consistently enhanced enrichment compared to the standard F-Score across all five tested proteins.
    • Superior performance was particularly noted for thymidine kinase (TK), acetylcholine esterase (AChE), and peroxisome proliferator-activated receptor gamma (PPARgamma).
    • SSM demonstrated significant improvement in enriching top-ranked compounds, proving beneficial for practical drug screening applications.

    Conclusions:

    • The supervised scoring model (SSM) offers a robust approach to optimize scoring functions for structure-based virtual screening.
    • SSM enhances the efficiency and accuracy of drug discovery by improving ligand enrichment in virtual screening.
    • This target-specific optimization strategy holds significant promise for accelerating the identification of novel therapeutic agents.