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Related Experiment Videos

Ligand-based structural hypotheses for virtual screening.

Ajay N Jain1

  • 1UCSF Cancer Research Institute and Comprehensive Cancer Center, University of California, San Francisco, California 94143-0128, USA. ajain@cc.ucsf.edu

Journal of Medicinal Chemistry
|February 6, 2004
PubMed
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This study introduces a novel computational method for drug discovery, generating accurate ligand conformations for structure-based design. The Surflex-Sim approach effectively identifies potential drug candidates, enhancing virtual screening efficiency.

Area of Science:

  • Computational Chemistry
  • Drug Discovery
  • Structural Biology

Background:

  • Many protein drug targets lack sufficient 3D structural data for traditional structure-based drug design.
  • Existing 3D Quantitative Structure-Activity Relationship (QSAR) methods rely on ligand conformation and alignment hypotheses.
  • Accurate prediction of molecular activity hinges on a reliable ligand-based binding site hypothesis.

Purpose of the Study:

  • To develop and assess an objective function for generating bioactive ligand conformations.
  • To evaluate the utility of the Surflex similarity module (Surflex-Sim) for identifying true ligands among decoys.
  • To demonstrate the method's applicability in rational drug design and high-throughput virtual screening.

Main Methods:

  • Utilized the Surflex docking system's molecular similarity function combined with molecular volume minimization.

Related Experiment Videos

  • Generated hypotheses of bioactive conformations for small molecules binding to target proteins.
  • Tested the Surflex-Sim module on ligands for serotonin, histamine, muscarinic, and GABA(A) receptors.

    • Main Results:

    • Surflex-Sim successfully differentiated true ligands from random compounds using models with only 2-3 known ligands.
    • Achieved true positive rates of 60% with false positive rates between 0-3%.
    • Demonstrated theoretical enrichment rates exceeding 150-fold compared to random screening.

    Conclusions:

    • The developed method effectively generates hypotheses of bioactive ligand conformations.
    • Surflex-Sim offers a practical and efficient approach for rational drug design and virtual screening.
    • The method provides competitive performance against many structure-based docking algorithms, especially when structural data is limited.