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Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine.

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 7, 2003
PubMed
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Surflex, a flexible molecular docking algorithm, accurately predicts protein-ligand poses. It also excels as a screening tool, showing high true positive rates for drug discovery.

Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Molecular docking is crucial for identifying potential drug candidates.
  • Accurate prediction of protein-ligand interactions is essential for drug design.
  • Existing docking methods face challenges in speed and accuracy for flexible molecules.

Purpose of the Study:

  • To evaluate the reliability and accuracy of the Surflex docking algorithm.
  • To assess Surflex's performance as a screening tool for drug discovery.
  • To determine the computational efficiency of Surflex.

Main Methods:

  • Surflex employs a surface-based molecular similarity search engine.
  • It integrates the Hammerhead scoring function for pose generation.

Related Experiment Videos

  • Validation involved 81 diverse protein/ligand complexes with crystallographic data.

    • Main Results:

    • Surflex achieved top-ranked poses within 2.5 Å root-mean-square deviation (rmsd) for over 90% of complexes.
    • In screening simulations, Surflex demonstrated >80% true positive rate at <1% false positive rate.
    • Docking time scales linearly with the number of rotatable bonds, offering efficiency.

    Conclusions:

    • Surflex is a reliable and accurate flexible molecular docking tool.
    • Its superior performance makes it a valuable asset for virtual screening in drug discovery.
    • The algorithm offers a good balance of speed and accuracy for molecular modeling.