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Related Experiment Videos

A novel subshape molecular descriptor.

Santosh Putta1, John Eksterowicz, Christian Lemmen

  • 1Deltagen Research Labs, 740 Bay Road, Redwood City, California 94063, USA. sputta@RationalDiscovery.com

Journal of Chemical Information and Computer Sciences
|September 23, 2003
PubMed
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This study introduces a novel computational method to compare molecular shapes, focusing on critical subshapes for drug discovery. This approach enhances the identification of potential drug leads by analyzing shape similarity more effectively.

Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Drug discovery

Background:

  • Similar molecular shapes often correlate with similar biological activity.
  • Current computational methods primarily assess overall molecular shape similarity.
  • Existing methods may overlook critical subshapes essential for molecular binding.

Purpose of the Study:

  • To develop a novel computational approach for comparing molecular shapes of varying sizes.
  • To accurately calculate subshape similarity for improved drug lead identification.
  • To address limitations of overall shape comparison in discovering biologically active molecules.

Main Methods:

  • Developed a skeletal shape representation independent of covalent connectivity.
  • Implemented a triangle-matching filter for rapid initial alignment.

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  • Utilized successive filtering stages including direction, feature, and shape matching.
  • Applied the approach to identify and evolve drug leads.

    • Main Results:

    • The skeletal representation simplifies rotational and translational sampling.
    • The triangle-matching filter efficiently eliminates improbable alignments.
    • The multi-stage filtering refines shape comparisons for accuracy.
    • Demonstrated successful application in lead discovery and evolution scenarios.

    Conclusions:

    • The new method enables effective comparison of molecular shapes across different sizes.
    • Subshape similarity analysis is crucial for identifying potent drug candidates.
    • This approach offers a significant advancement in computational drug discovery and lead optimization.