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Efficient calculation of molecular properties from simulation using kernel molecular dynamics.

W Michael Brown1, Ariella Sasson, Donald R Bellew

  • 1Computational Biology, Sandia National Laboratories, Albuquerque, New Mexico 87185-1316, USA. wmbrown@sandia.gov

Journal of Chemical Information and Modeling
|August 5, 2008
PubMed
Summary
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This study introduces a novel computational method combining simulation and informatics to predict molecular properties. This approach overcomes simulation cost limitations and improves molecular description for accurate structure-property relationships.

Area of Science:

  • Computational Chemistry
  • Cheminformatics
  • Molecular Modeling

Background:

  • Understanding chemical structure-function relationships is crucial in chemistry and biology.
  • Traditional simulation methods are computationally expensive.
  • Informatics methods struggle with accurate and general molecular descriptions.

Purpose of the Study:

  • To develop a novel approach combining simulation and informatics for structure-property relationships.
  • To utilize supervised learning to address simulation sampling limitations.
  • To enable learning from molecular descriptions based on intermolecular force physics.

Main Methods:

  • Integration of simulation and informatics techniques.
  • Application of supervised learning to overcome simulation sampling challenges.

Related Experiment Videos

  • Development of molecular descriptors rooted in the physics of dynamic intermolecular forces.

    • Main Results:

    • Successful formulation of structure-property relationships using the novel approach.
    • Demonstrated overcoming of simulation sampling problems via supervised learning.
    • Application to corticosteroid binding, formylpeptide receptor ligand identification, and NF-kappaB inhibition.

    Conclusions:

    • The hybrid simulation-informatics approach offers a powerful tool for predicting molecular properties.
    • Supervised learning effectively enhances simulation-based predictions.
    • The method is applicable to diverse biological and chemical problems, including drug discovery.