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LOCAL KERNEL CANONICAL CORRELATION ANALYSIS WITH APPLICATION TO VIRTUAL DRUG SCREENING.

Daniel Samarov1, J S Marron, Yufeng Liu

  • 1National Institute of Standards and Technology, University of North Carolina, University of North Carolina, University of North Carolina and University of North Carolina.

The Annals of Applied Statistics
|November 29, 2011
PubMed
Summary
This summary is machine-generated.

Computational drug discovery uses virtual screening to filter millions of compounds. We introduce Indefinite Kernel Canonical Correlation Analysis (IKCCA) for improved accuracy in identifying biologically active compounds.

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Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Machine learning

Background:

  • Drug discovery involves screening vast compound libraries, often millions of molecules.
  • Experimental testing of all compounds is time-consuming and expensive.
  • Computational methods, like virtual screening, are crucial for reducing the search space.

Purpose of the Study:

  • To propose novel computational approaches for virtual screening.
  • To enhance the predictive accuracy of identifying biologically active compounds.
  • To introduce Indefinite Kernel Canonical Correlation Analysis (IKCCA) for virtual screening.

Main Methods:

  • Canonical Correlation Analysis (CCA) and its kernel-based extensions.
  • Development and application of Indefinite Kernel CCA (IKCCA).
  • Evaluation using both a toy problem and real-world drug discovery data.

Main Results:

  • IKCCA demonstrates strong performance in virtual screening tasks.
  • Significant improvements in predictive accuracy compared to existing methods.
  • Successful application to real-world drug discovery datasets.

Conclusions:

  • IKCCA offers a powerful and accurate method for computational drug discovery.
  • The proposed approach effectively reduces the search space for potential drug candidates.
  • Novel spectral learning-based methods enhance virtual screening efficiency and success rates.