Time-resolved evaluation of compound repositioning predictions on a text-mined knowledge network.

Journal: BMC bioinformatics
Published Date:

Abstract

BACKGROUND: Computational compound repositioning has the potential for identifying new uses for existing drugs, and new algorithms and data source aggregation strategies provide ever-improving results via in silico metrics. However, even with these advances, the number of compounds successfully repositioned via computational screening remains low. New strategies for algorithm evaluation that more accurately reflect the repositioning potential of a compound could provide a better target for future optimizations.

Authors

  • Michael Mayers
    The Scripps Research Institute, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA.
  • Tong Shu Li
    Department of Molecular and Experimental Medicine, the Scripps Research Institute, La Jolla, CA, USA.
  • NĂºria Queralt-Rosinach
    Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA.
  • Andrew I Su
    Department of Molecular and Experimental Medicine, the Scripps Research Institute, La Jolla, CA, USA.