Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria.

Journal: F1000Research
Published Date: Jul 17, 2017

Abstract

The first challenge in the 2014 competition launched by the Teach-Discover-Treat (TDT) initiative asked for the development of a tutorial for ligand-based virtual screening, based on data from a primary phenotypic high-throughput screen (HTS) against malaria. The resulting Workflows were applied to select compounds from a commercial database, and a subset of those were purchased and tested experimentally for anti-malaria activity. Here, we present the two most successful Workflows, both using machine-learning approaches, and report the results for the 114 compounds tested in the follow-up screen. Excluding the two known anti-malarials quinidine and amodiaquine and 31 compounds already present in the primary HTS, a high hit rate of 57% was found.

Authors

  • Sereina Riniker
    Laboratory of Physical Chemistry, ETH Zürich, Zürich, Switzerland.
  • Gregory A Landrum
    T5 Informatics GmbH, Basel, Switzerland.
  • Floriane Montanari
    Department of Bioinformatics , Bayer AG , Berlin , Germany . Email: robin.winter@bayer.com.
  • Santiago D Villalba
    IMP - Research Institute of Molecular Pathology, Vienna Biocenter, Vienna, Austria.
  • Julie Maier
    Department of Chemical Biology & Therapeutics, St. Jude Children's Research Hospital, Memphis, TN, USA.
  • Johanna M Jansen
    Department of Global Discovery Chemistry, Novartis Institutes for BioMedical Research, Emeryville, CA, USA.
  • W Patrick Walters
    Relay Therapeutics, Cambridge, MA, USA.
  • Anang A Shelat
    Department of Chemical Biology & Therapeutics, St. Jude Children's Research Hospital, Memphis, TN, USA.

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