Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria [version 2; referees: 3 approved]
Sereina Riniker,
Gregory A. Landrum,
Floriane Montanari,
Santiago D. Villalba,
Julie Maier,
Johanna M. Jansen,
W. Patrick Walters,
Anang A. Shelat
Affiliations
Sereina Riniker
Laboratory of Physical Chemistry, ETH Zürich, Zürich, Switzerland
Gregory A. Landrum
T5 Informatics GmbH, Basel, Switzerland
Floriane Montanari
Pharmacoinformatics Research Group, Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria
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
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.
