Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria.
- Riniker S Laboratory of Physical Chemistry, ETH Zürich, Zürich, Switzerland.
- Landrum GA T5 Informatics GmbH, Basel, Switzerland.
- Montanari F Pharmacoinformatics Research Group, Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria.
- Villalba SD IMP - Research Institute of Molecular Pathology, Vienna Biocenter, Vienna, Austria.
- Maier J Department of Chemical Biology & Therapeutics, St. Jude Children's Research Hospital, Memphis, TN, USA.
- Jansen JM Department of Global Discovery Chemistry, Novartis Institutes for BioMedical Research, Emeryville, CA, USA.
- Walters WP Relay Therapeutics, Cambridge, MA, USA.
- Shelat AA Department of Chemical Biology & Therapeutics, St. Jude Children's Research Hospital, Memphis, TN, USA.
- 2018-03-10
Published in:
- F1000Research. - 2017
English
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.
- Language
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- English
- Open access status
- gold
- Identifiers
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- DOI 10.12688/f1000research.11905.2
- PMID 28928948
- Persistent URL
- https://sonar.ch/global/documents/238165
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