DeepSynergy: predicting anti-cancer drug synergy with Deep Learning.

Journal: Bioinformatics (Oxford, England)
Published Date: May 1, 2018

Abstract

MOTIVATION: While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerged as a time- and cost-efficient way to prioritize combinations to test, based on recently available large-scale combination screening data. Recently, Deep Learning has had an impact in many research areas by achieving new state-of-the-art model performance. However, Deep Learning has not yet been applied to drug synergy prediction, which is the approach we present here, termed DeepSynergy. DeepSynergy uses chemical and genomic information as input information, a normalization strategy to account for input data heterogeneity, and conical layers to model drug synergies.

Authors

  • Kristina Preuer
    Institute of Bioinformatics, Johannes Kepler University, 4040 Linz, Austria.
  • Richard P I Lewis
    Department of Chemistry, Centre for Molecular Science Informatics, University of Cambridge, Cambridge CB2 1EW, UK.
  • Sepp Hochreiter
    Institute for Machine Learning Johannes Kepler University Linz Austria.
  • Andreas Bender
    Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK ab454@cam.ac.uk.
  • Krishna C Bulusu
    Department of Chemistry, Centre for Molecular Science Informatics, University of Cambridge, Cambridge CB2 1EW, UK.
  • Günter Klambauer
    ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, A-4040 Linz, Austria.

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