eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates.

Journal: BMC pharmacology & toxicology
Published Date: Jan 8, 2019

Abstract

BACKGROUND: The efficiency of drug development defined as a number of successfully launched new pharmaceuticals normalized by financial investments has significantly declined. Nonetheless, recent advances in high-throughput experimental techniques and computational modeling promise reductions in the costs and development times required to bring new drugs to market. The prediction of toxicity of drug candidates is one of the important components of modern drug discovery.

Authors

  • Limeng Pu
    Division of Electrical & Computer Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.
  • Misagh Naderi
    Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, 70803, USA.
  • Tairan Liu
    Department of Mechanical Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.
  • Hsiao-Chun Wu
    Division of Electrical & Computer Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.
  • Supratik Mukhopadhyay
    Department of Environmental Sciences, Center for Computation & Technology, Coastal Studies Institute, Louisiana State University, Baton Rouge, LA, United States.
  • Michal Brylinski
    Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, United States.

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