Deep learning identifies synergistic drug combinations for treating COVID-19 Academic Article uri icon

  •  
  • Overview
  •  
  • Research
  •  
  • Identity
  •  
  • Additional Document Info
  •  
  • View All
  •  

abstract

  • Significance COVID-19 has caused more than 2.5 million deaths worldwide. It is imperative that we develop therapies that can mitigate the effect of the disease. While searching for individual drugs for this purpose has been met with difficulties, synergistic drug combinations offer a promising alternative. However, the lack of high-quality training data pertaining to drug combinations makes it challenging to use existing machine learning methods for effective novel combination prediction tasks. Our proposed approach addresses this challenge by leveraging additional readily available data, such as drug‚ąítarget interactions, thus enabling an effective in silico search for synergistic combinations against SARS-CoV-2.

authors

  • Jin, Wengong
  • Stokes, Jonathan
  • Eastman, Richard T
  • Itkin, Zina
  • Zakharov, Alexey V
  • Collins, James J
  • Jaakkola, Tommi S
  • Barzilay, Regina

publication date

  • September 28, 2021