COVID-19 - Exploration of potential therapeutics against underexplored targets.

  • Funded by UK Research and Innovation (UKRI)
  • Total publications:5 publications

Grant number: EP/V010948/1

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Key facts

  • Disease

  • Start & end year

  • Known Financial Commitments (USD)

  • Funder

    UK Research and Innovation (UKRI)
  • Principle Investigator

  • Research Location

    United Kingdom, Europe
  • Lead Research Institution

    University of Oxford
  • Research Category

    Therapeutics research, development and implementation

  • Research Subcategory

    Pre-clinical studies

  • Special Interest Tags


  • Study Subject


  • Clinical Trial Details


  • Broad Policy Alignment


  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable


The genome of SARS CoV-2 encodes proteins which perform various functions essential for the replication of the virus. By exploiting our knowledge of the 3D structures of these proteins we can identify and/or design small molecules (i.e. drugs) that bind to viral proteins to prevent them from performing their normal function. COVID-19 research groups worldwide been determining 3D structures of proteins encoded within the viral genome. The focus has been on high-profile target proteins of Cov-2, including the protease, spike protein and helicases. Perhaps surprisingly, less effort is being directed towards other promising targets for which there is structural information. We focus on two underexplored proteins, NSP9 (involved in RNA processing) and E protein (a viroporin). NSP9 helps the virus to replicate its genome. By identifying a compound that binds to NSP9, we would have a potential drug to halt viral replication in infected cells. E protein is a viroporin, forming channels in infected cell and viral membranes. Molecules which 'plug' the channel ("channel blockers") are potential anti-viral drugs. For target proteins, we will combine advanced molecular simulations in Oxford with AI-driven identification of potential compounds by IBM to enable and accelerate identification of compounds which could be repurposed as candidate anti-viral drugs. The IBM generative AI method has already been successful in identifying new antimicrobials that have since been experimentally validated. The work here will be undertaken as part of a long-standing collaboration between Oxford and IBM and the strong relationship will ensure delivery of this highly collaborative effort.

Publicationslinked via Europe PMC

Last Updated:39 minutes ago

View all publications at Europe PMC

Mg2+-dependent conformational equilibria in CorA and an integrated view on transport regulation.

Water Nanoconfined in a Hydrophobic Pore: Molecular Dynamics Simulations of Transmembrane Protein 175 and the Influence of Water Models.

Influence of water models on water movement through AQP1.

Effect of Water Models on Transmembrane Self-Assembled Cyclic Peptide Nanotubes.

Molecular Simulations of Hydrophobic Gating of Pentameric Ligand Gated Ion Channels: Insights into Water and Ions.