Selected article for: "acute respiratory syndrome and additionally allow"
Author: Hofmarcher, Markus; Mayr, Andreas; Rumetshofer, Elisabeth; Ruch, Peter; Renz, Philipp; Schimunek, Johannes; Seidl, Philipp; Vall, Andreu; Widrich, Michael; Hochreiter, Sepp; Klambauer, Gunter
Title: Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks
  • Cord-id: 0b77duc8
  • Document date: 2020_3_25
    • ID: 0b77duc8
    Snippet: Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs. We conducted a large-scale virtual screening for small molecules that are potential CoV-2 inhibitors. To this end, we utilized"ChemAI", a deep neural network trained on more than 220M data points across 3.6M molecules from three public drug-discovery databases. With ChemAI, we screened and ranked one billion molecules from the ZINC database for favourab
    Document: Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs. We conducted a large-scale virtual screening for small molecules that are potential CoV-2 inhibitors. To this end, we utilized"ChemAI", a deep neural network trained on more than 220M data points across 3.6M molecules from three public drug-discovery databases. With ChemAI, we screened and ranked one billion molecules from the ZINC database for favourable effects against CoV-2. We then reduced the result to the 30,000 top-ranked compounds, which are readily accessible and purchasable via the ZINC database. Additionally, we screened the DrugBank using ChemAI to allow for drug repurposing, which would be a fast way towards a therapy. We provide these top-ranked compounds of ZINC and DrugBank as a library for further screening with bioassays at https://github.com/ml-jku/sars-cov-inhibitors-chemai.

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