Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors
using deep neural networks
- URL: http://arxiv.org/abs/2004.00979v3
- Date: Mon, 17 Aug 2020 15:58:09 GMT
- Title: Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors
using deep neural networks
- Authors: Markus Hofmarcher, Andreas Mayr, Elisabeth Rumetshofer, Peter Ruch,
Philipp Renz, Johannes Schimunek, Philipp Seidl, Andreu Vall, Michael
Widrich, Sepp Hochreiter, G\"unter Klambauer
- Abstract summary: "ChemAI" is a deep neural network trained on more than 220M data points across 3.6M molecules.
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.
- Score: 5.50889410189681
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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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