Large-Scale Ligand-Based Virtual Screening for SARS-CoV-2 Inhibitors Using Deep Neural Networks

7 Pages Posted: 26 Mar 2020 Last revised: 3 Apr 2020

See all articles by Markus Hofmarcher

Markus Hofmarcher

Johannes Kepler University Linz - ELLIS Unit at the LIT AI Lab

Andreas Mayr

Johannes Kepler University Linz - ELLIS Unit at the LIT AI Lab

Elisabeth Rumetshofer

Johannes Kepler University Linz - ELLIS Unit at the LIT AI Lab

Peter Ruch

Johannes Kepler University Linz - ELLIS Unit at the LIT AI Lab

Philipp Renz

Johannes Kepler University Linz - ELLIS Unit at the LIT AI Lab

Johannes Schimunek

Johannes Kepler University Linz - ELLIS Unit at the LIT AI Lab

Philipp Seidl

Johannes Kepler University Linz - ELLIS Unit at the LIT AI Lab

Andreu Vall

Johannes Kepler University Linz - ELLIS Unit at the LIT AI Lab

Michael Widrich

Johannes Kepler University Linz - ELLIS Unit at the LIT AI Lab

Sepp Hochreiter

Johannes Kepler University Linz - ELLIS Unit at the LIT AI Lab

Günter Klambauer

Johannes Kepler University Linz - ELLIS Unit at the LIT AI Lab

Date Written: March 23, 2020

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. We provide these top-ranked compounds as a library for further screening with bioassays at https://github.com/ml-jku/sars-cov-inhibitors-chemai.

Note: Funding: Institute for Machine Learning (JKU).

Conflict of Interest: The authors declare no competing interest.

Keywords: Artificial intelligence, neural networks, deep learning, QSAR, virtual screening, SARS-CoV, Corona, SARS-CoV-2, CoV inhibitors, Coronavirus

Suggested Citation

Hofmarcher, Markus and Mayr, Andreas and Rumetshofer, Elisabeth and Ruch, Peter and Renz, Philipp and Schimunek, Johannes and Seidl, Philipp and Vall, Andreu and Widrich, Michael and Hochreiter, Sepp and Klambauer, Günter, Large-Scale Ligand-Based Virtual Screening for SARS-CoV-2 Inhibitors Using Deep Neural Networks (March 23, 2020). Available at SSRN: https://ssrn.com/abstract=3561442 or http://dx.doi.org/10.2139/ssrn.3561442

Markus Hofmarcher

Johannes Kepler University Linz - ELLIS Unit at the LIT AI Lab ( email )

Austria

Andreas Mayr

Johannes Kepler University Linz - ELLIS Unit at the LIT AI Lab ( email )

Austria

Elisabeth Rumetshofer

Johannes Kepler University Linz - ELLIS Unit at the LIT AI Lab ( email )

Austria

Peter Ruch

Johannes Kepler University Linz - ELLIS Unit at the LIT AI Lab ( email )

Austria

Philipp Renz

Johannes Kepler University Linz - ELLIS Unit at the LIT AI Lab ( email )

Austria

Johannes Schimunek

Johannes Kepler University Linz - ELLIS Unit at the LIT AI Lab ( email )

Austria

Philipp Seidl

Johannes Kepler University Linz - ELLIS Unit at the LIT AI Lab ( email )

Austria

Andreu Vall

Johannes Kepler University Linz - ELLIS Unit at the LIT AI Lab ( email )

Austria

Michael Widrich (Contact Author)

Johannes Kepler University Linz - ELLIS Unit at the LIT AI Lab ( email )

Austria

Sepp Hochreiter

Johannes Kepler University Linz - ELLIS Unit at the LIT AI Lab ( email )

Austria

Günter Klambauer

Johannes Kepler University Linz - ELLIS Unit at the LIT AI Lab ( email )

Austria

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