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

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