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.
@article{arxiv.2004.00979,
title = {Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks},
author = {Markus Hofmarcher and Andreas Mayr and Elisabeth Rumetshofer and Peter Ruch and Philipp Renz and Johannes Schimunek and Philipp Seidl and Andreu Vall and Michael Widrich and Sepp Hochreiter and Günter Klambauer},
journal= {arXiv preprint arXiv:2004.00979},
year = {2020}
}
Comments
Additional results added. Various corrections to formulations and typos