A Generative Deep Learning Approach for the Discovery of SARS CoV2 Protease Inhibitors

23 April 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

COVID19 has caused thousands of deaths worldwide within a few months. The rapid spread of this virus that causes COVID19, termed SARS CoV2, has been facilitated by the lack of effective vaccines and treatments against this virus. In recent months, our team has developed a novel deep learning platform, Rosalind, for drug design and optimisation, and it enables rapid in silico discovery and evaluation of novel chemical designs. In the current work, we applied Rosalind for the rapid discovery of SARS CoV2 replication inhibitors that target the virus main protease Mpro. Through a series of training and optimisation rounds based on reported SARS CoV2 Mpro inhibitors helped by docking into the recently reported crystal structures of SARS CoV2 Mpro and medicinal chemistry input, we identified the a series of promising SARS CoV2 Mpro inhibitors. These compounds are presented in this work so they scientific community could pursue them while we continue our deep learning-based work in a collaborative manner to identify lead SARS CoV2 Mpro compounds with excellent drug-like properties that could be developed in a timely manner to address the urgent need for new and effective COVID19 treatments.

Keywords

COVID19
SARS CoV2
Protease
Inhibitor
Deep Learning

Supplementary materials

Title
Description
Actions
Title
Shaker et al. Deep Learning SARS CoV2 Mpro SI F
Description
Actions

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.