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

preprint
submitted on 22.04.2020 and posted on 23.04.2020 by Noor Shaker, Mohamed Abou-Zleikha, Mubarak AlAmri, Youcef Mehellou
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.

History

Email Address of Submitting Author

MehellouY1@cardiff.ac.uk

Institution

Cardiff University

Country

United Kingdom

ORCID For Submitting Author

0000-0001-5720-8513

Declaration of Conflict of Interest

No conflict of interest.

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