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Predicting Potential SARS-COV-2 Drugs - In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking

preprint
submitted on 23.05.2020 and posted on 26.05.2020 by Nischal Komal Karki, Niraj Verma, Francesco Trozzi, Peng Tao, Elfi Kraka, Brian Zoltowski
Severe Acute Respiratory Syndrome Corona Virus 2 has altered life on a global scale. Currently, research labs around the world are looking for new pharmaceutical treatments by repurposing existing drugs, identifying potential antibody-based therapeutics, as well as the design of new pharmaceutical products and vaccines. To be able to rapidly identify potentional new treatments we require global cooperation and an enhanced open-access research model to distribute new ideas and leads. Herein, we employ a combined machine learning and drug docking approach to evaluate the potential efficacy of existing FDA and World approved drugs to impact the ACE2-Spike complex necessary for viral entry and replication. Further, we extend the machine learning approach to databases containing between 700,000-1 billion compounds. The results of large library screens are incorporated into a open-access web interface to allow researchers from diverse fields to target molecules of interest. Our combined approach allows for both the identification of existing drugs that may be able to be repurposed, and de novo design of ACE2-regulatory compounds. Through these efforts we identify intriguing links between COVID-19 pathologies, particularly in regard to possible sex-differences in disease outcomes.

History

Email Address of Submitting Author

nirajverma288@gmail.com

Institution

Southern Methodist University

Country

United States of America

ORCID For Submitting Author

0000-0003-4762-944X

Declaration of Conflict of Interest

No conflict of interest

Version Notes

First Version

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