These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
2 files

Data-Driven Drug Repurposing for COVID-19

submitted on 16.07.2020 and posted on 16.07.2020 by Raghvendra Mall, Abdurrahman Elbasir, Hossam Al Meer, Sanjay Chawla, Ehsan Ullah
Motivation: A global effort is underway to identify drugs for the treatment of COVID-19. Since de novo drug design is an extremely long, time-consuming, and expensive process, efforts are underway to discover existing drugs that can be
repurposed for COVID-19.

Model: We propose a machine learning representation framework that uses deep learning induced vector embeddings of drugs and viral proteins as features to predict drug-viral protein activity. The prediction model in-turn is used to build an ensemble framework to rank approved drugs based on their ability to inhibit the three main proteases (enzymes) of the SARS-COV-2 virus.

Results: We identify a ranked list of 19 drugs as potential targets including 7 antivirals, 6 anticancer, 3 antibiotics, 2 antimalarial, and 1 antifungal. Several drugs, such as Remdesivir, Lopinavir, Ritonavir, and Hydroxychloroquine, in our ranked list, are currently in clinical trials. Moreover, through molecular docking simulations, we demonstrate that majority of the anticancer and antibiotic drugs in our ranked list have low binding energies and thus high binding affinity with the 3CL-pro protease of SARS-COV-2 virus.

Disclaimer: Our models are computational and the drugs suggested should not be taken for treating COVID-19 without a doctor's advice, as further wet-lab research and clinical trials are essential to elucidate their efficacy for this purpose.


No funding application


Email Address of Submitting Author


Qatar Computing Research Institute



ORCID For Submitting Author


Declaration of Conflict of Interest

No conflict of interest

Version Notes

This is version 1.0


Logo branding