Machine-Learning Driven Drug Repurposing for SARS-CoV-2

Konuk Yazar

Machine-Learning Driven Drug Repurposing for SARS-CoV-2

We developed artificial intelligence to identify antiviral compounds that merit further study as possible pharmaceutical treatments for COVID-19.

Contributing authors:

Semih Cantürk, Aman Singh, Patrick St-Amant, Jason Behrmann, PhD and fellow colleagues of Zetane Systems

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All results here are preliminary and have yet to undergo external peer-review. The findings in this article should not be used to guide clinical decision-making, nor do these findings identify a definitive treatment for COVID-19.

Our work aims to discover the underlying associations between amino acid sequences of viral proteins and antiviral agents that are effective against them using the artificial intelligence technology of artificial neural networks (ANN). We then use the patterns uncovered by our ANN to identify potential antiviral agents that may be effective against comparable amino acid sequences found in SARS-CoV-2, the virus at the centre of the worldwide COVID-19 pandemic. We used public data sources to make a dataset that pairs amino acid sequences with antivirals known to associate with defined viral amino acid sequences. This dataset served to train long short-term memory networks (LSTM) and convolutional neural networks (CNN). Preliminary results from our AI model produce outputs of possible safe-in-human drug candidates for treating SARS-CoV-2, and thus merit further investigation. Our preliminary results suggest Brincidofovir, Tilorone, Rapamycin, Artesunate, Cidofovir, Valacyclovir, Lopinavir and Ritonavir are of notable interest given that some of these results complement recent findings from noteworthy clinical studies, such as the “Triple combination of interferon beta-1b, lopinavir–ritonavir, and ribavirin in the treatment of patients admitted to hospital with COVID-19: an open-label, randomized, Phase II trial”, recently published in The Lancet.