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

Posted by Zetane Systems on May 22, 2020 5:21:36 PM
Zetane Systems

Zentane Systems have developed artificial intelligence to identify antiviral compounds that merit further study as possible pharmaceutical treatments 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 data set that pairs amino acid sequences with antivirals known to associate with defined viral amino acid sequences. This data set 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.


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Artificial intelligence (AI) technology is a recent addition to bioinformatics that shows much promise in streamlining the discovery of pharmacologically active compounds (Stephenson et al., 2019). The subdomain of AI, machine learning, provides particular benefits in identifying how drugs effective in one context might have utility in an unknown clinical context or against a novel pathology (Napolitano et al., 2013). The technology works by finding patterns in how a pharmaceutical molecule exerts its activity by binding to defined regions of a biomolecule, such as a segment of a protein.

Past research now provides a sizeable bank of information concerning drug-biomolecule interactions. Training machine learning models with these findings can uncover patterns, patterns which then serve to make inferences and predict future outcomes. Using drug re-purposing as an example, we can now train machine learning algorithms to identify patterns in how antiviral compounds bind to proteins from diverse virus species. We aim to train an AI model so that when presented with the proteome of a novel virus, it will identify the presence of protein segments that are similar to those identified in past studies. The final output from the AI model is a best-fit prediction as to which known antivirals are likely to associate with those familiar protein segments.

The application of AI in biomedical research provides new means to conduct in-silico exploratory studies and high-throughput analyses using information already available. In addition to deriving more value from past research, researchers can develop AI technology in relatively short periods of time.

These benefits are of particular interest for the current COVID-19 health crisis. The novelty of the SARS-CoV-2 virus requires that we execute health interventions based on past observations. Grappling with an unforeseen pandemic with no known treatments or vaccines and when every passing day is met with the loss of thousands of lives, time is a precious commodity in short supply.

The potential for rapid innovation from AI technology is of utmost significance. The ability to conduct many complex analyses with AI enables us to research insights quickly that can help steer us in the right direction for future studies likely to produce fruitful results. Predictions made by AI also can provide complementary evidence when paired with less-robust studies that are faster and more practical to complete. This can offer greater support for sound decision-making as we wait to complete lengthy, though necessary and rigorous, clinical trials for therapeutics and vaccines. As a company specialising in AI, we present here our attempts to develop AI models that can guide efforts to re-purpose current antiviral drugs as therapeutics against COVID-19.


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Zetane Systems developed AI models to predict which antivirals might be able to treat COVID-19. Initial results are promising since some of the AI-predicted antivirals were confirmed as effective in independent clinical trials. Zetane now seeks partners in biomedical research to use their AI models to streamline drug development for the pandemic. Can you help? Contact them at



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Topics: AI, Digital Health, HealthTech, Machine Learning, COVID-19