海角社区

海角社区 DeepDrug Team Uses Artificial Intelligence to Discover New Treatments for Coronavirus

Goal: New COVID-19 treatments within days or weeks.

The 海角社区 DeepDrug team, a current semifinalist for the , is using artificial intelligence, or AI, to discover new drugs. Although their work so far has focused mainly on antibiotics and antimicrobials, they are now working as quickly as they can to find new antivirals effective against coronavirus. The team is in the process of retraining their AI by feeding it large datasets of information and asking it questions鈥攕ome with known answers鈥攖o see how well it is learning. Within days, they hope to have their AI begin suggesting new drugs, or drug combinations, to help save lives. 

 

Some of the drugs the AI will suggest would need to go through rigorous testing to show they鈥檙e safe and effective, but some would not. There are about 90 antiviral drugs on the market that have already been approved by the FDA. It is possible that DeepDrug鈥檚 AI will propose using one or a combination of them to treat COVID-19. This could altogether eliminate the need for clinical trials and significantly push up the timeline for getting drugs to patients. Most of the FDA-approved antivirals available have so far been used to treat HIV, hepatitis B and C, and herpes, but could prove effective against coronavirus as well.

To train their AI, DeepDrug is using a dataset of 2,700 antiviral peptides and associated targets. The reason for this is the limited number of FDA-approved antivirals that target influenza and positive-strand RNA-type viruses, which coronaviruses are. Available drugs simply don鈥檛 provide enough data to render the artificial intelligence 鈥渟mart.鈥�

 

鈥淲e cannot eliminate the virus from the body or prevent it from infecting more people. What we can do is lower the threat and lower the mortality rate鈥攅specially for people with severe conditions who instead could have mild conditions. They鈥檒l still get infected, but they鈥檒l survive. More people could survive the pandemic.鈥濃��Michal Brylinski

One component of DeepDrug鈥檚 software is called eDrugRes, which the team has been using to predict drug resistance in bacteria. But the way eDrugRes works, by analyzing the protein-protein interactions in cells, can be applied to viruses as well.
 
鈥�Earlier, we looked at the protein-protein interactions across many different species, actually 18,000 different bacterial strains,鈥� said Adam Bess, an 海角社区 computer science graduate student who is working with lead researchers Supratik Mukhopadhyay, associate professor in the Department of Computer Science, and Michal Brylinski, recent Rainmaker Award winner and associate professor in the Department of Biological Sciences with a joint appointment in the Center for Computation & Technology. 鈥淣ow, we鈥檙e only analyzing the human side; the different mechanisms through which viruses move and propagate in the body. Because of the lack of data, we have to use a slightly different methodology.鈥�
 
The team, with guidance from 海角社区 immunotoxicologist Stephania Cormier, is analyzing 45 known mechanisms, including how a virus forms a bond with a host cell; membrane fusion, the process through which a virus enters a cell; how a virus unpacks its contents; how it replicates itself; and how new copies of the virus then go infect other host cells.

鈥淓ssentially, we鈥檙e looking at all of the different aspects of how a virus spreads throughout the body,鈥� Mukhopadhyay continued. 鈥淎nd then we link these antiviral peptides with each of these mechanisms, associate them with specific protein pathways, and score them depending on how effective they are at inhibiting a particular mechanism. At the end, we should know which are the three or four drugs best suited to slow or prevent a virus from propagating.鈥�
 
鈥淭he mapping is a critical component,鈥� Brylinski added. 鈥淚f we cannot map the peptides properly, nothing is going to work. But if we can, we鈥檒l establish very good connections between viral activity and specific drugs in one unifying framework. It鈥檚 challenging but exciting work.鈥�
 
If the team is successful, their AI could suggest antiviral drugs to reduce the impact of COVID-19 within mere days. The team is careful to point out, however, that their end result won鈥檛 be neither a vaccine nor a complete cure.
 
鈥淲e cannot eliminate the virus from the body or prevent it from infecting more people,鈥� Brylinski clarified. 鈥淲hat we can do is lower the threat and lower the mortality rate鈥攅specially for people with severe conditions who instead could have mild conditions. They鈥檒l still get infected, but they鈥檒l survive. More people could survive the pandemic.鈥�

A few drugs, such as chloroquine (and its derivative hydroxychloroquine, both used to prevent and treat malaria) along with azithromycin and remdesivir, have been approved by the FDA for treating SARS-CoV-2 (betacoronavirus) infections. Most of these treatments were discovered through trial and error in different parts of the world. What DeepDrug proposes is a principled approach to drug discovery and drug repurposing (meaning, established drugs are used to treat new or different conditions) based on datasets that would be too large鈥攐r at least quite slow鈥攖o process without the use of artificial intelligence. Also, by helping to predict the success/failure rate of a drug, the AI could prevent researchers from wasting a lot of time exploring what are likely to be dead ends. DeepDrug鈥檚 AI would save time exploring the currently FDA-approved antivirals to see if they could treat COVID-19 as well鈥攁long with many possible combinations with other antivirals and drugs.

 

 

Elsa Hahne
海角社区 Office of Research & Economic Development
225-578-4774
ehahne@lsu.edu