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Using Artificial Intelligence To Discover New Drug Treatments: Science Next

Louisiana State University

In the fields of medicine and pharmacology, drugs have historically been discovered either by identifying active ingredients from traditional remedies, or by serendipity, similar to how penicillin was discovered in 1928. But modern scientists have found an alternative method that makes the hunt for new pharmaceuticals quicker, cheaper and more effective.

AI, or artificial intelligence, can be found in everything from chess-playing computers, to self-driving cars, to the maps application on your phone when it’s calculating directions. And AI has found a new home: discovering new drugs.

It currently takes on average 10 years and over $2 billion to create a new drug and get it approved. LSU’s DeepDrug team expects its AI tool, eSynth, to reduce the time for preclinical drug discovery and testing from an average of three years to six to eight months.

DeepDrug is an interdisciplinary LSU team led by Supratik Mukhopadhyay, associate professor in the Department of Computer Science, and Michal Brylinski, associate professor in the Department of Biological Sciences. 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’t be a vaccine or a complete cure.

“We cannot eliminate the virus from the body or prevent it from infecting more people,” Brylinski says. “What we can do is lower the threat and lower the mortality rate—especially for people with severe conditions who instead could have mild conditions. They’ll still get infected, but they’ll survive. More people could survive the pandemic.”

DeepDrug’s work recently made it to the semifinals of the IBM Watson AI XPRIZE, competing against AI research teams around the world for prize money totalling $5 million.

So, how does AI help to discover new drugs?
Several decades of research have led to the development of mathematical tools that have helped scientists improve their understanding of the nature of pathogens. They’re also identifying potential drug targets and predicting epidemics.

AI-based model systems are promising because they can reason over huge amounts of data. Then they can identify approaches to treat diseases by proposing a drug target, designing a molecule, and even defining patients to test it with. While it’s still considered the early days for AI’s full potential in the larger pharma industry, the technology is already making progress in this new role.

A few drugs, such as chloroquine, hydroxychloroquine, 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—or at least quite slow—to process without the use of AI.

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’s AI would save time exploring the currently FDA-approved antivirals to see if they could treat COVID-19 as well—along with many possible combinations with other antivirals and drugs.

The team is also working on additional AI modules. eToxPred will ensure that identified compounds are safe for humans. Another module, eDrugRes, will examine the protein-protein interaction network of pathogens to predict susceptibility and/or resistance to known drugs.

AI has already helped us discover new ways to mitigate the effects of the coronavirus. Recently, researchers in Singapore used AI to identify the best therapies to fight against the virus that causes COVID-19. Their results identified a combination of the drugs remdesivir, ritonavir and lopinavir at specific doses.

Special thanks to Associate Professor Michal Brylinski in the LSU Department of Biological Sciences for contributing to this report.

LSU Researchers Enter Semifinals for the $5M IBM Watson AI XPRIZE: https://www.lsu.edu/research/news/2020/0121-xprize.php
DeepDrug team website: https://www.deepdrug.org/