Shantenu Jha, a computational scientist at Rutgers University and Brookhaven National Laboratory, is coupling artificial intelligence techniques and algorithms with high-performance computing simulations to speed up the ability to screen billions of existing drugs for their interactions with and ability to disrupt SARS-CoV-2 proteins. In a pandemic, “there are many drug candidates people would like to screen and, even with the proliferation of cloud and supercomputers, there just wouldn’t be enough computing” to test them all, he says.
Each Friday, team members share a list of top candidates with collaborators, who either use their own AI-based methods to assess the small molecules virtually or test their effectiveness against coronavirus in the lab. Whatever the results from those analyses, the researchers feed them back into the simulations to refine the search for drugs.
“With AI, the whole idea is the more accurate data you can give it, the better its ability to predict, guide, whatever you’re using it for. You can never get enough good data,” says Jha. As the researchers tweak the simulations in response to community input, they aim to increase their chances of landing on a drug candidate that could make a difference in the pandemic.