My scientific work was recently highlighted in The Scientist.
The lead on aricle highlights work from my group:
Then, in 2018, graduate student Na Le Dang at Washington University in St. Louis hit upon a way to use artificial intelligence (AI)—specifically, a machine learning algorithm—to work out the possible biochemical pathways terbinafine takes when it is metabolized by the liver. Trained on large numbers of known metabolic pathways, the algorithm had learned the most likely outcomes when different types of molecules were broken down in the organ. With that information, it was able to identify what no human could: that the metabolism of terbinafine to TBF-A was a two-step process.
It’s really our ambition to automate as much as possible, so the chemists are just focusing on the much higher level, the difficult problems, the strategy.
—Adrian Schreyer, Exscientia
Two-step metabolites are much more difficult than direct metabolites to detect experimentally, which is likely why this potentially lethal outcome wasn’t flagged until after the product was on the market, says S. Joshua Swamidass, a physician scientist and computational biologist at Washington University and Dang’s supervisor. The discovery not only shed light on a long-standing biochemical mystery, but showed how the use of AI could more broadly aid drug discovery and development.
Given enough data, machine learning algorithms can identify patterns, and then use those patterns to make predictions or classify new data much faster than any human. “A lot of the questions that are really facing drug development teams are no longer the sorts of questions that people think that they can handle from just sorting through data in their heads,” Swamidass says. “There’s got to be some sort of systematic way of looking at large amounts of data . . . to answer questions and to get insight into how to do things.”
There is unquestionably a lot of hype around the potential of AI in drug discovery—Swamidass likens this period to the internet boom of the late 1990s. But many, such as Schreyer, are excited about the possibilities that this new technology offers, particularly when it comes to finding novel therapeutics for difficult-to-treat diseases.