YSM Issue 97.1
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FEATURE<br />
Computational Biology<br />
AI VS. SUPERBUGS<br />
CAN DEEP LEARNING BEAT<br />
ANTIBIOTIC RESISTANCE?<br />
BY MADELEINE POPOFSKY | ART BY MADELEINE POPOFSKY<br />
The great irony of recent advances<br />
in artificial intelligence (AI)<br />
is that even as programs have<br />
gained capabilities that ten years<br />
ago we could only dream of, most of<br />
the time, we have no idea how they<br />
do it. This lack of explainability has<br />
particularly frustrating consequences<br />
for the use of AI in drug discovery.<br />
Lacking the understanding of how AI<br />
generates predictions of successful drug<br />
candidates, scientists struggle to design<br />
drugs similar to those predicted. Felix<br />
Wong, a postdoctoral fellow at MIT,<br />
worked in collaboration with other<br />
researchers to solve this explainability<br />
problem in a recent Nature paper. The<br />
team applied newly explainable AI<br />
results to one of the most<br />
pressing medical crises<br />
of our age: antibiotic<br />
resistance. Currently,<br />
bacteria are<br />
developing resistance to antibiotics at<br />
a rate that outpaces researchers’ ability<br />
to design new ones. “Antimicrobial<br />
resistance is a public health crisis that<br />
is projected to kill ten million people<br />
worldwide per year by 2050,” Wong<br />
said. An especially deadly enigma is<br />
methicillin-resistant Staphylococcus<br />
aureus (MRSA), which already kills<br />
over ten thousand per year in the<br />
United States. For researchers, MRSA<br />
has proved to be an elusive target. As<br />
a Gram-positive bacterium, S. aureus<br />
lacks an outer membrane that helps<br />
many bacteria defend themselves from<br />
antibiotics, but it has nevertheless<br />
independently evolved resistance to<br />
many antibiotics. Thus, to find an<br />
antibiotic for a bacterium as unique<br />
as MRSA, researchers would need to<br />
identify a whole new structural class<br />
of drugs—a tall order, given that gaps<br />
between such kinds of discoveries have<br />
previously exceeded thirty-five years.<br />
This is where AI steps in. Over the<br />
years, researchers have trained deep<br />
learning models, a type of AI, to<br />
identify certain desirable molecules<br />
that are linked to antimicrobial activity.<br />
Such models have limited success when<br />
researchers cannot identify the models’<br />
reasoning—without it, researchers can’t<br />
point to specific chemical features that<br />
give the molecules their desired effects.<br />
These models are typically referred to<br />
as “black box” models.<br />
Equipped with these standard “black<br />
box” models and determined to shed<br />
light on their inner workings, Wong and<br />
his colleagues took on the challenge of<br />
creating an antibiotic for MRSA. Their<br />
crucial insight was to focus on specific<br />
chemical substructures as units for the<br />
deep learning models. If a model focuses<br />
on specific substructures, Wong and his<br />
colleagues thought, then maybe they<br />
could get the model to explain which<br />
substructures in its chosen molecules<br />
account for its antimicrobial activity.<br />
During preliminary testing, this idea<br />
was confirmed. “We noticed that<br />
compounds with similar structures have<br />
consistently similar model prediction<br />
28 Yale Scientific Magazine March 2024 www.yalescientific.org