YSM Issue 97.1

Computational Biology
FEATURE
We can now [...] provide a justification for why
some molecules work better than others in a way
that directly aims to produce the next generation
of antibiotic candidates.
scores,” Wong said. The next step was
to get the models to explain why these
substructures matter.
To accomplish this, Wong and his
colleagues started by manually screening
nearly forty thousand compounds for
their ability to inhibit MRSA growth and
their toxicity to human cells. They then
trained deep learning models on this
data, testing the models with additional
data afterward to ensure accuracy.
Next, they fed over twelve million new
compounds with unknown effects into
these models. The models returned the
compounds expected to have the best
ability to inhibit MRSA growth with
low toxicity to humans. Finally, the
researchers applied a technique known
as a Monte Carlo tree search, which
performed the all-important task of
identifying substructures responsible
for the outputs of the models.
“We set out to see if we could ‘open
the box,’” Wong said. In the end, the
model used an algorithm similar to the
one used in the game-playing AI called
AlphaGo. While playing AlphaGo and
identifying a new antibiotic class may
seem like two very different processes,
they have a key similarity: a need to
search through an extremely large space
of possibilities efficiently.
Armed with potential structural
classes, Wong and his colleagues
sorted through the data. They found
explanations from the algorithm that
matched the logic behind already
identified antibiotic classes. However,
the model also identified five different
justifications for
substructures that
could potentially
identify new classes. After narrowing
down activity, they found that over
forty percent of molecules within these
newly identified classes showed activity
that inhibited MRSA growth. All of these
molecules were new to antibiotic researchers.
Further testing in the lab isolated the
two best candidates, forming an entirely
new class of antibiotics likely able to
combat MRSA effectively in a clinical
setting. These two compounds share
a common substructure which was
identified by the model as the source
of their antimicrobial ability. Testing
of their abilities in living organisms
showed that they worked specifically
against Gram-positive bacteria such as
MRSA while avoiding injury of healthy
cells. They kill bacteria by dissipating
the pH gradient within them, causing
them to burst open. The compounds
succeeded in the tough task of killing
MRSA in afflicted mice, showing
promising clinical potential.
In identifying this new class, Wong
and his colleagues opened the proverbial
“black box,” finding a way to make
deep learning algorithms explain their
results using chemical substructures.
While successful in explaining the
properties that give a potential drug
antimicrobial abilities, the model
falls short in other areas. There are
other important properties necessary
for a potential antibiotic, including
avoiding side effects such as hemolysis,
the destruction of red blood cells, or
genotoxicity, the damaging of DNA.
Wong and his colleagues were forced
to consider these possible side effects
only after they had identified their new
structural class. “Better predicting of
all of these properties remains a critical
challenge,” Wong said.
This process took two years, but
since a large part of that time was spent
developing the methodology, future
research could take place considerably
faster. Therefore, this new technique
is a crucial new way to fight antibiotic
resistance. “This discovery directly
contributes to our arsenal of antibiotic
candidates,” Wong said. “Our work also
promises to accelerate antibiotic drug
discovery by making deep learning
models more explainable and providing
publicly available large datasets and
models that accurately predict selective
antibiotic activity.”
Wong and his colleagues are currently
working on using the substructurebased
explanations given by AI to
design new antibiotics from scratch. But
impacts extend beyond just antibiotics.
“We have also been continuing to
develop and apply approaches like the
one published here to discover other
types of drugs—for instance, those
that modulate aging and age-related
pathways,” Wong said.
With the black box open, the future
of antibiotic resistance is looking less
bleak. “This is a very different approach
from the one-target, one-disease
approach prevalent in drug discovery,
which typically aims to just optimize
the fit of the small molecule against
the target,” Wong said. “We can now
[...] provide a justification for why some
molecules work better than others in a
way that directly aims to produce the
next generation of antibiotic candidates.”
In the end, when describing the
process of reaching this crucial
finding, Wong had only one word to
use: “Exhilarating.” ■
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March 2024 Yale Scientific Magazine 29