A new system that can predict a proposed drug’s chemical
structure could help prevent adverse drug interactions, one of the leading
causes of patient death.
Why it matters: According
to the FDA, serious adverse drug interactions could kill more than 100,000
hospitalized people in the US every year. But traditional ways of avoiding such
interactions during drug development require expensive and laborious physical
testing and clinical trials to catalogue all the proposed drug’s possible
chemical interactions with existing ones.
How it works: The system takes in two different drugs
and generates a prediction for how or whether they will interact. To get there,
the researchers first developed a new way to represent the 3D chemical
structures of drugs in a character format that could be read by a neural
network. The drug melatonin,
for example, is represented by “CC(=O)NCCC1=CNc2c1cc(OC)cc2,” while morphine is represented
by “CN1CCC23C4OC5=C(O)C=CC(CC1C2C=CC4O)=C35.”
They then translated a database of known drug
interactions into this format and trained a neural network. The resulting
system predicts the probability that two drugs will have an adverse interaction
and shows the particular parts of the molecule that contributed to that
prediction.
The results: When the researchers tested their system on two common
drug interaction data sets, it performed better than state-of-the-art results
from existing AI systems. The paper is being presented
at the proceedings of the Association for the Advancement of Artificial
Intelligence later this week.
Co-pilot: The new techniques for analyzing chemical data could have
many other applications, including drug and material design. “There's just an
awful lot of the modern world that depends on chemistry,” says David Cox, the
director of the MIT-IBM Watson AI Lab, a member of which coauthored the paper.
“There’s tremendous potential for AI to be a copilot for us, augmenting our
ability to reason about chemical interactions, properties, and qualities.”
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