This post originally appeared on MIT Technology Review
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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