The machine learning-assisted discovery of abaucin, an antibacterial compound with narrow-spectrum activity against Acinetobacter baumannii, by a research team including Regina Barzilay and Jim Collins of MIT Abdul Latif Jameel Clinic for Machine Learning in Health (MIT Jameel Clinic) is celebrated as one of the most significant scientific advances supported by artificial intelligence in the last year.
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I’ll wager that the event of 2023 that will change our lives the most in coming years is not the sighting of a Chinese spy balloon, the failure of Silicon Valley Bank, the fall of Kevin McCarthy’s speakership or any of the other eruptions that transfixed us this year.
More likely, the event that’s judged most transformative will be some scientific or technological advance that only a handful of people know about right now — because that’s how things almost always go. The first time the word “transistor” appeared in print was in an article in The New York Times in 1948, on Page 46, following a report on two new radio shows, “Mr. Tutt” and “Our Miss Brooks.” I think we can agree that the transistor has had more impact on our daily lives in the 75 years since than either of those bits of entertainment.
Here’s one: Artificial intelligence is battling the scourge of multidrug-resistant bacteria. In May, scientists from McMaster University in Hamilton, Ontario, and elsewhere reported in the journal Nature Chemical Biology that they had used machine learning to come up with a compound against Acinetobacter baumannii, which can infect people with catheters or on ventilators in hospitals. The scientists tested around 7,500 molecules for their effectiveness against it, then poured the data into a neural network that looked for commonalities among the effective ones, then used the resulting model on 6,680 molecules it hadn’t been exposed to before. The one that was the most potent against A. baumannii had originally been explored as a treatment for kidney damage from diabetes.