Using machine learning to find new antibiotics

Despite growing concerns about antibiotic resistance, the discovery of new antibiotic drugs has slowed as new molecules become increasingly difficult to identify. But in a paper publishing February 20 in the journal Cell, researchers demonstrate a method to uncover new antibiotics quickly and efficiently through machine learning.

James Collins, a synthetic biologist at MIT, and his team trained a deep neural network to identify possible antibiotic molecules using compounds known to suppress E. coli growth; the network then used these data to examine other molecules in existing chemical libraries and predict their likelihood to suppress growth of bacteria. The researchers found that nearly 50% of those compounds they prioritized to test showed some effectiveness in vitro in limiting E. coli proliferation. One compound in particular, halicin, is structurally divergent from conventional antibiotic molecules and uses a mechanism that’s uncommon in clinical antibiotics to fight a wide range of human pathogens in mice, including Clostridioides difficile and Acinetobacter baumannii.

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