Using a type of artificial intelligence known as deep learning, MIT researchers have discovered a class of compounds that can kill a drug-resistant bacterium that causes more than 10,000 deaths in the United States every year.
In a study appearing in Nature, the researchers showed that these compounds could kill methicillin-resistant Staphylococcus aureus (MRSA) grown in a lab dish and in two mouse models of MRSA infection. The compounds also show very low toxicity against human cells, making them particularly good drug candidates.
A key innovation of the new study is that the researchers were also able to figure out what kinds of information the deep-learning model was using to make its antibiotic potency predictions. This knowledge could help researchers to design additional drugs that might work even better than the ones identified by the model.
“The insight here was that we could see what was being learned by the models to make their predictions that certain molecules would make for good antibiotics,” said James Collins, the Termeer Professor of Medical Engineering and Science in MIT’s Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering, a core faculty member of the Wyss Institute for Biologically Inspired Engineering at Harvard University, and an institute member at the Broad Institute of MIT and Harvard. “Our work provides a framework that is time-efficient, resource-efficient, and mechanistically insightful, from a chemical-structure standpoint, in ways that we haven’t had to date.”
We are thrilled to announce the latest discovery by our partners at the Collins Lab: a novel class of antibiotics with activity against MRSA! Read more about the team's "explainable AI" approach underlying this groundbreaking discovery.https://t.co/o8PRSLULwM
— Phare Bio (@PhareBio) December 21, 2023