Researchers at MIT and McMaster University have used artificial intelligence (AI) to discover a new antibiotic that effectively kills drug-resistant bacteria, particularly Acinetobacter baumannii, a species commonly found in hospitals. This bacterium is associated with serious infections, such as pneumonia and meningitis, and is one of the leading causes of infections among wounded soldiers. The rise of antibiotic-resistant bacteria requires the development of new antibiotics, and the use of AI in drug discovery holds great promise.
Researchers used a machine learning algorithm to screen nearly 7,000 chemical compounds and identify a potential drug that inhibits the growth of Acinetobacter baumannii. The AI algorithm was trained to recognize patterns in large data sets and predict the inhibitory properties of chemical compounds. This approach allows the identification of new antibiotics with distinct chemical structures compared to existing drugs.
In their initial study, the team successfully trained the AI algorithm to identify compounds that could inhibit the growth of E. coli, producing a molecule called halicin. Halicin demonstrated the ability to kill multiple bacterial species resistant to conventional treatment. Building on this success, the researchers focused on combating A. baumannii, a major threat due to its multidrug resistance.
To train their computational model, the researchers exposed A. baumannii to various chemical compounds and observed their inhibitory effects. The AI algorithm analyzed the chemical structures of these compounds and learned to associate specific characteristics with growth inhibition. The algorithm then screened more than 6,000 compounds from the Broad Institute’s Center for Drug Reuse, quickly identifying a few hundred top candidates. From there, the team selected 240 compounds for experimental testing in the lab, prioritizing those with properties structurally distinct from existing antibiotics.
The tests turned up nine potential antibiotics, including one particularly potent compound. Originally investigated as a diabetes drug, this compound effectively kills A. baumannii without affecting other bacterial species. This narrow spectrum of activity minimizes the risk of bacterial resistance and reduces damage to beneficial gut bacteria that help prevent opportunistic infections.
The researchers named the powerful antibiotic abaucin and demonstrated its efficacy in treating A. baumannii wound infections in mice. Laboratory tests confirmed its efficacy against several drug-resistant strains of A. baumannii isolated from human patients. Further research revealed that abaucin interferes with lipoprotein trafficking, a cellular process involved in protein transport. In particular, abaucin selectively targets A. baumannii despite the fact that this process is present in all Gram-negative bacteria. The researchers suggest that subtle differences in the way A. baumannii traffics lipoproteins contribute to drug selectivity.
The team is collaborating with McMaster researchers to optimize the medicinal properties of abaucin for its potential use in patients. Furthermore, they plan to apply their AI modeling approach to identify potential antibiotics for other drug-resistant infections caused by bacteria such as Staphylococcus aureus and Pseudomonas aeruginosa.
The successful application of AI in the identification of a new antibiotic highlights its potential to accelerate and expand the search for effective treatments against drug-resistant bacteria. This research addresses the urgent need for new antibiotics and demonstrates the power of AI to revolutionize the field of drug discovery.
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Niharika is a technical consulting intern at Marktechpost. She is a third year student, currently pursuing her B.Tech from the Indian Institute of Technology (IIT), Kharagpur. She is a very enthusiastic individual with a strong interest in machine learning, data science, and artificial intelligence and an avid reader of the latest developments in these fields.
