The new drug has been identified from a catalogue of 7,000 potential drug compounds using AI by researchers from the Massachusetts Institute of Technology (MIT) and McMaster University.
: Researchers at the Massachusetts Institute of Technology (MIT) and McMaster University have used machine learning, a branch of Artificial Intelligence (AI), to identify a new antibiotic that could fight bacteria that enhances drug-resistant infections.
The drug could help combat Acinetobacter baumannii, a species of bacteria that is often found in hospitals and can lead to pneumonia, meningitis, and other serious infections. The same bacteria is also responsible for the leading cause of infections in wounded soldiers.
The new drug has been identified from a catalogue of 7,000 potential drug compounds using AI, which was trained to evaluate whether a chemical compound will inhibit the growth of A. baumannii.
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“Acinetobacter can survive on hospital doorknobs and equipment for long periods of time, and it can take up antibiotic resistance genes from its environment. It’s really common now to find A. baumannii isolates that are resistant to nearly every antibiotic,” Jonathan Stokes, a former MIT postdoc, said in a statement.
The study published in the journal Nature Chemical Biology states that machine learning methods allow for the rapid exploration of chemical space, increasing the probability of discovering new antibacterial molecules.
The new discovery further validates the role of AI in speeding up the process of identifying novel antibiotics.
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The team trained the AI to identify chemical structures that could inhibit growth of E. coli and screened over 100 million compounds, which yielded a molecule that the researchers called halicin. This molecule could not only kill E. coli but several other bacterial species as well.
"We turned our attention to what I perceive to be public enemy No. 1 for multidrug-resistant bacterial infections, which is Acinetobacter,” Jonathan Stokes added.
They cultured A. baumannii in lab and exposed it to about 7,500 different chemical compounds to see which ones could inhibit growth of the microbe. The structure of each molecule was then fed to the AI system.
In an animal trial on mice, the team showed that the drug, which they named abaucin, could treat wound infections caused by A. baumannii and was also working against a variety of other drug-resistant infections.
MIT in a release said that further experiments revealed that the drug kills cells by interfering with a process known as lipoprotein trafficking, which cells use to transport proteins from the interior of the cell to the cell envelope.
“We haven’t finalized the experimental data acquisition yet, but we think it’s because A. baumannii does lipoprotein trafficking a little bit differently than other Gram-negative species. We believe that’s why we’re getting this narrow spectrum activity,” Stokes said.
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