In laboratory tests, the drug killed many of the world’s most problematic disease-causing bacteria, including some strains that are resistant to all known antibiotics. It also cleared infections in two different mouse models.
The group of researchers from Massachusetts Institute of Technology (MIT) claim the technology is able to work more quickly and efficiently than existing efforts, because it checks more than a hundred million chemical compounds in a matter of days to pick out potential antibiotics that kill bacteria.
It was trained specifically to track down possible antibiotic molecules known for being effective against E.coli growth.
“We’re facing a growing crisis around antibiotic resistance, and this situation is being generated by both an increasing number of pathogens becoming resistant to existing antibiotics, and an anaemic pipeline in the biotech and pharmaceutical industries for new antibiotics,” said MIT’s Professor James Collins, who is also co-founder of antibiotic drug discovery firm EnBiotix.
He added: “We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery.
“Our approach revealed this amazing molecule which is arguably one of the more powerful antibiotics that has been discovered.”
The molecule, named halicin, proved effective against E.coli, which did not develop any resistance to it during a 30-day treatment period on mice. They hope to eventually be able to use the drug on humans.
Over the past few decades, very few new antibiotics have been developed, and most of those newly approved antibiotics are slightly different variants of existing drugs.
Current methods for screening new antibiotics are often prohibitively costly, require a significant time investment, and are usually limited to a narrow spectrum of chemical diversity.
The new machine learning method could make it easier to find new drugs in future. The researchers train the algorithm on about 2,500 molecules that are already known to be effective at killing E. coli.
Once the model was trained, the researchers tested it on a library of about 6,000 compounds and picked out one molecule that was predicted to have strong antibacterial activity, and had a chemical structure different from any existing antibiotics.
Using a different machine-learning model, the researchers also showed that this molecule would likely have low toxicity to human cells.
The researchers also identified several other promising antibiotic candidates, which they plan to test further. They believe the model could also be used to design new drugs, based on what it has learned about chemical structures that enable drugs to kill bacteria.
Data scientist Mark Frankish at SAS UK & Ireland said the discovery marks a “huge step forward” for the applications of AI in healthcare.
“The technology is now more accurate than humans in diagnosing brain tumours, and its use in discovering new antibiotics is a sign of its widening scope,” he said.
“That being said, it will by no means make human doctors redundant. Instead, it will work in unison with clinicians and other medical staff, unburdening them from hours of manual processes by saving them vital time to treat patients.
“With an effective cure for the coronavirus yet to be found and fears of antibiotic resistance on the rise, the use of AI in drug discovery comes at the right time. A combination of AI, human expertise and global collaboration will allow for further developments, enabling the NHS to derive maximum value and ultimately, save more lives.”