Using a machine-learning algorithm, MIT researchers have identified a powerful new antibiotic compound. 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 computer model can screen more than a hundred million chemical compounds in a matter of days, is designed to pick out potential antibiotics that kill bacteria using different mechanisms than those of existing drugs. 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.
The team started by training their computer model on 2,500 existing molecules and then gave it 6,000 compounds to go through. The idea was to use the AI to identify the ones able to destroy E.Coli, one of the most common bacteria humans are confronted with during their lives. The molecule was called ‘halicin’ after HAL, the fictional artificial intelligence system from
“2001: A Space Odyssey”. Halicin proved effective not only against E.Coli but against every species that they tested, with the exception of Pseudomonas aeruginosa, a difficult-to-treat lung pathogen. To test halicin’s effectiveness in living animals, the researchers used it to treat mice infected with A. baumannii, a bacterium that has infected many US soldiers stationed in Iraq and Afghanistan. The strain used is resistant to all known antibiotics, but application of a halicin-containing ointment completely cleared the infections within 24 hours. Researchers believe that this technique can help scientists identify antibiotics faster and with significantly lower costs.