A groundbreaking machine-learning approach has identified powerful new types of antibiotic from a pool of more than 100 million molecules including one that works against a wide range of bacteria, including tuberculosis and strains considered untreatable. According to nature.com, the researchers state that the antibiotic, called halicin, is the first discovered with artificial intelligence (AI). Even though AI has been used to aid parts of the antibiotic-discovery process before, they say that this is the first time it has identified completely new kinds of antibiotics from scratch, without using any previous human assumptions.
The work, led by synthetic biologist Jim Collins at the Massachusetts Institute of Technology in Cambridge, is published in Cell. Jacob Durrant, a computational biologist at the University of Pittsburgh, Pennsylvania states that the study is remarkable. The team did not just identify candidates, but also validated promising molecules in animal tests, he says. What’s more, the approach could also be applied to other types of drug, such as those used to treat cancer or neurodegenerative diseases, states Durrant.
Bacterial resistance to antibiotics is rising dramatically worldwide, and researchers predict that unless new drugs are developed urgently, resisting infections could kill tens of millions of people by 2050. Yet over the past few decades, the discovery and regulatory approval of new antibiotics has slowed. “People keep finding the same molecules over and over,” says Collins. “We need novel chemistries with novel mechanisms of action.”
In fact, approximately 2.8 million people become infected with antibiotic-resistant pathogens in the United States annually resulting in more than 35,000 deaths data from the Centers for Disease Control and Prevention has shown. Which makes it even more important to find antibiotics. Yet recently, very few have developed. Those that have tend to be quite similar to drugs which are currently available. The search for new antibiotics is not helped by the fact that identifying potentially effective compounds is a pricy process, as well as being a lengthy one. These searches also tend to only focus on a relatively narrow spectrum of chemical compounds.
“We use AI to virtual screen molecules to predict their antibacterial properties,” Regina Barzilay told Newsweek. “Typically, such screening is done in the lab, which is both costly and slow. Machine [learning] on the other hand can screen hundreds of millions of compounds to identify a few interesting candidates that require experimental testing. The low cost of this approach enables us to explore huge chemical space, while only testing compounds which are likely to be potent. This is the first time AI was used to find a new potent antibiotic molecule,” she said.
To begin with, the researchers trained their machine learning algorithm to identify characteristics in a database of chemicals which make compounds effective at wiping out the E. coli bacteria. After the algorithm was “trained,” the team then used it comb through another database containing around 6,000 pharmaceutical compounds. Throughout, the search, the algorithm identified an intriguing drug known as “halicin”named after the infamous artificial intelligence system in Stanley Kubrick’s sci-fi epic 2001: A Space Odyssey which has previously been explored by scientists as a potential treatment for diabetes.
The scientists used halicin to treat mice which had been infected with a lethal strain of the bacteria A. baumannii which is resistant to all known antibiotics. Surprisingly, the compound was able to completely wipe out the infection within 24 hours.