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Using AI, Researchers Identify a New Class of Antibiotic Candidates That Can Kill a Drug-Resistant Bacterium

How is AI helping Researchers Identify a New Class of Antibiotic?

To combat diseases caused by bacteria that are resistant to many antibiotics, artificial intelligence has been essential in the discovery of a new class of medications. This may be useful in the fight against antibiotic resistance, which is a growing problem that killed over 1.2 million people in 2019 and will likely continue to do so for decades to come. A novel antibiotic that can kill a type of bacterium responsible for many drug-resistant diseases has been identified by researchers at MIT and McMaster University using an artificial intelligence algorithm.

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The medicine has the potential to battle Acinetobacter baumannii, a type of bacteria commonly found in healthcare facilities and a cause of pneumonia, meningitis, and other severe diseases if it were to be developed for use in patients. Wounds sustained by troops serving in Iraq and Afghanistan are also frequently infected with this particular bacterium. Using a machine-learning model they trained to determine if a chemical compound inhibits the growth of A. baumannii, the researchers were able to identify the novel medicine from a library of roughly seven thousand potential medicinal molecules.

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What are its features?

While very few new antibiotics have been created during the past several decades, many pathogenic bacteria have grown progressively resistant to current ones.

Collins, Stokes, and Regina Barzilay, a professor at MIT and co-author of the current paper, set out a few years ago to tackle this increasing problem using machine learning, an AI technique that can learn to identify patterns in massive datasets. Collaborating with MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health, Collins and Barzilay intended to find novel medicines with structurally distinct molecular bonds using this method.

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First, they showed that they could train a machine-learning system to find chemical compounds that could stop E. coli from growing. The researchers named the molecule halicin after the fictitious AI system from “2001: A Space Odyssey.” The algorithm produced it from a screen of over 100 million molecules. In addition to killing E. coli, they demonstrated that this chemical might eradicate other treatment-resistant bacterial species. After training the algorithm, scientists fed it data from the Broad Institute’s Drug Repurposing Hub, which included 6,680 novel molecules. A few hundred high-quality results were produced by this analysis, which did not take more than two hours. Researchers focused on compounds with structures different from current antibiotics or molecules from the training data, choosing 240 to test experimentally in the lab.

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A New Class of Antibiotic Candidates That Can Kill a Drug-Resistant Bacterium

Nine antibiotics, including a highly effective one, were produced during those tests. This chemical, which was first investigated for its use as a diabetes medication, was found to be highly efficient against A. baumannii but inactive against other bacterial species such as Pseudomonas aeruginosa, Staphylococcus aureus, and carbapenem-resistant Enterobacteriaceae.

Antibiotics are highly prized for their “narrow spectrum” killing capabilities, which reduces the likelihood of bacteria quickly developing resistance to the medicine. A further perk is that the medicine will probably not harm the good bacteria already present in the human digestive tract, which helps to prevent opportunistic illnesses like Clostridium difficile.David Braley Center for Antibiotic Discovery, Weston Family Foundation, Audacious Project, Digital Transformation Institute, Abdul Latif Jameel Clinic for Machine Learning in Health, DARPA Accelerated Molecular Discovery, Canadian Institutes of Health Research, Genome Canada, McMaster University’s Faculty of Health Sciences, Boris Family, a Marshall Scholarship, and the Department of Energy Biological and Environmental Research program were among the organizations that contributed to the funding of this research.

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