Research & Development

The World Health Organization (WHO) has identified Acinetobacter baumannii as one of the world’s most dangerous antibiotic resistant bacteria, which the researchers were attempting to treat.

Often, this bacterium is found in hospital settings, surviving on surfaces for long periods of time, as well as picking up DNA from other species of bacteria in its environment, for example antibiotic-resistant genes.

The study was published in Nature Chemical Biology and explains how the researchers utilised an AI platform to predict the structural classes of antibacterial molecules and ultimately identify a new antibacterial compound named abaucin.

Jonathan Stokes, lead author on the study and an assistant professor in McMaster University’s Department of Biomedicine and Biochemistry, commented: “This work validates the benefits of machine learning in the search for new antibiotics. Using AI, we can rapidly explore vast regions of chemical space, significantly increasing the chances of discovering fundamentally new antibacterial molecules. […] We know broad-spectrum antibiotics are suboptimal and that pathogens have the ability to evolve and adjust to every trick we throw at them. AI methods afford us the opportunity to vastly increase the rate at which we discover new antibiotics, and we can do it at a reduced cost. This is an important avenue of exploration for new antibiotic drugs.”

James J Collins, professor of medical engineering and science at MIT and Life Sciences faculty lead at the MIT Abdul Latif Jameel Clinic for Machine Learning in Health, added: “AI approaches to drug discovery are here to stay and will continue to be refined. We know algorithmic models work, now it’s a matter of widely adopting these methods to discover new antibiotics more efficiently and less expensively.”