Generative AI develops potential new drugs for antibiotic-resistant bacteria

“Now we have not just entirely new molecules but also explicit instructions for how to make those molecules,” Zou said.

A new chemical space

The researchers chose the 70 compounds with the highest potential to kill the bacterium and worked with the Ukrainian chemical company Enamine to synthesize them. The company was able to efficiently generate 58 of these compounds, six of which killed a resistant strain of A. baumannii when researchers tested them in the lab. These new compounds also showed antibacterial activity against other kinds of infectious bacteria prone to antibiotic resistance, including E. coli, Klebsiella pneumoniae and MRSA.

The scientists were able to further test two of the six compounds for toxicity in mice, as the other four didn’t dissolve in water. The two they tested seemed safe; the next step is to test the drugs in mice infected with A. baumannii to see if they work in a living body, Zou said.

The six compounds are vastly different from each other and from existing antibiotics. The researchers don’t know how their antibacterial properties work at the molecular level, but exploring those details could yield general principles relevant to other antibiotic development.

“This AI is really designing and teaching us about this entirely new part of the chemical space that humans just haven’t explored before,” Zou said.

Zou and Swanson are also refining SyntheMol and broadening its reach. They’re collaborating with other research groups to use the model for drug discovery for heart disease and to create new fluorescent molecules for laboratory research.

The study was funded by the Weston Family Foundation, the David Braley Centre for Antibiotic Discovery, the Canadian Institutes of Health Research, M. and M. Heersink, the Chan-Zuckerberg Biohub, and the Knight-Hennessy scholarship.

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