McMaster AI model designs new antibiotic candidate in early tests

Researchers at McMaster University have developed a new generative artificial intelligence (AI) model capable of drastically speeding up drug discovery — and, in early tests, it has already designed a brand-new antibiotic. In a new study, published April 23 and selected for the cover of the June issue of Molecular Systems Biology, the team put the model to the test and identified a novel, water-soluble compound called synthecin.

The new model, called SyntheMol-RL, is trained to explore a vast chemical space of up to 46 billion possible compounds — far beyond what could realistically be tested in the lab, where even large-scale screens top out at around a million molecules. Drawing on roughly 150,000 molecular “building blocks” and a set of 50 chemical synthesis reactions, the AI model is designed to generate structurally novel antibiotic candidates.

Past iterations of SyntheMol exclusively designed molecules with antibacterial activity, without consideration for other critical properties. Over the past two years, the team — with collaborators at Stanford University — refined the model so it only generates antibacterial compounds that are easy to develop in the lab and likely to be soluble in the body.

The researchers tasked the enhanced model with generating water-soluble antibiotics that could treat infections caused by Staphylococcus aureus and quickly got several hits. From a batch of 79 model-proposed antibacterials, the group homed in on one particularly interesting compound — a novel, water-soluble compound that seemed likely to have antibiotic activity against S. aureus.

The new computer-designed drug candidate was then formulated as a topical cream in the lab and tested on an otherwise drug-resistant wound infection in mouse models. Synthecin was highly effective at controlling the infection and worked extremely well as a topical drug, while also showing early promise as something that could be applied or optimized for systemic use in the future.

The team has yet to uncover how the drug inhibits bacteria, a key step in determining its safety profile and therefore its likelihood of someday landing in clinics. The group is now actively engaged in these critical mechanism-of-action studies.

The discovery of synthecin is seen as validation that the AI model can rapidly generate high-potential drug candidates, shifting the burden of drug discovery from finding viable compounds to designing and optimizing them. The model was built to be disease agnostic, and the researchers said that shift is significant not only for antibiotic discovery, but also for other areas of biochemistry.