As reported in the Economist:
In 2019, scientists at the Massachusetts Institute of Technology (mit) did something unusual in modern medicine—they found a new antibiotic, halicin. In May this year another team found a second antibiotic, abaucin. What marked these two compounds out was not only their potential for use against two of the most dangerous known antibiotic-resistant bacteria, but also how they were identified.
In both cases, the researchers had used an artificial-intelligence (AI) model to search through millions of candidate compounds to identify those that would work best against each “superbug”. The model had been trained on the chemical structures of a few thousand known antibiotics and how well (or not) they had worked against the bugs in the lab. During this training the model had worked out links between chemical structures and success at damaging bacteria. Once the AI=spat out its shortlist, the scientists tested them in the lab and identified their antibiotics. If discovering new drugs is like searching for a needle in a haystack, says Regina Barzilay, a computer scientist at MIT who helped to find abaucin and halicin, ai
acts like a metal detector. To get the candidate drugs from lab to clinic will take many years of medical trials. But there is no doubt that AI-accelerated the initial trial-and-error part of the process. It changes what is possible, says Dr Barzilay. With ai, “the type of questions that we will be asking will be very different from what we’re asking today.”
The article is interesting throughout and here is a companion piece here.