An AI identifying molecules with features similar to other known antibiotics is exactly what statistical models are good for. But it's a first pass before actually testing whether those molecules actually work. There are a lot of false positives, but that's OK, because they still greatly narrow the field to be tested.

An AI language model is also going to generate a lot of false positives - gibberish - that you can only tell by testing it. i.e.: by having someone knowledgeable in the field read it and possibly fact check it. That kind of defeats the point of a lot of AI writing applications.

How many AI trials didn't result the same? How many trials of non-AI origin were there? What percentage of trials, where the same amount of variation was allowed, could have been similarly successful by just randomly joining chemicals etc. together the same way that the AI did but without claims of it being intelligent?

AI is just brute-force statistics, in effect. It's not a demonstration of intelligence, even if it was a useful tool. It was basically a fast brute-force simulation of a huge number of chemical interactions (and the "intelligence" is in determining what the criteria are for success - i.e. how did they "know" it was likely going to be a useful antibiotic? Because the success criteria they wrote told them so).

Intelligence would have been if the computer didn't just blindly try billions of things, but sat, saw the shape of the molecule, and assembled a molecule to clip into it almost perfectly with only a couple of attempts because it understood how it needed to fit together (how an intelligent being would do the same job). Not just try every combination of every chemical bond in every orientation until it hit.

Great for brute-force finding antibiotics, the same way that computers are in general great at automating complex and tedious tasks when told exactly what to do. But not intelligence.