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Reading the study's abstract, there will not be a "next time" because the dataset they created, and the model they trained, was specific to Acinetobacter baumannii: Here we screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. We trained a neural network with this growth inhibition dataset and performed in silico predictions for structurally new molecules with activity against A. baumannii. Through this approach, we discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii. https://www.nature.com/articles/s41589-023-01349-8 Not only were both the dataset they created, and the model they trained on it, specific to one organism, the drug they discovered also only works on that one organism ("narrow spectrum activity against A. baumannii"). If they wanted to discover drugs that work on other organisms, like Staphylococcus aureus and Pseudomonas aeruginosa that the BBC article mentions, they'd have to start all over again. So, not an approach that looks very practical at this time. Maybe in the future, when the sample efficiency and generalisation ability of neural nets has significantly improved it will be useful in practice. Study: https://www.nature.com/articles/s41589-023-01349-8 |
We can reasonably expect the bacteria to mutate against the new antibiotic if/once it's used. It's one shifty opponent. This may make the model obsolete, but maybe not - there'd cause to try the model. Actually, it would have been preferable to get more than one result at first...
[EDIT: Then again, would they have another candidate list? This model doesn't do toxicology. The second list was created by using existing proven-safe meds. Do they have another couple thousand materials good to go? If not, they won't be able to run a second time. ]