Science journalism frequently oversimplifies scientific investigation. This comment will similarly make simplifications, but hopefully overall will provide more clarity.

> The AI was then unleashed on a list of 6,680 compounds whose effectiveness was unknown. The results - published in Nature Chemical Biology - showed it took the AI an hour and a half to produce a shortlist.

"published in Nature Chemical Biology" is a link you have to click to see the article in fulltext, which I would encourage you to read if you really want to understand the study. I would link it directly, but there is some site-referrer magic happening that allows the BBC article link to cause Nature publishing group to show the fulltext.

To better understand what was done:

Training set (manually tested): off-patent drugs (2,341 molecules) and synthetic chemicals (5,343 molecules). In particular the synthetic chemicals are likely to have unacceptable side effect profiles. Result of manual screen: 480 molecules capable of inhibiting Acinetobacter growth by 20%. 480/(2341+5343) = 6-7%

Result of AI processing and additional filtering criteria: model applied to "Drug Repurposing Hub" dataset consisting of 6,680 molecules which they claim have demonstrably favorable cytotoxicity profiles and drug-like properties and yielding 3 sets of 240 drugs each. The three sets:

1. 240 drugs identified by the model as having >20% probability of at least 20% growth inhibition of Acinetobacter and structurally _dissimilar_ to those with antibiotic activity in the training set. Manually testing for those capable of more stringent criteria (80% inhibition of growth) yielded 9 drugs.

2. 240 drugs with lowest prediction scores: Manual testing yields no active drugs, providing some basic validation that classifier is functional.

3. 240 drugs with highest prediction scores (without additional filtering criteria based on structural dissimilarity): Manual testing yields 40 drugs capable of 80% inhibition. 40/240 = 16-17%. Yield enrichment: 16%/6% = 260% or a 2.6x improvement compared to naive screen of training set. To be fair, this isn't a direct claim of the paper for good reason: the drugs in their training set and "validation set/drug repurposing hub" are fundamentally different and may have different baseline antibiotic activity across the set.

The process of narrowing down these datasets using the model could be accomplished in hours (their claim) instead of days (my claim). Days is optimistic prediction, requiring high-throughput systems and/or staffing in place to run these screening assays mostly in parallel instead of serially. Also prevents costs associated with further biochemical investigation (cultures, chemical synthesis and assays are not free).

Most direct value of this work is in accelerating drug screening process, reducing cost and developing AI-tractable representations of pharmaceutically-relevant chemical features. Additionally, proof of concept for identifying drugs with appropriate side effect profiles that happen to have antibiotic activity but would not have been identified with existing/common structural analysis approaches, since they are structurally dissimilar to the testing dataset screen. In this case they identified a "CCR2− selective chemokine receptor antagonist" that had antibiotic properties; some googling suggests that this drug class mostly has roles in fibrosis/inflammation regulation and may have roles in autoimmune disorders and those with significant fibrosis as part of the pathology (e.g. cardiovascular disease, liver disease, diabetes). You wouldn't expect most drugs in this class to have any antibiotic properties and many companies would not focus their first efforts on screening such drugs with biochemical assays.