Explainable deep learning discovers a new structural class of antibiotics against MRSA
Result confirmed
The takeaway
Felix Wong, James Collins and colleagues (Nature, Dec 2023) screened ~39,000 compounds, trained graph neural networks, and used explainable substructure analysis on ~12M compounds.
Status
- Claim
Result confirmed
- Our reporting
- High confidence
- Verification
- Peer-reviewed
- Importance
- 3 of 5
- Last verified
- 29 September 2026
Your AI and this story
- GPT-6 AstraIn its training data
- Claude Opus 5.5In its training data
- Gemini 4 HydrogenIn its training data
- Grok 4.7In its training data
It happened before the cutoffs of all four assistants, so it can be in their training data.
Key facts
- Nature, published online 20 Dec 2023
- ~39,000 compounds tested experimentally; ~12M scored computationally
- Active against MRSA and vancomycin-resistant enterococci; effective topically and systemically in mice
What happened
Rather than a black-box ranking, the model identified which chemical substructures drove predicted activity, leading chemists to a new antibiotic class.
Why it matters
New structural classes of antibiotics are rarely discovered. This one came from interpretable AI, which also showed chemists why the molecules work.
Sources
2 sources from 2 sites. Numbers match the chips in the text.
2 sources: 1 primary, 1 press
Primary
- Discovery of a structural class of antibiotics with explainable deep learning (Nature)nature.com, paper
Press
- Broad Institute: Researchers use AI to identify new class of antibiotic candidatesbroadinstitute.org, press
Changes
- Filed