Explainable deep learning discovers a new structural class of antibiotics against MRSA
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. They found a new structural class of antibiotics active against MRSA and VRE that worked in mouse models.
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
Science result
- Field
- medicine / antibiotic discovery
- Problem
- New antibiotic classes against MRSA
- Result
- First new structural class of antibiotics found via explainable deep learning, validated in mouse infection models.
- AI system
- graph neural networks with Monte Carlo tree search rationale extraction
- Human role
- Human-led with AI tools
- Verification
- Peer-reviewed in Nature; lab-validated in mice
- Status
- confirmed
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.
Changelog
- 2026-09-29: created
Related events
- Deep learning discovers halicin, a structurally new broad-spectrum antibiotic ★★★★
- AI finds abaucin, a narrow-spectrum antibiotic against the superbug Acinetobacter baumannii ★★★
Sources (2)
- paperDiscovery of a structural class of antibiotics with explainable deep learning (Nature)
- pressBroad Institute: Researchers use AI to identify new class of antibiotic candidates
id: 2023-12-20-ai-new-antibiotic-structural-class · updated 2026-09-29 · open in the interactive timeline