Genentech’s GNEprop screens 1.4 billion virtual compounds and finds 82 new antibacterial hits
Result confirmed
The takeaway
In Nature Biotechnology (24 Oct 2025), Genentech researchers with NVIDIA and Mila described GNEprop, a graph neural network trained on a ~2-million-compound phenotypic screen against sensitized E. coli.
Status
- Claim
Result confirmed
- Our reporting
- High confidence
- Verification
- Peer-reviewed
- Importance
- 2 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
- Training data: ~2 million small molecules screened experimentally against a sensitized E. coli strain
- Virtual screen of >1.4 billion synthetically accessible compounds; 82 confirmed actives; ~90-fold higher hit rate than HTS
- GNEprop includes explainability (active motifs) and out-of-distribution detection for structural novelty vs known antibiotics
- Authors include Gabriele Scalia, Steven T. Rutherford and Tommaso Biancalani (Genentech BRAID / Infectious Diseases / Computational Chemistry)
- Preprint first posted on bioRxiv in Sept 2024; Nature Biotechnology ran an accompanying commentary
What happened
Genentech combined a huge wet-lab screen with a graph neural network, then used the model to search a 1.4-billion-molecule virtual library. The model’s picks were far more likely to kill bacteria than randomly screened compounds, and several had new scaffolds.
Why it matters
Industrial-scale evidence for ML-guided antibiotic discovery, following MIT’s halicin and abaucin work. It is a hit-finding result; no candidate has entered the clinic.
Sources
4 sources from 3 sites. Numbers match the chips in the text.
4 sources: 3 primary, 1 press
Primary
- Nature Biotechnology: Deep-learning-based virtual screening of antibacterial compoundsnature.com, paper
- Nature Biotechnology commentary: Deep learning speeds the search for new antibiotic scaffoldsnature.com, paper
- bioRxiv preprint (Sept 2024)biorxiv.org, paper
Press
- STAT (sponsored): How AI is supercharging antibiotic discoverystatnews.com, press
Changes
- Filed (the paper was published 24 Oct 2025, and STAT’s sponsored piece dates from Jan 2026)