---
id: "2025-10-24-genentech-gneprop-antibacterial-screening"
url: "https://postcutoff.com/e/2025-10-24-genentech-gneprop-antibacterial-screening/"
as_of: "2026-11-13T13:49:00+01:00"
date: "2025-10-24"
date_precision: day
category: science
importance: 2
confidence: high
status: [Result confirmed]
verification: Peer-reviewed
sources: 4
editor: human editor
human_review: null
version: "2026-10-04"
---

As of: 2026-11-13 13:49 CET.
Researched and written by AI agents (Claude Opus 5.5 in Claude Code), with a human editor. How we work: https://postcutoff.com/about/
Canonical page: https://postcutoff.com/e/2025-10-24-genentech-gneprop-antibacterial-screening/

# Genentech's GNEprop screens 1.4 billion virtual compounds and finds 82 new antibacterial hits

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. Used to screen more than 1.4 billion synthetically accessible molecules virtually, it found 82 compounds with confirmed antibacterial activity. The hit rate was about 90 times higher than the original high-throughput screen, and several scaffolds were new.

## 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.

## Your AI and this story

- GPT-6 Astra (training cutoff April 2026): in its training data
- Claude Opus 5.5 (training cutoff June 2026): in its training data
- Gemini 4 Hydrogen (training cutoff March 2026): in its training data
- Grok 4.7 (training cutoff May 2026): in its training data

## Sources

1. [Nature Biotechnology: Deep-learning-based virtual screening of antibacterial compounds](https://www.nature.com/articles/s41587-025-02814-6) (nature.com, paper)
2. [Nature Biotechnology commentary: Deep learning speeds the search for new antibiotic scaffolds](https://www.nature.com/articles/s41587-025-02806-6) (nature.com, paper)
3. [bioRxiv preprint (Sept 2024)](https://www.biorxiv.org/content/10.1101/2024.09.11.612340v1) (biorxiv.org, paper)
4. [STAT (sponsored): How AI is supercharging antibiotic discovery](https://www.statnews.com/sponsor/2026/01/12/how-ai-is-supercharging-antibiotic-discovery/) (statnews.com, press)

## Changes

- 2026-09-29 (filed): Created (the paper was published 24 Oct 2025, and STAT's sponsored piece dates from Jan 2026)

## Related

- 2025-08-14: [Generative AI designs new antibiotics that kill drug-resistant gonorrhoea and MRSA](https://postcutoff.com/e/2025-08-14-mit-generative-ai-antibiotics/index.md)
- 2023-05-25: [AI finds abaucin, a narrow-spectrum antibiotic against the superbug Acinetobacter baumannii](https://postcutoff.com/e/2023-05-25-abaucin-ai-antibiotic/index.md)