{"schema":"postcutoff/event@1","as_of":"2026-11-13T13:49:00+01:00","url":"https://postcutoff.com/e/2025-10-24-genentech-gneprop-antibacterial-screening/","md":"https://postcutoff.com/e/2025-10-24-genentech-gneprop-antibacterial-screening/index.md","disclosure":{"written_by":"AI agents (Claude Opus 5.5 in Claude Code)","editor":"human editor","policy":"https://postcutoff.com/about/"},"license":null,"id":"2025-10-24-genentech-gneprop-antibacterial-screening","date":"2025-10-24","date_precision":"day","short_title":"Genentech's GNEprop screens 1.4 billion virtual compounds and finds 82 new antibacterial hits","deck":null,"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.","category":"science","category_label":"Science & math","importance":2,"confidence":"high","status":{"key":"confirmed","labels":["Result confirmed"]},"sources":[{"n":1,"title":"Nature Biotechnology: Deep-learning-based virtual screening of antibacterial compounds","url":"https://www.nature.com/articles/s41587-025-02814-6","type":"paper","group":"primary","domain":"nature.com"},{"n":2,"title":"Nature Biotechnology commentary: Deep learning speeds the search for new antibiotic scaffolds","url":"https://www.nature.com/articles/s41587-025-02806-6","type":"paper","group":"primary","domain":"nature.com"},{"n":3,"title":"bioRxiv preprint (Sept 2024)","url":"https://www.biorxiv.org/content/10.1101/2024.09.11.612340v1","type":"paper","group":"primary","domain":"biorxiv.org"},{"n":4,"title":"STAT (sponsored): How AI is supercharging antibiotic discovery","url":"https://www.statnews.com/sponsor/2026/01/12/how-ai-is-supercharging-antibiotic-discovery/","type":"press","group":"press","domain":"statnews.com"}],"official":3,"filed":"2026-09-29","updated":"2026-09-29","orgs":["Genentech","NVIDIA","Mila"],"title":"Genentech's GNEprop screens 1.4 billion virtual compounds and finds 82 new antibacterial hits","summary":"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"],"key_numbers":[],"tags":["antibiotics","drug-discovery","graph-neural-network","virtual-screening","biology"],"science":{"field":"biology","subfield":"antibiotic discovery","problem":"Finding structurally novel antibacterial scaffolds in ultra-large chemical libraries","result":"Deep-learning virtual screen of 1.4B compounds yielded 82 experimentally confirmed antibacterial hits, ~90x HTS hit rate.","open_since":"","ai_system":["GNEprop (graph neural network)"],"human_role":"Human-led; experimental screening and validation by Genentech scientists","verification":{"key":"peer-review","label":"Peer-reviewed","text":"Peer-reviewed in Nature Biotechnology; lab-validated in vitro"},"status":"confirmed","shock":""},"body_md":"## What happened\n\nGenentech 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.\n\n## Why it matters\n\nIndustrial-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.","disputed":[],"related":[{"id":"2025-08-14-mit-generative-ai-antibiotics","url":"https://postcutoff.com/e/2025-08-14-mit-generative-ai-antibiotics/","date":"2025-08-14","date_precision":"day","short_title":"Generative AI designs new antibiotics that kill drug-resistant gonorrhoea and MRSA","deck":null,"takeaway":"It showed generative AI exploring chemical space beyond existing compound libraries for one of medicine's most urgent needs.","category":"science","category_label":"Science & math","importance":4,"confidence":"high","status":{"key":"confirmed","labels":["Result confirmed"]},"sources":2,"official":1,"filed":"2026-09-29","updated":"2026-09-29","orgs":["MIT"]},{"id":"2023-05-25-abaucin-ai-antibiotic","url":"https://postcutoff.com/e/2023-05-25-abaucin-ai-antibiotic/","date":"2023-05-25","date_precision":"day","short_title":"AI finds abaucin, a narrow-spectrum antibiotic against the superbug Acinetobacter baumannii","deck":null,"takeaway":"It showed that AI could find narrow-spectrum antibiotics, which spare the microbiome and slow resistance.","category":"science","category_label":"Science & math","importance":3,"confidence":"high","status":{"key":"confirmed","labels":["Result confirmed"]},"sources":2,"official":1,"filed":"2026-09-29","updated":"2026-09-29","orgs":["McMaster University","MIT"]}],"people":[],"posts":[],"videos":[],"models":[],"changes":[{"date":"2026-09-29","type":"filed","text":"Created (the paper was published 24 Oct 2025, and STAT's sponsored piece dates from Jan 2026)"}],"provenance":{"agents":[{"model":"Claude Opus 5.5","maker":"Anthropic","tool":"Claude Code"}],"filed":"2026-09-29","run":null,"sources_read":null,"updated":"2026-09-29","human_review":null,"version":{"date":"2026-10-04"}},"gaps":[{"model_id":"gpt-6-astra","name":"GPT-6 Astra","cutoff":"2026-04","days_after":null,"in_training_data":true},{"model_id":"claude-opus-5-5","name":"Claude Opus 5.5","cutoff":"2026-06","days_after":null,"in_training_data":true},{"model_id":"gemini-4-hydrogen","name":"Gemini 4 Hydrogen","cutoff":"2026-03","days_after":null,"in_training_data":true},{"model_id":"grok-4-7","name":"Grok 4.7","cutoff":"2026-05","days_after":null,"in_training_data":true}],"short_url":null}