ArticleNature biotechnology2026
Deep-learning-based virtual screening of antibacterial compounds.
Article in Nature biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Artificial intelligence virtual bone organoids (AIVBOs).Journal of orthopaedic translation · 2026Review
- Siteomix: a PyMOL plugin for the detection, alignment, and similarity analysis of protein‒ligand binding sites.Journal of computer-aided molecular design · 2026Article
- Artificial intelligence in drug discovery - what it is, where we stand and the path forward.Nature reviews. Drug discovery · 2026Review
- The Pharmacology of Bacterial Persistence: From Antibiotic Tolerance to Antimicrobial Resistance.Antibiotics (Basel, Switzerland) · 2026Review
- Improving Generalizability in Whole-Cell Antibiotic Discovery Through Active Learning.bioRxiv : the preprint server for biology · 2026Article
- Deep learning-enabled discovery of antibiotics effective againstScience translational medicine · 2026Article
- Target-driven machine learning-enabled virtual screening (TAME-VS) enables prioritization of AKR1C3 inhibitors.Communications chemistry · 2026Article
- Harnessing artificial intelligence for antimicrobial discovery and optimization.Current opinion in microbiology · 2026Review
- Framework for evaluating explainable AI in antimicrobial drug discovery.Journal of cheminformatics · 2026Article
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- A General Group Testing Strategy for Discovering Chemical Cooperativity.Angewandte Chemie (International ed. in English) · 2026Article
- Artificial Intelligence at the Intersection of Chemistry and Materials Science.AI (Basel, Switzerland) · 2026Article
- Drug Discovery Strategies for Kallikrein-Related Peptidases.International journal of molecular sciences · 2025Review
Corrections and comments
- Erratum issued
Authors and funding
20 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increase in multidrug-resistant bacteria underscores an urgent need for additional antibiotics. Here, we integrate small-molecule high-throughput screening with a deep-learning-based virtual screening approach to uncover new antibacterial compounds. We screen ~2 million small molecules against a sensitized Escherichia coli strain, yielding thousands of hits. We use these data to train a deep learning model, GNEprop, to predict antibacterial activity, retrospectively validating robustness with respect to out-of-distribution generalization and activity cliff prediction. Virtual screening of over 1.4 billion synthetically accessible compounds identifies potential candidates, of which 82 exhibit antibacterial activity on the same strain, illustrating a 90-fold improved hit rate over the high-throughput screening experiment used for training. Many newly identified compounds exhibit high dissimilarity to known antibiotics, potency beyond the training bacterial strain and selectivity. Biological characterization identifies specific, validated targets, indicating promising avenues for further exploration in antibiotic discovery.
Indexed as
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.