ArticleCell chemical biology2024
Discovery of antibiotics that selectively kill metabolically dormant bacteria.
Article in Cell chemical biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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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
35 citing papers in PubMed.
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- Artificial Intelligence-Guided Phenotypic Drug Repurposing Against Streptococcus pneumoniae.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Machine Learning-Guided Discovery of Bacterial-Selective Membrane-Active Compounds Reveals Mechanistic Bias in Antibiotic Training Datasets.bioRxiv : the preprint server for biology · 2026Article
- Generation of antifungals to combat drug resistance using language models and diffusion models.Journal of advanced research · 2026Article
- Selective Reprogramming of Ribosome Assembly and Translational Control during Bacterial Growth Phase Transitions.ACS omega · 2026Article
- Deep learning-enabled discovery of antibiotics effective againstScience translational medicine · 2026Article
- Harnessing artificial intelligence for antimicrobial discovery and optimization.Current opinion in microbiology · 2026Review
- Peering Inside the Black Box: Explainable AI to Interpret Advanced Computer Vision Fungal Pathogen Prediction.Scientific reports · 2026Article
- Targeting metabolism to combat anticancer and antibacterial drug resistance.Trends in pharmacological sciences · 2026Review
- Large-scale testing of antimicrobial lethality at single-cell resolution predicts mycobacterial infection outcomes.Nature microbiology · 2026Article
- Enhancement of antibiotic therapy in osteomyelitis by inhibiting talin-1-mediated bacterial internalization.Frontiers in cellular and infection microbiology · 2026Article
- Impact of Host and Bacterial Metabolism on Antibiotic Susceptibility.Biochemistry · 2025Review
- Neuroimmune interactions in inflammatory bowel disease: Role of intestinal macrophages and the cholinergic pathway.World journal of gastroenterology · 2025Review
- Polymyxin B lethality requires energy-dependent outer membrane disruption.Nature microbiology · 2025Article
- Article
- Machine Learning for Multi-Target Drug Discovery: Challenges and Opportunities in Systems Pharmacology.Pharmaceutics · 2025Review
- A rational approach to discovering new persister control agents.Antimicrobial agents and chemotherapy · 2025Article
- Discovery and optimization of a guanylhydrazone-based small molecule to replace bFGF for cell culture applications.Biochemistry and biophysics reports · 2025Article
- A broad-spectrum antibiotic targets multiple-drug-resistant bacteria with dual binding targets and no detectable resistance.Nature communications · 2025Article
- Heterogeneous efflux pump expression underpins phenotypic resistance to antimicrobial peptides.eLife · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
There is a need to discover and develop non-toxic antibiotics that are effective against metabolically dormant bacteria, which underlie chronic infections and promote antibiotic resistance. Traditional antibiotic discovery has historically favored compounds effective against actively metabolizing cells, a property that is not predictive of efficacy in metabolically inactive contexts. Here, we combine a stationary-phase screening method with deep learning-powered virtual screens and toxicity filtering to discover compounds with lethality against metabolically dormant bacteria and favorable toxicity profiles. The most potent and structurally distinct compound without any obvious mechanistic liability was semapimod, an anti-inflammatory drug effective against stationary-phase E. coli and A. baumannii. Integrating microbiological assays, biochemical measurements, and single-cell microscopy, we show that semapimod selectively disrupts and permeabilizes the bacterial outer membrane by binding lipopolysaccharide. This work illustrates the value of harnessing non-traditional screening methods and deep learning models to identify non-toxic antibacterial compounds that are effective in infection-relevant contexts.
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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.