ArticleNature chemical biology2023
Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii.
Article in Nature chemical biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 171 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
171 citing papers in PubMed, 3 syntheses or guidelines pooled it, 294 citations in OpenAlex.
- Data-driven Atlas ofFrontiers in cellular and infection microbiology · 2026Pooled it
- Antimicrobial resistance crisis: could artificial intelligence be the solution?Military Medical Research · 2024Pooled it
- Harnessing AI for advancing pathogenic microbiology: a bibliometric and topic modeling approach.Frontiers in microbiology · 2024Pooled it
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- AI-Driven Synthesis in Medicinal Chemistry: Integrating Large Language Models, Robotic Automation, and Sustainability Metrics to Accelerate Drug Discovery.Medicinal research reviews · 2026Review
- Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- 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
- Bridging the AI Divide in Drug Discovery: Practical Lessons from Capacity Strengthening in Africa.Journal of medicinal chemistry · 2026Article
- Mechanistic basis of LolDF-mediated lipoprotein transport in Acinetobacter.Nature communications · 2026Article
- Addressing antimicrobial resistance: Current challenges, emerging strategies, and an AI-powered, community-driven approach.PNAS nexus · 2026Review
- Breaking the antimicrobial resistance dilemma: marine natural products, a treasure trove for the development of new anti-Marine life science & technology · 2026Review
- Conformational hijacking of lipoprotein transporter LolDF enables precision antimicrobial activity against Acinetobacter baumannii.Nature communications · 2026Article
- Antimicrobial Resistance as a Global Public Health Challenge: Epidemiological Burden, Bioethical Dimensions and Emerging Therapeutic Strategies.Infectious disease reports · 2026Review
- Improving Generalizability in Whole-Cell Antibiotic Discovery Through Active Learning.bioRxiv : the preprint server for biology · 2026Article
- Deep-learning-based virtual screening of antibacterial compounds.Nature biotechnology · 2026Article
- Covalent Inhibitors in Antimicrobial Drug Development-Beyond β-Lactams.Molecules (Basel, Switzerland) · 2026Review
- Identification of drug candidates against glioblastoma with machine learning and high-throughput screening of heterogeneous cellular models.Digital discovery · 2026Article
- Deep learning-enabled discovery of antibiotics effective againstScience translational medicine · 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
111 more citing papers are in PubMed but not listed here.
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Authors and funding
17 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acinetobacter baumannii is a nosocomial Gram-negative pathogen that often displays multidrug resistance. Discovering new antibiotics against A. baumannii has proven challenging through conventional screening approaches. Fortunately, machine learning methods allow for the rapid exploration of chemical space, increasing the probability of discovering new antibacterial molecules. Here we screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. We trained a neural network with this growth inhibition dataset and performed in silico predictions for structurally new molecules with activity against A. baumannii. Through this approach, we discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii. Further investigations revealed that abaucin perturbs lipoprotein trafficking through a mechanism involving LolE. Moreover, abaucin could control an A. baumannii infection in a mouse wound model. This work highlights the utility of machine learning in antibiotic discovery and describes a promising lead with targeted activity against a challenging Gram-negative pathogen.
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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.