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Quantitative biology

Deep learning speeds the search for new antibiotic scaffolds

Nature Biotechnology (2025)Cite this article

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A deep learning model trained on experimental data from high-throughput screening identifies structurally novel antimicrobial compounds.

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Fig. 1: GNEprop pipeline.

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Author information

Authors and Affiliations

  1. Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria, Australia

    Yumeng Zhang & Jiangning Song

  2. Monash AI Institute, Monash University, Melbourne, Victoria, Australia

    Yumeng Zhang & Jiangning Song

  3. Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Cesar de la Fuente-Nunez

  4. Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA

    Cesar de la Fuente-Nunez

  5. Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA

    Cesar de la Fuente-Nunez

  6. Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA

    Cesar de la Fuente-Nunez

Authors
  1. Yumeng Zhang
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  2. Jiangning Song
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  3. Cesar de la Fuente-Nunez
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Corresponding authors

Correspondence to Jiangning Song or Cesar de la Fuente-Nunez.

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Competing interests

C.d.l.F.-N. is a co-founder of and scientific advisor to Peptaris, Inc., provides consulting services to Invaio Sciences and is a member of the scientific advisory boards of Nowture S.L., Peptidus, European Biotech Venture Builder, the Peptide Drug Hunting Consortium (PDHC), ePhective Therapeutics, Inc. and Phare Bio. All other authors declare no competing interests.

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Zhang, Y., Song, J. & de la Fuente-Nunez, C. Deep learning speeds the search for new antibiotic scaffolds. Nat Biotechnol (2025). https://doi.org/10.1038/s41587-025-02806-6

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  • DOI: https://doi.org/10.1038/s41587-025-02806-6

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