{
  "abstract": "Antibiotic resistance is rising globally, demanding faster, more reliable routes to design antimicrobial candidates. Although artificial-intelligence-based methods have accelerated antimicrobial discovery, most are designed to screen fixed libraries or generate candidates broadly, rather than optimize existing peptide scaffolds under practical design constraints. Here, to address this challenge, we present APEX generative optimization (ApexGO). ApexGO uses a transformer variational autoencoder that embeds peptide sequences in a continuous latent space, whereas Bayesian optimization efficiently proposes sequence edits to boost antimicrobial potency. Unlike traditional approaches, ApexGO generates peptide sequences through modifications of template peptides, opening avenues for peptide design and antibiotic discovery. Using ten peptides as templates, ApexGO generated optimized derivatives with enhanced antimicrobial properties. We chemically synthesized 100 of these compounds and conducted comprehensive in vitro characterizations, including assessments of antimicrobial activity, mechanism of action, secondary structure and cytotoxicity. In particular, ApexGO achieved an 85% ground-truth experimental hit rate and a 72% success rate in enhancing antimicrobial activity against Gram-negative pathogens, outperforming previously reported methods for antibiotic discovery and optimization. In two preclinical mouse models of Acinetobacter baumannii infection, artificial-intelligence-optimized molecules exhibited potent anti-infective activity superior to their template controls and comparable with or exceeding that of last-resort antibiotic. These findings highlight the potential of ApexGO as a generative artificial intelligence approach for peptide design and antibiotic optimization, offering a powerful tool to accelerate antibiotic discovery.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA USA",
    "Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA",
    "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",
    "Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA USA",
    "Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA USA"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0,
        1,
        2,
        3
      ],
      "name": "Marcelo D. T. Torres",
      "orcid": "0000-0002-6165-9138"
    },
    {
      "affs": [
        4
      ],
      "name": "Yimeng Zeng",
      "orcid": "0009-0001-9676-0893"
    },
    {
      "affs": [
        0,
        1,
        2,
        3
      ],
      "name": "Fangping Wan",
      "orcid": "0000-0003-1647-3278"
    },
    {
      "affs": [
        4
      ],
      "name": "Natalie Maus",
      "orcid": "0000-0002-6616-8506"
    },
    {
      "affs": [
        4
      ],
      "name": "Jacob R. Gardner",
      "orcid": "0000-0003-1897-8384"
    },
    {
      "affs": [
        0,
        1,
        2,
        3
      ],
      "name": "César de la Fuente‐Núñez",
      "orcid": "0000-0002-2005-5629"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1w8z6wteww6ae.bib",
  "cited_by": 37,
  "doi": "10.1038/s42256-026-01237-5",
  "fulltext_url": null,
  "id": "1w8z6wteww6ae",
  "issns": [
    "2522-5839"
  ],
  "landing_url": "https://doi.org/10.1038/s42256-026-01237-5",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2404|Antimicrobial Peptides and Activities"
    ]
  ],
  "openalex": "W7161005196",
  "pdf_url": "https://www.nature.com/articles/s42256-026-01237-5.pdf",
  "pmcid": "PMC13201158",
  "pmid": "42206144",
  "published": "2026-05-13",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/1w8z6wteww6ae.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:26:51Z",
      "native_id": "W7161005196",
      "source": "openalex",
      "url": "https://doi.org/10.1038/s42256-026-01237-5"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2404|Antimicrobial Peptides and Activities",
      "subfield": "microbiology"
    }
  ],
  "title": "A generative artificial intelligence approach for peptide antibiotic optimization",
  "title_text": "A generative artificial intelligence approach for peptide antibiotic optimization",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T13:20:46Z",
  "url": "https://research.draconitic.com/paper/1w8z6wteww6ae",
  "venue": "Nature Machine Intelligence",
  "versions": []
}