{
  "abstract": "Molecular probes derived from antimicrobial peptides are powerful tools for diagnosing bacterial infectious diseases and guiding antibiotic therapy. However, existing probes rely on a limited repertoire of natural antimicrobial peptides, leaving the vast sequence space of linear peptides largely unexplored. Here, we used explainable artificial intelligence to design AIGP-3, a de novo peptide vector for fluorescent probes. Conjugated to Nile Red, NR-AIGP-3 exhibits high selectivity for Gram-positive bacteria, combining the environmental sensitivity of the fluorophore with the strong membrane binding of the vector and favorable MIC values for live-cell imaging. NR-AIGP-3 outperforms a ubiquicidin-derived probe in both flow cytometry and confocal microscopy, demonstrating its high potential for developing molecular probes for the diagnosis of bacterial infections.",
  "abstract_shown": true,
  "affiliations": [
    "Laboratoire d’Innovation Thérapeutique, UMR 7200 CNRS/Université de Strasbourg, Strasbourg Drug Discovery and Development Institute (IMS)",
    "Chimie de la Matière Complexe, UMR 7140 CNRS/Université de Strasbourg",
    "Laboratoire de Bioimagerie et Pathologies, UMR 7021 CNRS/Université de Strasbourg",
    "Institute for Chemical Reaction Design and Discovery (WPI-ICReDD, Hokkaido University, Kita 21, Sapporo, Hokkaido, Japan, 001-0021",
    "Institut Universitaire de France (IUF)"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Anthony Augé"
    },
    {
      "affs": [
        1
      ],
      "name": "Evgeniia Milova"
    },
    {
      "affs": [
        1
      ],
      "name": "Alexey Orlov"
    },
    {
      "affs": [
        0
      ],
      "name": "Hugo Debelvalet"
    },
    {
      "affs": [
        0
      ],
      "name": "Sergio Ortiz"
    },
    {
      "affs": [
        2
      ],
      "name": "Dmytro Dziuba"
    },
    {
      "affs": [
        1
      ],
      "name": "Dragos Horvath"
    },
    {
      "affs": [
        1,
        3
      ],
      "name": "Alexandre Varnek"
    },
    {
      "affs": [
        0,
        4
      ],
      "name": "Julie Karpenko",
      "orcid": "0000-0002-7633-3691"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ad7g9cerpxm9x.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009498/v1",
  "fulltext_url": null,
  "id": "ad7g9cerpxm9x",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009498/v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2404|Antimicrobial Peptides and Activities"
    ]
  ],
  "openalex": "W7214598302",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009498/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-27",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/ad7g9cerpxm9x.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15009498",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009498/v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2404|Antimicrobial Peptides and Activities",
      "subfield": "microbiology"
    }
  ],
  "title": "AI-Generated Peptide Vectors: Advancing Fluorescent Detection of Bacteria",
  "title_text": "AI-Generated Peptide Vectors: Advancing Fluorescent Detection of Bacteria",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/ad7g9cerpxm9x",
  "venue": "ChemRxiv",
  "versions": []
}