{
  "abstract": "Lipid nanoparticle (LNP) delivery constructs provide a precise and targeted therapeutic platform for nucleic-acid and subunit vaccines. By shielding fragile antigens and genetic cargo from premature degradation, these constructs help vaccines overcome the physiological barriers that separate an injection site from its intracellular target, increasing specificity, potency, and the precision of controlled release. This capability has broadened the range of immunomodulatory targets in the fight against infectious disease and has opened the door to vaccines that treat tumors and cancer. Increasingly, artificial intelligence (AI) and machine learning (ML) are being applied to predict the LNP formulations that maximize mRNA-vaccine efficacy, and the same computational strategies are now being extended across diverse vaccine modalities. By approximating in vivo pharmacokinetics and pharmacodynamics, in silico methods promote rational, data-driven design that is reshaping the drug-development pipeline. In this Review we summarize current literature on the optimization of LNP delivery constructs for vaccine therapeutics using ML, survey the rapid clinical translation of LNP-mRNA technology since 2021, and discuss the limitations and predictive capabilities of computer-simulated experimental models.",
  "abstract_shown": true,
  "affiliations": [
    "Tandon School of Engineering",
    "New York University",
    "College of Osteopathic Medicine",
    "Touro University",
    "School of Medicine",
    "Tel Aviv University",
    "Department of Immunology",
    "Montefiore Medical Center"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Eric H. Rosenn",
      "orcid": "0009-0000-6084-8933"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Max Engel",
      "orcid": "0009-0002-3435-0693"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Violet Munarova"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Maya Lipshitz",
      "orcid": "0009-0002-0363-399X"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Nata Edell",
      "orcid": "0009-0003-4294-6451"
    },
    {
      "affs": [
        4,
        5
      ],
      "name": "Dania Halperin",
      "orcid": "0000-0001-5108-6930"
    },
    {
      "affs": [
        6,
        7
      ],
      "name": "Eryn Fox"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/etmy0w0p6cxg1.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009316/v1",
  "fulltext_url": null,
  "id": "etmy0w0p6cxg1",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009316/v1",
  "language": null,
  "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",
      "1312|RNA Interference and Gene Delivery"
    ]
  ],
  "openalex": "W7214081802",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009316/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-22",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/etmy0w0p6cxg1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15009316",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009316/v1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|RNA Interference and Gene Delivery",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Computer Learning Can Streamline Development of Lipid Nanoparticle Vaccine Delivery Constructs",
  "title_text": "Computer Learning Can Streamline Development of Lipid Nanoparticle Vaccine Delivery Constructs",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/etmy0w0p6cxg1",
  "venue": "ChemRxiv",
  "versions": []
}