{
  "abstract": "Generative AI is transforming drug discovery, yet most approaches follow one-drug-one-target paradigms ill-suited to the heterogeneity of chronic, systemic diseases. Systems pharmacology offers an alternative, but generative tools designed for it remain scarce. We introduce GEM-GPT, a transcriptomics-guided framework that generates personalized therapeutic candidate molecules intended to shift cell type-specific disease states toward healthy phenotypes. GEM-GPT uses a biology-inspired deep fusion architecture that couples a single-cell RNA-sequencing foundation model with a molecular GPT, modeling cell type-specific chemical-gene interactions throughout molecule generation rather than through fixed conditioning. Across bulk and single-cell chemical perturbations and CRISPR knock-out signatures, GEM-GPT outperforms state-of-the-art baselines, produces cell type-resolved molecules, and generalizes to unseen cellular contexts. In a case study on opioid use disorder (OUD), it generates novel candidates, recovers FDA-approved OUD-related drugs absent from training, and yields predicted binders to OUD-related targets. GEM-GPT bridges single-cell omics and molecular generation for personalized, cell-type-resolved, systems-aware therapeutic design.",
  "abstract_shown": true,
  "affiliations": [
    "Northeastern University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "S. Zhang"
    },
    {
      "name": "R. Ohlan"
    },
    {
      "name": "M. Mottaqi"
    },
    {
      "affs": [
        0
      ],
      "name": "L. Xie"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bj3xj91yg7g5z.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.07.17.739269",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.07.17.739269v2.full",
  "id": "bj3xj91yg7g5z",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.07.17.739269v2",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "pharmacology and toxicology"
    ],
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7170039660",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.07.17.739269v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-17",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bj3xj91yg7g5z.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:45Z",
      "native_id": "10.64898/2026.07.17.739269",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.07.17.739269v2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "biorxiv:pharmacology and toxicology",
      "subfield": "pharmacology-toxicology"
    }
  ],
  "title": "GEM-GPT Enables Personalized Cell Type-Resolved Therapeutic Design for Systems Pharmacology",
  "title_text": "GEM-GPT Enables Personalized Cell Type-Resolved Therapeutic Design for Systems Pharmacology",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:45Z",
  "url": "https://research.draconitic.com/paper/bj3xj91yg7g5z",
  "venue": "bioRxiv",
  "versions": []
}