{
  "abstract": "Establishing ordinary differential equations (ODEs) describing population data is a fundamental part of mathematical modeling in pharmacology, crucial to developing digital twins. However, doing so from sparse, noisy data is a slow, expert-driven task. Existing automated methods either search a restricted model space or ignore population inter-individual variability. Here we introduce APOD (Agentic Population ODE Discovery), a language-model agent that iteratively reasons over biological knowledge and fit diagnostics in an open-ended search space to discover a population digital twin (PDT), i.e., a shared ODE system with between-subject variability. On synthetic pharmacokinetic and tumor-dynamics benchmarks, APOD recovered ground-truth structures in 94-100\\% of runs, 12-fold faster in median than an established library-based search. On real cohorts it converged to valid structures, and proposed a PDT of radioligand-therapy-induced platelet dynamics that predicts thrombocytopenia from first-cycle data and simulates alternative dosing schedules that lower the predicted risk of toxicity.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.06227",
  "author_total": 10,
  "authors": [
    {
      "name": "Romain Ferrara"
    },
    {
      "name": "Martin Soucail"
    },
    {
      "name": "Victor Gertner"
    },
    {
      "name": "Adil Moussali"
    },
    {
      "name": "Joris Cocquebert"
    },
    {
      "name": "Sandrine Oziel-Taieb"
    },
    {
      "name": "Julien Nicolas"
    },
    {
      "name": "Florence Gattacceca"
    },
    {
      "name": "Mihaela van der Schaar"
    },
    {
      "name": "Sébastien Benzekry"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3mtz80bd1p1fd.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.06227",
  "fulltext_url": null,
  "id": "3mtz80bd1p1fd",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.06227",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7220662319",
  "pdf_url": "https://arxiv.org/pdf/2610.06227",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3mtz80bd1p1fd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:11Z",
      "native_id": "2610.06227",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.06227"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "APOD: reasoning-guided agentic population ordinary differential equation discovery for pharmacological digital twins",
  "title_text": "APOD: reasoning-guided agentic population ordinary differential equation discovery for pharmacological digital twins",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:16Z",
  "url": "https://research.draconitic.com/paper/3mtz80bd1p1fd",
  "venue": "arXiv",
  "versions": []
}