APOD: reasoning-guided agentic population ordinary differential equation discovery for pharmacological digital twins
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-...
| Authors | Romain Ferrara, Martin Soucail, Victor Gertner et al. |
|---|---|
| Primary category | q-bio.QM |
| Published | 5 October 2026 |
| Last updated at source | 5 October 2026 |
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