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CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning
Dongxia Wu, · 2026-05-23 · via cs.LG updates on arXiv.org

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Abstract:Building virtual cells with generative models to simulate cellular behavior in silico is emerging as a promising paradigm for accelerating drug discovery. However, prior image-based generative approaches can produce implausible cell images that violate basic physical and biological constraints. To address this, we propose to post-train virtual cell models with reinforcement learning (RL), leveraging biologically meaningful evaluators as reward functions. We design seven rewards spanning three categories-biological function, structural validity, and morphological correctness-and optimize the state-of-the-art CellFlux model to yield CellFluxRL. CellFluxRL consistently improves over CellFlux across all rewards, with further performance boosts from test-time scaling. Overall, our results present a virtual cell modeling framework that enforces physically-based constraints through RL, advancing beyond "visually realistic" generations towards "biologically meaningful" ones.
Subjects: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
Cite as: arXiv:2603.21743 [cs.LG]
  (or arXiv:2603.21743v4 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2603.21743

arXiv-issued DOI via DataCite

Submission history

From: Dongxia Wu [view email]
[v1] Mon, 23 Mar 2026 09:33:18 UTC (10,037 KB)
[v2] Tue, 24 Mar 2026 08:41:38 UTC (10,037 KB)
[v3] Sun, 5 Apr 2026 09:48:41 UTC (10,037 KB)
[v4] Wed, 20 May 2026 19:52:16 UTC (10,027 KB)