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arXiv · 2603.21743

CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning

Dongxia Wu·Shiye Su·Yuhui Zhang·Elaine Sui·Emma Lundberg·Emily B. Fox·Serena Yeung-Levy
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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.

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Dongxia Wu, Shiye Su, Yuhui Zhang, Elaine Sui, Emma Lundberg, Emily B. Fox, Serena Yeung-Levy. 2026-03-23. CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning. https://arxiv.org/abs/2603.21743

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