[2603.21743] CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning

Computer Science > Machine Learning arXiv:2603.21743 (cs) [Submitted on 23 Mar 2026] Title:CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning Authors:Dongxia Wu, Shiye Su, Yuhui Zhang, Elaine Sui, Emma Lundberg, Emily B. Fox, Serena Yeung-Levy View a PDF of the paper titled CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning, by Dongxia Wu and 6 other authors View PDF HTML (experimental) 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); Quantitati...