CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning

ArXi:2603.21743v1 Announce Type: new 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.