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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2603.21743 |
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| _version_ | 1866918515502481408 |
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| author | Wu, Dongxia Su, Shiye Zhang, Yuhui Sui, Elaine Lundberg, Emma Fox, Emily B. Yeung-Levy, Serena |
| author_facet | Wu, Dongxia Su, Shiye Zhang, Yuhui Sui, Elaine Lundberg, Emma Fox, Emily B. Yeung-Levy, Serena |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_21743 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning Wu, Dongxia Su, Shiye Zhang, Yuhui Sui, Elaine Lundberg, Emma Fox, Emily B. Yeung-Levy, Serena Machine Learning Quantitative Methods 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. |
| title | CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning |
| topic | Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2603.21743 |