
























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 |
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)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。