发表机构
University of Zurich; University Children’s Hospital Zurich（苏黎世大学; 苏黎世大学儿童医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出联合流匹配方法，通过双时间步公式建模细胞隐变量与药物浓度，实现连续剂量条件下的单细胞变形，在两种化合物的各浓度指标上表现优于或媲美基线，还具备浓度估计等离散方法无法实现的能力。
AI 中文摘要
生成式建模在预测化学化合物处理下的细胞扰动效应方面展现出日益增长的潜力。现有方法要么将扰动建模为分布到分布的映射而未显式处理浓度，要么将浓度视为离散类别标签，无法实现连续剂量控制。我们提出一种联合流匹配方法，通过双时间步公式同时建模细胞隐变量与药物浓度，利用流匹配的可逆性实现剂量条件下的单细胞变形。该联合公式在隐空间中诱导出单调的剂量响应几何结构，还支持从细胞形态估计浓度。作为概念验证，我们进一步证明该方法能泛化到训练时保留的未见过剂量。实验表明，与代表性基线相比，我们的方法在两种化合物上的各浓度指标达到了有竞争力或更优的表现，同时具备离散类别方法在结构上无法实现的能力。
英文摘要
Generative modeling has shown increasing promise for predicting cellular perturbation effects under chemical compound treatments. Existing approaches either model perturbation as a distribution-to-distribution mapping without explicit concentration handling, or treat concentration as a discrete class label, precluding continuous dose control. We introduce a joint flow matching approach that simultaneously models cell latents and drug concentration via a dual-timestep formulation, enabling dose-conditioned single-cell morphing through the invertibility of flow matching. The joint formulation induces a monotonic dose-response geometry in latent space and additionally supports concentration estimation from cell morphology. As proof of concept, we further demonstrate generalization to an unseen dose held out during training. Empirically, our method achieves competitive or improved per-concentration metrics on two compounds compared with representative baselines, while enabling capabilities structurally unavailable to discrete-class methods.
