用连续剂量控制细胞形态变化，实现精准药物反应预测。
Joint Flow Matching Enables Continuous Dose-Conditioned Cell Morphing
- 联合流匹配同时建模细胞潜空间与药物浓度，支持连续剂量调控。
- 在两种化合物上表现优于或相当基线模型，且可估计浓度。
- 适合需要精细剂量控制的药物研发与单细胞分析场景。
生成建模在预测化学化合物处理下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无显式浓度处理的分布到分布映射，要么将浓度视为离散类别标签，无法实现连续剂量控制。本文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潜变量与药物浓度，利用流匹配的可逆性实现剂量条件下的单细胞形态演化。该联合形式在潜空间中诱导出单调的剂量-响应几何结构，并支持从细胞形态反推浓度。作为概念验证，我们进一步展示了对训练中未见剂量的泛化能力。实验表明，本方法在两种化合物上相较于代表性基线在各浓度下的指标表现达到竞争性或更优水平，同时具备离散分类方法无法实现的结构性能力。
原文摘要 · Abstract (English)
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.
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