ActFlow: Activation-Aware Flow Matching for Single-Cell Perturbation Prediction

Single-cell perturbation prediction is central to building virtual cell models for gene function analysis and drug discovery. However, single-cell expression profiles are highly sparse, with most genes inactive in each cell, causing conventional regression objectives to be dominated by zero-expression entries. This may obscure sparse yet biologically informative activation signals. To address this challenge, we propose ActFlow, a diffusion transformer-based framework that models perturbation-induced cellular transitions via continuous-time flow matching. During training, ActFlow constructs a cell-specific mask for each perturbed cell and jointly learns two velocity fields within a unified model: a target velocity field that predicts velocities in gene expression space under perturbation conditions, and an auxiliary mask velocity field that models gene activation states. The mask velocity field contributes activation-aware supervision exclusively during training, encouraging the model to focus on biologically relevant activated genes; at inference, only the target expression trajectory is retained as the predicted output. Experiments on genetic and drug perturbation benchmarks show that ActFlow consistently outperforms state-of-the-art methods across both global reconstruction and distribution-level metrics, demonstrating that explicitly modeling gene activation states provides an effective inductive bias for single-cell perturbation prediction.