PerturbSB: Learning Single-Cell Perturbation Dynamics with Control-Informed Schrödinger Bridge

Single-cell perturbation data are central to understanding cellular mechanisms and applications such as drug discovery. Yet perturbation effects vary across cellular states, making it difficult to predict responses in states without treated observations. We propose PerturbSB, a Schr\"odinger-bridge-inspired framework for transferring perturbation responses across cellular contexts. Its stochastic dynamics separate a learned intrinsic drift, shared by control and treated cells, from a state-dependent perturbation drift. Target control cells provide the initial distribution, while training matches predicted treated endpoints to observed treated populations in source contexts. Across three single-cell benchmarks, PerturbSB delivers competitive response predictions and outperforms established baselines on several evaluation measures. These results suggest that separating intrinsic and perturbation dynamics can help predict responses in unseen cellular states.