CellBridge: Biologically Faithful Cell Morphology Prediction via Source-Conditioned Schrodinger Bridges

Cellular Morphology Perturbation Prediction (CMPP) is a fundamental capability toward building predictive virtual cells, aiming to simulate how cellular phenotypes change in response to chemical or genetic interventions. Such predictive models have the potential to accelerate therapeutic discovery by reducing experimental perturbation screening and enabling systematic exploration of cellular response mechanisms. However, existing generative approaches insufficiently account for the source-dependent nature of cellular state transitions and the biological mechanisms which connect perturbations to phenotypic responses. To fill these gaps, we propose CellBridge, a source-conditioned Schrödinger Bridge framework for modeling cellular morphological responses to chemical and genetic perturbations. CellBridge consists of two key components: i) modeling perturbation response as a bridge from an observed control cell state to its perturbed phenotype, with the control image supplied to the network at every step, and ii) incorporating structured compound-target-pathway conditioning to connect molecular perturbations with their downstream biological programs. For the challenging generalization setting, where structurally distinct compounds can induce similar phenotypes through shared biological mechanisms, the pathway representation enables CellBridge to move beyond scaffold similarity and capture mechanism-related responses. Comprehensive experiments on BBBC021, RxRx1, and JUMP demonstrate that CellBridge consistently improves perturbation-specific phenotype recovery across various settings including chemical compound and genetic perturbations.