scBridge: Stochastic Distribution Transport for Single-Cell Drug Response Prediction

Single-cell drug response prediction is limited by the scarcity of labeled scRNA-seq data, motivating knowledge transfer from large-scale bulk pharmacogenomic datasets. However, substantial distribution shifts between bulk and single-cell transcriptomes make such transfer challenging. Existing approaches are largely representation-centric, aligning the two domains in a shared latent space without explicitly modeling the transition between their distributions. To address this limitation, we formulate bulk-to-single-cell adaptation as an unpaired stochastic distribution transport problem and propose scBridge, a diffusion-based framework for single-cell drug response prediction. scBridge first learns compact latent representations and performs coarse covariance alignment to provide a stable cross-domain initialization. Drawing on Diffusion Schrödinger Bridge, scBridge then models a continuous stochastic transition between bulk and single-cell latent distributions through coupled forward and backward dynamics, optimized alternately via iterative proportional fitting with cycle-consistency regularization. Confidence-aware pseudo-label refinement further adapts the transported representations for downstream prediction. Across 12 scRNA-seq datasets, scBridge achieves a mean AUROC of 0.87, with a 9.33% relative improvement over the second-best model. The learned transport reduces domain separability by 56.8%–77.9% and reveals heterogeneous cell-level transport trajectories with distinct temporal correction dynamics across datasets. Model attribution further identifies biologically relevant mechanisms associated with BRAF inhibitor resistance. These results support stochastic distribution transport as an effective perspective for single-cell drug response prediction across modalities.