SDFM: Cross-Aware Knowledge-Conditioned Flow Matching for Single-Cell Genetic Perturbation Prediction

Genetic perturbation screens reveal how interventions reshape cell populations, but their limited coverage leaves much of the intervention space unexplored. Predicting unmeasured responses requires generalization across perturbations while preserving cellular diversity. Flow matching provides a natural framework for modeling such population-level transformations, yet its local velocity objective only indirectly supervises the responses induced by complete generative trajectories. We propose SDFM, a cross-aware knowledge-conditioned flow matching framework for perturbation response prediction. Knowledge graph representations from STRING and Gene Ontology condition the velocity field through a cross-aware perturbation representation, allowing related perturbations and combinatorial contexts to influence learned transport dynamics. Mini-batch optimal transport establishes population-level correspondences between unpaired control and perturbed cells, while full-rollout supervision connects local velocity learning with predicted endpoints and population expression shifts. Experiments on Adamson, HepG2, and Norman show that SDFM achieves the highest mean all-gene expression change Pearson correlation among evaluated methods across the three single-gene benchmarks and improves combinatorial response prediction. It also reduces mean sliced Wasserstein and energy distances relative to a uniform mean-shift baseline, supporting its ability to model both average transcriptional effects and heterogeneous population-level responses.