Mechanism-Grounded Flow Matching for Interpretable Single-Cell Perturbation Prediction with Adaptive Transport Dynamics

Single-cell perturbation prediction is essential for systems biology and therapeutic discovery, where interpretability is crucial for both grounding transcriptional responses in biological mechanisms and understanding how mechanisms shape cellular state transitions. Recently, flow matching has emerged as an effective approach for modeling continuous transitions between cellular states. However, existing methods primarily use it to predict perturbed expression profiles, without explicitly grounding the learned transitions in underlying biological mechanisms, thus limiting interpretability. To solve this problem, in this paper, we propose MechFlow, a mechanism-grounded flow matching framework that adaptively models the dynamics of transport from untreated to perturbed states. Specifically, we propose three modules: target-anchored mechanism grounding for perturbation drift propagates perturbation effects through directed biological graphs to derive a mechanism drive toward transcriptional changes, termed the perturbation drift; graph metric adaptation for state-responsive cellular geometry calculates a graph metric to define an adaptive geometry that assigns different costs to directions of transcriptional change according to gene associations, and transport dynamics for interpretable distribution flow integrates the drift and graph geometry to construct interpretable transition flows for supervising velocity field learning in flow matching. Together, these modules enable MechFlow to ground predicted transcriptional responses in biological mechanisms and characterize how they unfold along cellular state transitions. Experiments on Tahoe-100M show that MechFlow achieves not only strong predictive performance under various settings but also interpretability at both the response and transition levels.