Rotation Operators for Single-Cell Perturbation Response Prediction

Predicting how cells respond to genetic and chemical perturbations is becoming central to mechanistic biology and drug discovery. However, the space of possible combinations grows quadratically, and identifying which pairs interact non-additively before measuring them remains an open problem. To address this, we introduce OpPert, a new deep learning framework that models each perturbation as a norm-preserving rotation of the cell's latent representation. To model combinations of two perturbations, their rotations compose into a pair prediction via Baker-Campbell-Hausdorff, and the resulting Lie bracket gives their interaction strength. This interaction score is computed from single-perturbation operators alone, without supervision from the candidate pair, and we show it correlates with measured non-additivity. To infer effects of unseen perturbations, a flow-matching model generates their rotations, with sampling variance serving as a confidence estimate. We validate OpPert computationally on known benchmarks and experimentally in the wet lab. Across held-out evaluations on three genetic screens (Norman, K562, RPE1), OpPert achieves 81-88% directional accuracy, above existing approaches. We further evaluate OpPert on a small-molecule screen and validate predicted Belinostat targets by RT-qPCR in MCF7 cells, elucidating apoptotic regulators never previously associated with this drug.