GenInv: Generative Inverse Learning for Cell Perturbation Prediction

Predicting how drugs alter gene expression across cellular contexts can guide the selection of treatments for experimental screening. Large single-cell perturbation screens provide a basis for learning these responses, but measure separate control and treated populations without observing individual cells before and after treatment. Recent models address this constraint by learning transitions between populations. Their forward objective directly supervises the treated distribution, yet does not require the predictor to recover the untreated context represented in those observations. This suggests a complementary learning signal: predicting controls from observed treated cells. We introduce GenInv, a generative inverse learning approach that jointly supervises forward response prediction and reverse control prediction through a shared network. Both tasks use the same experimental measurements, and inference retains the original forward procedure. On five evaluation cell lines in State-Tahoe-Filtered, GenInv improves all four primary response metrics over a matched forward-only model. Mean response correlation (Pearson-$\Delta$) increases from 0.922 to 0.931, and differential-expression log-fold-change Spearman correlation increases from 0.612 to 0.665. Label interventions show that reverse prediction retains an expression-level benefit under shuffled inverse labels, while correct conditioning further improves differential-expression recovery. These results establish generative inverse supervision as an effective training principle in this setting, extending the predictive value of existing perturbation measurements. Code will be available at https://anonymous.4open.science/r/geninv/.