CellReprogrammer: Phenotype-Guided Therapeutic Target Discovery for Cell Reprogramming

Therapeutic target discovery requires interventions that improve disease-associated cellular phenotypes while remaining feasible and limiting disruption to other cellular programs. Existing methods often stop at gene ranking or response prediction, while combination approaches may rely on full-transcriptome matching. To address these limitations, we introduce $\text{CellReprogrammer}$, a differentiable framework that directly optimizes combinatorial target sets while jointly considering effectiveness, feasibility, and toxicity. Specifically, CellReprogrammer couples a disease-state classifier that defines phenotype improvement with a perturbation surrogate and a differentiable combination optimizer. The optimizer selects a fixed-size target set, incorporates druggability, penalizes high-risk targets, and caps the magnitude of downstream transcriptional changes, without requiring paired disease–treatment profiles from the target cohort. Across five disorders and three cell types, phenotype-guided optimization substantially reduced downstream transcriptional disruption relative to full-transcriptome matching at matched predicted effectiveness. Across the broader benchmark, CellReprogrammer outperformed 20 baselines in predicted reprogramming effectiveness while selecting compact target sets with 93.7% druggability and lower predicted target and downstream toxicity. Finally, the selected interventions were disease- and cell-type-specific and recovered coherent biological programs and established cross-disease relationships. Together, these results establish phenotype-guided combinatorial optimization as a practical approach to therapeutic target discovery.