ToDNA: Preference-Programmable Test-Time Optimization for Regulatory DNA Design

Restricting gene expression to target cell types is a central goal in regulatory DNA design for gene therapy and synthetic biology. While genomic foundation models capture rich sequence regularities, their pre-trained priors do not explicitly enforce the differential activity across cellular contexts that defines cellular selectivity. Although reward-based post-training can encode such context-specific preferences into model parameters, adapting to new design objectives often necessitates costly re-training. To address this limitation, we introduce , a novel test-time optimization framework that cleanly decouples a reusable regulatory sequence prior from cell-specific functional objectives. Specifically, ToDNA employs target-agnostic low-rank adaptation (LoRA) to specialize a base model (Evo 2) to specific regulatory-element classes, while leveraging a frozen multi-cell predictor to guide nucleotide-logit updates, regularized against a fixed local reference. This modular separation allows target-to-off-target preferences to be dynamically re-configured via reward functions without retraining the generative backbone. Evaluated across four enhancer and promoter design tasks, ToDNA demonstrates that candidate optimization substantially boosts enhancer design specificity, whereas promoter responsiveness exhibits more modest gains. Sequence-level analyses further reveal that stronger motif agreement does not necessarily align with higher predicted cell specificity. Crucially, we show that alternative reward configurations can redirect cell-activity preferences purely at inference time, though their effectiveness varies depending on the interplay between activity and sequence-level objectives. These findings establish the viability of reusable, inference-time control for regulatory DNA design, while highlighting objective formulation and predictor validity as key determinants of true cellular selectivity.