Designing Cell-Type-Specific Regulatory DNA with Annealed Diffusion Beam Search

Designing regulatory DNA with cell-type-specific activity is broadly relevant for cell engineering and gene therapy. An ideal AI-designed sequence should drive high expression in desired cell types, have minimal activity in undesired cell types, and adhere to the genome's natural regulatory grammar. Diffusion models have become powerful priors for DNA sequence generation. Inference-time search guided by predictive oracles can steer these models toward high specificity. However, activity scoring requires complete DNA sequences, which must be derived heuristically from partially masked intermediates during search. Positionwise maximum a posteriori (MAP) estimation provides an efficient heuristic for completing these intermediates and computing lookahead scores. Here, we report that these scores are unreliable early in denoising and their reliability improves non-uniformly as sequence context accumulates. Based on this observation, we developed Progress-Annealed Candidate Selection (PACS), a training-free method that schedules how strongly MAP scores influence selection within diffusion beam search. PACS standardizes scores within each parent's candidate pool and increases selection strength with the expected fraction of revealed nucleotides, allowing broader exploration early and a stronger preference for high-scoring sequences later. We provide a local KL-regularized interpretation of the selector, characterize its limiting cases, and establish large-pool consistency under stated assumptions. Across three human cell lines, PACS improves sequence fidelity by 12–16% relative to greedy selection while maintaining high predicted specificity. On an eight-cell-type immune benchmark, PACS outperforms most of the evaluated reinforcement learning, inference-time alignment, and iterative optimization methods.