Designing Cell-Type-Specific Regulatory DNA with Guided Discrete Diffusion

Designing regulatory DNA with cell-type-specific activity is broadly relevant for cell engineering and gene therapy. The regulatory activity of DNA elements such as promoters and enhancers emerges from the combinatorial organization of transcription factor binding sites and sequence context, which collectively constitute the genome's regulatory grammar. Deep learning models can effectively predict DNA regulatory activity across cell types, but current generative approaches often produce DNA sequences that strongly deviate from this natural regulatory grammar, increasing the risk of unexpected behavior and off-target effects in real-world applications. Here, we introduce DNA-CRAFT, a method for designing regulatory DNA with high predicted cell-type-specific activity while preserving naturalness. DNA-CRAFT formulates regulatory sequence design as a guided search over a generative model pre-trained on millions of naturally occurring regulatory elements from the human and mouse genomes. Our method enables targeted exploration of DNA sequence space, efficient evaluation of candidate designs, and explicit suppression of unwanted activity. Across benchmarks spanning human cell lines and primary immune cells, DNA-CRAFT outperforms existing generative and optimization-based methods, consistently achieving the best trade-off between cell-type specificity and naturalness in the designed regulatory DNA sequences.