Structure-Guided RNA Design via Multinomial Diffusion

Abstract The structure of RNA plays an essential role in its biological function, with the secondary structure being a key determinant. While significant advances have been made in RNA folding models, the inverse problem, i.e., generating sequences that fold into a target structure has received less attention. Existing approaches commonly rely on iterative optimization, often guided by a folding oracle, which can limit scalability and risk converging to local optima. Diffusion models have shown a superior performance in many application areas, but their noising process is not directly applicable to discrete nucleotide sequences. In this paper, we adapt the multinomial diffusion framework, which introduces discrete noise via categorical transitions, to the RNA sequence space. Furthermore, we incorporate secondary structure information to guide the generative process toward sequences that fold into a desired target structure. For this purpose, we evaluate two architectural variants: (i) modeling the problem either by projecting the sequence embedding into a 2D pairwise representation that reflects the structure of a contact map, or (ii) by restructuring the architecture as a purely 1D sequence model. Our results show that both approaches generate sequences that fold with high accuracy while maintaining substantial variability among generated sequence samples. We further evaluated constrained generation via sequence inpainting, resulting in performance comparable to unconstrained generation. Even though the performance on the human-designed Eterna100 benchmark was lower than that of the state-of-the-art methods LEARNA and SAMFEO, our results indicate that diffusion-based models provide a promising and scalable framework for conditional nucleotide sequence generation.