De novo design of DNA origami with a generative diffusion model

Generative models that have advanced inverse design in protein engineering can be extended to DNA origami to explore broader design spaces enabling complex geometries and functions for emerging nanotechnologies. However, progress in generative DNA origami design has been limited by the lack of large, standardized structural datasets containing structural information. To address this challenge, we introduce a diffusion-based generative design framework trained on simulated equilibrium conformations obtained using a multiscale computational model. Given user-defined target geometries, our model produces physically plausible DNA origami designs through guided diffusion sampling and strand routing, with integrated structure prediction for quantitative evaluation. Among more than 100 generated candidates, selected structures are experimentally validated, demonstrating proper folding and functional behaviors such as auxetic transformation and modular assembly. Our results highlight the potential of generative modeling for complex DNA origami design, expanding the accessible design space and facilitating the creation of sophisticated, reconfigurable architectures. AI-based inverse design of DNA origami has been limited by the scarcity of large, standardised datasets. Here, the authors present a diffusion-based design framework trained on simulated equilibrium conformations, highlighting the promise of generative AI models empowered by physical simulation for de novo design of DNA origami.