Unlike proteins, which have a wealth of validated structural data, experimentally or computationally validated DNA origami datasets are limited. Here we present a graph neural network that can predict the three-dimensional conformation of DNA origami assemblies both rapidly and accurately. We develop a hybrid data-driven and physics-informed approach for model training, designed to minimize not only the data-driven loss but also the physics-informed loss. By employing an ensemble strategy, the model can successfully infer the shape of monomeric DNA origami structures almost in real time. Further refinement of the model in an unsupervised manner enables the analysis of supramolecular assemblies consisting of tens to hundreds of DNA blocks. The proposed model enables an automated inverse design of DNA origami structures for given target shapes. Our approach facilitates the real-time virtual prototyping of DNA origami, broadening its design space.

与拥有大量经过验证的结构数据的蛋白质不同，经过实验或计算验证的 DNA 折纸数据集是有限的。在这里，我们提出了一种图神经网络，可以快速准确地预测 DNA 折纸组件的三维构象。我们开发了一种混合数据驱动和物理知情的模型训练方法，旨在最大程度地减少数据驱动的损失和物理通知的损失。通过采用集成策略，该模型几乎可以实时成功推断单体 DNA 折纸结构的形状。以无人监督的方式进一步完善模型可以分析由数十到数百个 DNA 块组成的超分子组装体。所提出的模型能够对给定目标形状的 DNA 折纸结构进行自动逆向设计。我们的方法促进了 DNA 折纸的实时虚拟原型制作，拓宽了其设计空间。