ArticleNature materials2024
Prediction of DNA origami shape using graph neural network.
Article in Nature materials, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Tools For Building Artificial Biological Nanostructures.ACS nano · 2026Review
- Effect of asymmetry on frustrated crystallization.Scientific reports · 2026Article
- De novo design of DNA origami with a generative diffusion model.Nature communications · 2026Article
- DNA Origami and Their Application in Biosensors.Biosensors · 2026Review
- DNA Origami and Its Applications in Synthetic Biology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- SNUPI: A Computational Framework for Rapid Mechanical Analysis of Structured DNA Assemblies.JACS Au · 2025Article
- Review
- Evolving Data-Driven Strategies for the Characterization of Supramolecular Polymers and Systems.Angewandte Chemie (International ed. in English) · 2025Review
- Automated design of scaffold-free DNA wireframe nanostructures.Nature communications · 2025Article
- Precise nanoscale fabrication technologies, the "last mile" of medicinal development.Acta pharmaceutica Sinica. B · 2025Review
- Article
- Generative design-enabled exploration of wireframe DNA origami nanostructures.Nucleic acids research · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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.
Indexed as
Identifiers
38486095What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.