[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125505-en":3,"doc-seo-125505-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125505,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Predicting DNA Origami Stability in Physiological Media by Machine Learning","DNA origami nanostructures provide programmable, biocompatible platforms for drug delivery and diagnostics, but structural instability in physiological conditions limits practical use. A proof-of-concept framework combines dynamic light scattering (DLS) with machine learning to estimate diffusion-coefficient–based stability responses, addressing the time-consuming and hard-to-generalize nature of traditional image-based or empirical assessments. DLS screening is supported with gel electrophoresis and atomic force microscopy for selected conditions. A dataset of 1400+ measurements covers three DNA origami shapes under physiologically relevant variations in temperature, incubation time, MgCl2, pH, and DNase I, training a consensus model to predict new condition combinations and enabling community benchmarking.","Predicting DNA Origami Stability in Physiological Media by Machine Learning  \nCitation for published version (APA):  \nZubia-Aranburu, J. , Gardin, A. , Paffen, L. , Tollemeto, M. , Alberdi, A. , Termenon, M. , Grisoni, F. , & Patiño Padial, T. (2026) . Predicting DNA Origami Stability in Physiological Media by Machine Learning. Small Structures, 7(2), Article e202500784 . [https://doi.org/10.1002/sstr.202500784](https://doi.org/10.1002/sstr.202500784)  \nDocument license:  \nCC BY  \nDOI:  \n10.1002/sstr.202500784  \nDocument status and date:  \nPublished: 01/02/2026  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.  \n• The final author version and the galley proof are versions of the publication after peer review.  \n• The final published version features the final layout of the paper including the volume, issue and page numbers.  \nLink to publication  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal.  \nIf the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement:  \n[www.tue.nl/taverne](www.tue.nl/taverne)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[openaccess@tue.nl](openaccess@tue.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 26. Apr. 2026  \nSmall Structures  \n[www.small-structures.com](www.small-structures.com)  \n RESEARCH ARTICLE   \nPredicting DNA Origami Stability in Physiological Media by Machine Learning  \nJudith Zubia-Aranburu1,2,3 | Andrea Gardin1 | Lars Paffen1 | Matteo Tollemeto1,4 | Ane Alberdi3 | Maite Termenon3 | Francesca Grisoni1,5  | Tania Patio Padial1   \n1Department of Biomedical Engineering, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands | 2Interdisciplinary Nanoscience Center (iNANO), Aarhus University, Aarhus, Denmark | 3Biomedical Engineering Department, Mondragon University, Arrasate-Mondragon, Spain | 4The Danish National Research Foundation and Villum Foundation’s Center IDUN, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark | 5Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrecht, Utrecht, The Netherlands  \nCorrespondence: Francesca Grisoni ([f.grisoni@tue.nl](f.grisoni@tue.nl)) | Tania Patio Padial (t.patino.padial@tue.nl)  \nReceived: 30 October 2025 | Revised: 21 December 2025 | Accepted: 5 January 2026  \nKeywords: diffusion coefficient | DNA origami | dynamic light scattering | machine learning | stability  \nABSTRACT  \nDNA origami nanostructures offer substantial potential as programable, biocompatible platforms for drug delivery and diagnostics. However, their structural instability under physiological conditions remains a major barrier to practical applications. Stability assessment of DNA origami nanostructures has traditionally relied on image","cbCaisyJeKvjrYHw","https://ap.wps.com/l/cbCaisyJeKvjrYHw","pdf",2340251,1,13,"English","en",105,"# Introduction\n## DNA origami promise and applications\n## Challenge: stability in physiological conditions\n# Methods and dataset overview\n## DLS-ML framework and consensus modeling\n## Experimental support with gel electrophoresis and AFM\n# Results and resources\n## Diffusion coefficient estimation across conditions\n## Dataset and model release for community extension","[{\"question\":\"Why is predicting DNA origami stability in physiological media important?\",\"answer\":\"Structural instability under physiological conditions is a major barrier to drug delivery and diagnostic applications, so reliable stability assessment is critical for translation.\"},{\"question\":\"How does the proposed method estimate stability?\",\"answer\":\"The framework couples dynamic light scattering with machine learning to estimate stability responses based on diffusion coefficients, using a consensus model trained on extensive measurements.\"},{\"question\":\"What experimental factors are varied in the dataset?\",\"answer\":\"Measurements cover variations in temperature, incubation time, MgCl2 concentration, pH, and DNase I concentration across three DNA origami shapes.\"}]","Predicting DNA Origami Stability in Physiological Media by Machine Learning | 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