@article {70489,
	title = {De novo design of RNA pseudoknots with deep learning.},
	journal = {Science},
	volume = {393},
	year = {2026},
	month = {2026 Aug 27},
	pages = {931-937},
	abstract = {<p>RNA design has been hindered by the limited accuracy of three-dimensional (3D) structure prediction. In this study, we show that intricate RNA structures can be generated with current deep learning tools through accurate de novo design of pseudoknot secondary structures. In an Eterna competition involving 57 pseudoknots, generative artificial intelligence (AI) methods matched experienced human designers in solving most blind challenges, evaluated by single nucleotide-resolution chemical mapping, compensatory mutagenesis, and cryo-electron microscopy. AI-generated molecules with accurate secondary structures formed well-ordered 3D folds stabilized by noncanonical tertiary interactions not modeled during design. Success was guided by an RNet foundation model trained on prior chemical mapping data, suggesting that some difficult RNA design tasks may be tractable without first solving RNA 3D structure prediction.</p>
},
	keywords = {Cryoelectron Microscopy, Deep Learning, Generative Artificial Intelligence, Humans, Models, Molecular, Nucleic Acid Conformation, RNA, RNA Folding},
	issn = {1095-9203},
	doi = {10.1126/science.aeg6829},
	url = {https://doi.org/10.1126/science.aeg6829},
	author = {Townley, Jill and Kladwang, Wipapat and Baker, David and Blair, Hamish M and Choe, Christian A and El Nesr, Gina and Favor, Andrew and Fisker, Eli and Haack, Daniel B and He, Shujun and Hingey, Jason and Huang, Po-Ssu and Huang, Rui and Joshi, Chaitanya K and Karagianes, Thomas and Kubaney, Andrew and Li{\`o}, Pietro and Mancino, Adamo and Romano, Jonathan and Rudolfs, Boris and Spellmon, Nicholas and Toor, Navtej and Verma, Jigyasa and Wu, Vivian and Yu, Zhiheng and Participants, Eterna and Das, Rhiju}
}
