Stanford Advisors

All Publications

  • De novo design of RNA pseudoknots with deep learning. bioRxiv : the preprint server for biology Townley, J., Kladwang, W., Baker, D., Blair, H. M., Choe, C. A., Nesr, G. E., Favor, A., Fisker, E., Haack, D. B., He, S., Hingey, J., Huang, P. S., Huang, R., Joshi, C. K., Karagianes, T., Kubaney, A., Liò, P., Mancino, A., Romano, J., Rudolfs, B., Spellmon, N., Toor, N., Wu, V., Yu, Z., Participants, E., Das, R. 2026

    Abstract

    RNA design has been hindered by the limited accuracy of 3D structure prediction. Here, 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 AI methods matched experienced human designers in solving most blind challenges, evaluated by single-nucleotide-resolution chemical mapping, compensatory mutagenesis, and cryogenic electron microscopy. Unexpectedly, 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 a 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.

    View details for DOI 10.64898/2026.05.21.726960

    View details for PubMedID 42239184

    View details for PubMedCentralID PMC13228335