| Learning from protein structure with geometric vector perceptrons B Jing, S Eismann, P Suriana, RJL Townshend, R Dror International Conference on Learning Representations, 2021 | 331 | 2021 |
| Geometric deep learning of RNA structure RJL Townshend, S Eismann, AM Watkins, R Rangan, M Karelina, R Das, ... Science 373 (6558), 1047-1051, 2021 | 240 | 2021 |
| Molecular mechanism of GPCR-mediated arrestin activation NR Latorraca, JK Wang, B Bauer, RJL Townshend, SA Hollingsworth, ... Nature 557 (7705), 452-456, 2018 | 197 | 2018 |
| End-to-end learning on 3d protein structure for interface prediction RJL Townshend, R Bedi, PA Suriana, RO Dror Neural Information Processing Systems, 2018 | 117 | 2018 |
| Atom3d: Tasks on molecules in three dimensions RJL Townshend, M Vögele, P Suriana, A Derry, A Powers, Y Laloudakis, ... arXiv preprint arXiv:2012.04035, 2020 | 106 | 2020 |
| How GPCR phosphorylation patterns orchestrate arrestin-mediated signaling NR Latorraca, M Masureel, SA Hollingsworth, FM Heydenreich, ... Cell 183 (7), 1813-1825. e18, 2020 | 105 | 2020 |
| Simple biochemical features underlie transcriptional activation domain diversity and dynamic, fuzzy binding to Mediator AL Sanborn, BT Yeh, JT Feigerle, CV Hao, RJL Townshend, ... Elife 10, e68068, 2021 | 94 | 2021 |
| Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes S Eismann, RJL Townshend, N Thomas, M Jagota, B Jing, RO Dror Proteins: Structure, Function, and Bioinformatics 89 (5), 493-501, 2021 | 69* | 2021 |
| User-driven geolocation of untagged desert imagery using digital elevation models E Tzeng, A Zhai, M Clements, R Townshend, A Zakhor Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2013 | 34 | 2013 |
| Protein model quality assessment using rotation-equivariant, hierarchical neural networks S Eismann, P Suriana, B Jing, RJL Townshend, RO Dror arXiv preprint arXiv:2011.13557, 2020 | 11 | 2020 |
| Geometric prediction: Moving beyond scalars RJL Townshend, B Townshend, S Eismann, RO Dror arXiv preprint arXiv:2006.14163, 2020 | 8 | 2020 |
| Gold nanoparticles and tilt pairs to assess protein flexibility by cryo-electron microscopy M Jagota, RJL Townshend, LW Kang, DA Bushnell, RO Dror, ... Ultramicroscopy 227, 113302, 2021 | 4 | 2021 |
| Protein model quality assessment using rotation‐equivariant transformations on point clouds S Eismann, P Suriana, B Jing, RJL Townshend, RO Dror Proteins: Structure, Function, and Bioinformatics 91 (8), 1089-1096, 2023 | 1 | 2023 |
| ATOM-1: A foundation model for RNA structure and function built on chemical mapping data N Boyd, BM Anderson, B Townshend, R Chow, CJ Stephens, R Rangan, ... bioRxiv, 2023.12. 13.571579, 2023 | 1 | 2023 |
| Euclidean transformers for macromolecular structures: Lessons learned DD Liu, L Melo, A Costa, M Vögele, RJL Townshend, RO Dror 2022 ICML Workshop on Computational Biology, 2022 | 1 | 2022 |
| Methods, systems, and media method applying machine learning to chemical mapping data for rna tertiary structure prediction RJL Townshend, SJ Eismann US Patent App. 18/452,480, 2024 | | 2024 |
| Geometric deep learning of RNA structure (vol 379, eadg6616, 2023) RJL Townshend SCIENCE 380 (6649), 1022-1022, 2023 | | 2023 |
| Geometric deep learning of RNA structure (vol 373, pg 1047, 2021) RJL Townshend SCIENCE 379 (6630), 2023 | | 2023 |
| Geometric Learning of Biomolecular Structure RJL Townshend Stanford University, 2021 | | 2021 |
| Transferrable End-to-End Learning for Protein Interface Prediction RJL Townshend, R Bedi, RO Dror | | 2018 |
Ron DrorStanford UniversityVerified email at stanford.edu
