| High-speed recording of neural spikes in awake mice and flies with a fluorescent voltage sensor Y Gong, C Huang, JZ Li, BF Grewe, Y Zhang, S Eismann, MJ Schnitzer Science 350 (6266), 1361-1366, 2015 | 479 | 2015 |
| Learning from protein structure with geometric vector perceptrons B Jing, S Eismann, P Suriana, RJL Townshend, R Dror International Conference on Learning Representations, 2020 | 332 | 2020 |
| Geometric deep learning of RNA structure RJL Townshend, S Eismann, AM Watkins, R Rangan, M Karelina, R Das, ... Science 373 (6558), 1047-1051, 2021 | 241 | 2021 |
| Molecular mechanism of biased signaling in a prototypical G-protein-coupled receptor CM Suomivuori, NR Latorraca, LM Wingler, S Eismann, MC King, ... Biophysical Journal 118 (3), 162a, 2020 | 181 | 2020 |
| Learning neural PDE solvers with convergence guarantees JT Hsieh, S Zhao, S Eismann, L Mirabella, S Ermon arXiv preprint arXiv:1906.01200, 2019 | 125 | 2019 |
| 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 |
| Equivariant graph neural networks for 3d macromolecular structure B Jing, S Eismann, PN Soni, RO Dror arXiv preprint arXiv:2106.03843, 2021 | 62 | 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, 2020 | 61 | 2020 |
| Protein sequence‐to‐structure learning: Is this the end (‐to‐end revolution)? E Laine, S Eismann, A Elofsson, S Grudinin Proteins: Structure, Function, and Bioinformatics 89 (12), 1770-1786, 2021 | 38 | 2021 |
| A preclinical microbeam facility with a conventional x‐ray tube S Bartzsch, C Cummings, S Eismann, U Oelfke Medical physics 43 (12), 6301-6308, 2016 | 30 | 2016 |
| Bayesian optimization and attribute adjustment S Eismann, D Levy, R Shu, S Bartzsch, S Ermon Proc. 34th Conference on Uncertainty in Artificial Intelligence, 2018 | 25 | 2018 |
| 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 |
| Hierarchical, rotation-equivariant neural networks to predict the structure of protein complexes S Eismann, RJL Townshend, N Thomas, M Jagota, B Jing, R Dror arXiv preprint arXiv:2006.09275, 2020 | 8 | 2020 |
| Protein connectivity in chemotaxis receptor complexes S Eismann, RG Endres PLoS Computational Biology 11 (12), e1004650, 2015 | 6 | 2015 |
| 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 |
| 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 |
| Equivariant Machine Learning for Macromolecules SJ Eismann Stanford University, 2021 | | 2021 |
| Raphael JL Townshend Computer Science Stanford University A Derry, AS Powers, Y Laloudakis, S Balachandar, B Jing, B Anderson, ... | | |
