| Accurate prediction of protein structures and interactions using a three-track neural network M Baek, F DiMaio, I Anishchenko, J Dauparas, S Ovchinnikov, GR Lee, ... Science 373 (6557), 871-876, 2021 | 5934 | 2021 |
| Robust deep learning–based protein sequence design using ProteinMPNN J Dauparas, I Anishchenko, N Bennett, H Bai, RJ Ragotte, LF Milles, ... Science 378 (6615), 49-56, 2022 | 2006 | 2022 |
| Improved protein structure prediction using predicted interresidue orientations J Yang, I Anishchenko, H Park, Z Peng, S Ovchinnikov, D Baker Proceedings of the National Academy of Sciences 117 (3), 1496-1503, 2020 | 1611 | 2020 |
| Generalized biomolecular modeling and design with RoseTTAFold All-Atom R Krishna, J Wang, W Ahern, P Sturmfels, P Venkatesh, I Kalvet, GR Lee, ... Science 384 (6693), eadl2528, 2024 | 936 | 2024 |
| The trRosetta server for fast and accurate protein structure prediction Z Du, H Su, W Wang, L Ye, H Wei, Z Peng, I Anishchenko, D Baker, ... Nature protocols 16 (12), 5634-5651, 2021 | 716 | 2021 |
| De novo protein design by deep network hallucination I Anishchenko, SJ Pellock, TM Chidyausiku, TA Ramelot, S Ovchinnikov, ... Nature 600 (7889), 547-552, 2021 | 696 | 2021 |
| Computed structures of core eukaryotic protein complexes IR Humphreys, J Pei, M Baek, A Krishnakumar, I Anishchenko, ... Science 374 (6573), eabm4805, 2021 | 549 | 2021 |
| Scaffolding protein functional sites using deep learning J Wang, S Lisanza, D Juergens, D Tischer, JL Watson, KM Castro, ... Science 377 (6604), 387-394, 2022 | 542 | 2022 |
| De novo design of luciferases using deep learning AHW Yeh, C Norn, Y Kipnis, D Tischer, SJ Pellock, D Evans, P Ma, ... Nature 614 (7949), 774-780, 2023 | 443 | 2023 |
| Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA M Baek, R McHugh, I Anishchenko, H Jiang, D Baker, F DiMaio Nature methods 21 (1), 117-121, 2024 | 329 | 2024 |
| Improved protein structure refinement guided by deep learning based accuracy estimation N Hiranuma, H Park, M Baek, I Anishchenko, J Dauparas, D Baker Nature communications 12 (1), 1340, 2021 | 293 | 2021 |
| Protein interaction networks revealed by proteome coevolution Q Cong, I Anishchenko, S Ovchinnikov, D Baker Science 365 (6449), 185-189, 2019 | 285 | 2019 |
| Protein sequence design by conformational landscape optimization C Norn, BIM Wicky, D Juergens, S Liu, D Kim, D Tischer, B Koepnick, ... Proceedings of the National Academy of Sciences 118 (11), e2017228118, 2021 | 231* | 2021 |
| Origins of coevolution between residues distant in protein 3D structures I Anishchenko, S Ovchinnikov, H Kamisetty, D Baker Proceedings of the National Academy of Sciences 114 (34), 9122-9127, 2017 | 213 | 2017 |
| Atomic context-conditioned protein sequence design using LigandMPNN J Dauparas, GR Lee, R Pecoraro, L An, I Anishchenko, C Glasscock, ... Nature Methods, 1-7, 2025 | 193 | 2025 |
| Prediction of homoprotein and heteroprotein complexes by protein docking and template‐based modeling: a CASP‐CAPRI experiment MF Lensink, S Velankar, A Kryshtafovych, SY Huang, ... Proteins: Structure, Function, and Bioinformatics 84, 323-348, 2016 | 176 | 2016 |
| Efficient and accurate prediction of protein structure using RoseTTAFold2 M Baek, I Anishchenko, IR Humphreys, Q Cong, D Baker, F DiMaio BioRxiv, 2023.05. 24.542179, 2023 | 163 | 2023 |
| Computational design of serine hydrolases A Lauko, SJ Pellock, KH Sumida, I Anishchenko, D Juergens, W Ahern, ... Science 388 (6744), eadu2454, 2025 | 136 | 2025 |
| Protein contact prediction using metagenome sequence data and residual neural networks Q Wu, Z Peng, I Anishchenko, Q Cong, D Baker, J Yang Bioinformatics 36 (1), 41-48, 2020 | 105 | 2020 |
| Dockground: a comprehensive data resource for modeling of protein complexes PJ Kundrotas, I Anishchenko, T Dauzhenka, I Kotthoff, D Mnevets, ... Protein Science 27 (1), 172-181, 2018 | 105 | 2018 |
David BakerUniversity of WashingtonVerified email at uw.edu
