| 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 |
| 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 | 501 | 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 |
| Improving de novo protein binder design with deep learning NR Bennett, B Coventry, I Goreshnik, B Huang, A Allen, D Vafeados, ... Nature Communications 14 (1), 2625, 2023 | 408 | 2023 |
| Mega-scale experimental analysis of protein folding stability in biology and design K Tsuboyama, J Dauparas, J Chen, E Laine, Y Mohseni Behbahani, ... Nature 620 (7973), 434-444, 2023 | 335 | 2023 |
| 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 |
| Improving protein expression, stability, and function with ProteinMPNN KH Sumida, R Núñez-Franco, I Kalvet, SJ Pellock, BIM Wicky, LF Milles, ... Journal of the American Chemical Society 146 (3), 2054-2061, 2024 | 259 | 2024 |
| Hallucinating symmetric protein assemblies BIM Wicky, LF Milles, A Courbet, RJ Ragotte, J Dauparas, E Kinfu, S Tipps, ... Science 378 (6615), 56-61, 2022 | 258 | 2022 |
| 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 |
| Language models generalize beyond natural proteins R Verkuil, O Kabeli, Y Du, BIM Wicky, LF Milles, J Dauparas, D Baker, ... BioRxiv, 2022.12. 21.521521, 2022 | 190 | 2022 |
| Peptide-binding specificity prediction using fine-tuned protein structure prediction networks A Motmaen, J Dauparas, M Baek, MH Abedi, D Baker, P Bradley Proceedings of the National Academy of Sciences 120 (9), e2216697120, 2023 | 117 | 2023 |
| Computational design of soluble and functional membrane protein analogues CA Goverde, M Pacesa, N Goldbach, LJ Dornfeld, PEM Balbi, ... Nature 631 (8020), 449-458, 2024 | 95 | 2024 |
| Design of stimulus-responsive two-state hinge proteins F Praetorius, PJY Leung, MH Tessmer, A Broerman, C Demakis, ... Science 381 (6659), 754-760, 2023 | 75 | 2023 |
| Binding and sensing diverse small molecules using shape-complementary pseudocycles L An, M Said, L Tran, S Majumder, I Goreshnik, GR Lee, D Juergens, ... Science 385 (6706), 276-282, 2024 | 63* | 2024 |
| Self-organization of swimmers drives long-range fluid transport in bacterial colonies H Xu, J Dauparas, D Das, E Lauga, Y Wu Nature communications 10 (1), 1792, 2019 | 61 | 2019 |
| Blueprinting extendable nanomaterials with standardized protein blocks TF Huddy, Y Hsia, RD Kibler, J Xu, N Bethel, D Nagarajan, R Redler, ... Nature 627 (8005), 898-904, 2024 | 60 | 2024 |
| Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14 I Anishchenko, M Baek, H Park, N Hiranuma, DE Kim, J Dauparas, ... Proteins: Structure, Function, and Bioinformatics 89 (12), 1722-1733, 2021 | 58 | 2021 |
| Deep learning methods for designing proteins scaffolding functional sites J Wang, S Lisanza, D Juergens, D Tischer, I Anishchenko, M Baek, ... BioRxiv, 2021.11. 10.468128, 2021 | 54 | 2021 |
| End-to-end learning of multiple sequence alignments with differentiable Smith–Waterman S Petti, N Bhattacharya, R Rao, J Dauparas, N Thomas, J Zhou, AM Rush, ... Bioinformatics 39 (1), btac724, 2023 | 50 | 2023 |
