| 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 | 5930 | 2021 |
| De novo design of protein structure and function with RFdiffusion JL Watson, D Juergens, NR Bennett, BL Trippe, J Yim, HE Eisenach, ... Nature 620 (7976), 1089-1100, 2023 | 2403* | 2023 |
| 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 | 935 | 2024 |
| Computed structures of core eukaryotic protein complexes IR Humphreys, J Pei, M Baek, A Krishnakumar, I Anishchenko, ... Science 374 (6573), eabm4805, 2021 | 547 | 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 | 501 | 2022 |
| 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 |
| Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA M Baek, R McHugh, I Anishchenko, H Jiang, D Baker, F DiMaio Nature Methods, 1-5, 2023 | 377* | 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 |
| 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 |
| 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, I Humphreys, Q Cong, D Baker, F DiMaio bioRxiv, 2023.05. 24.542179, 2023 | 163 | 2023 |
| GalaxyHomomer: a web server for protein homo-oligomer structure prediction from a monomer sequence or structure M Baek, T Park, L Heo, C Park, C Seok Nucleic Acids Research 45 (W1), W320-W324, 2017 | 153 | 2017 |
| Top-down design of protein architectures with reinforcement learning ID Lutz, S Wang, C Norn, A Courbet, AJ Borst, YT Zhao, A Dosey, L Cao, ... Science 380 (6642), 266-273, 2023 | 150 | 2023 |
| Deep learning and protein structure modeling M Baek, D Baker Nature Methods 19 (1), 13-14, 2022 | 147 | 2022 |
| Blind prediction of homo‐and hetero‐protein complexes: The CASP13‐CAPRI experiment MF Lensink, G Brysbaert, N Nadzirin, S Velankar, RAG Chaleil, T Gerguri, ... Proteins: Structure, Function, and Bioinformatics 87 (12), 1200-1221, 2019 | 143 | 2019 |
| 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 |
| The challenge of modeling protein assemblies: the CASP12‐CAPRI experiment MF Lensink, S Velankar, M Baek, L Heo, C Seok, SJ Wodak Proteins: Structure, Function, and Bioinformatics 86, 257-273, 2018 | 109 | 2018 |
| Force Field Optimization Guided by Small Molecule Crystal Lattice Data Enables Consistent Sub-Angstrom Protein–Ligand Docking H Park, G Zhou, M Baek, D Baker, F DiMaio Journal of Chemical Theory and Computation 17 (3), 2000-2010, 2021 | 95 | 2021 |
| Democratizing protein language models with parameter-efficient fine-tuning S Sledzieski, M Kshirsagar, M Baek, R Dodhia, J Lavista Ferres, B Berger Proceedings of the National Academy of Sciences 121 (26), e2405840121, 2024 | 80 | 2024 |
| Accurate protein structure prediction: what comes next C Seok, M Baek, M Steinegger, H Park, GR Lee, J Won Biodesign 9 (3), 47-50, 2021 | 73 | 2021 |
David BakerUniversity of WashingtonVerified email at uw.edu
