| A guide to machine learning for biologists JG Greener, SM Kandathil, L Moffat, DT Jones Nature Reviews Molecular Cell Biology 23 (1), 40-55, 2022 | 1340 | 2022 |
| Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints JG Greener, SM Kandathil, DT Jones Nature communications 10 (1), 3977, 2019 | 194 | 2019 |
| Design of metalloproteins and novel protein folds using variational autoencoders JG Greener, L Moffat, DT Jones Scientific reports 8 (1), 16189, 2018 | 144 | 2018 |
| Prediction of interresidue contacts with DeepMetaPSICOV in CASP13 SM Kandathil, JG Greener, DT Jones Proteins: Structure, Function, and Bioinformatics 87 (12), 1092-1099, 2019 | 114 | 2019 |
| AlloPred: prediction of allosteric pockets on proteins using normal mode perturbation analysis JG Greener, MJE Sternberg BMC bioinformatics 16, 1-7, 2015 | 104 | 2015 |
| Structure-based prediction of protein allostery JG Greener, MJE Sternberg Current Opinion in Structural Biology 50, 1-8, 2018 | 91 | 2018 |
| Recent developments in deep learning applied to protein structure prediction SM Kandathil, JG Greener, DT Jones Proteins: Structure, Function, and Bioinformatics 87 (12), 1179-1189, 2019 | 68 | 2019 |
| High‐Throughput Kinetic Analysis for Target‐Directed Covalent Ligand Discovery GB Craven, DP Affron, CE Allen, S Matthies, JG Greener, RML Morgan, ... Angewandte Chemie International Edition 57 (19), 5257-5261, 2018 | 66 | 2018 |
| Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins SM Kandathil, JG Greener, AM Lau, DT Jones Proceedings of the National Academy of Sciences 119 (4), e2113348119, 2022 | 57* | 2022 |
| Julia for biologists E Roesch, JG Greener, AL MacLean, H Nassar, C Rackauckas, TE Holy, ... Nature Methods 20 (5), 655-664, 2023 | 51 | 2023 |
| Predicting protein dynamics and allostery using multi-protein atomic distance constraints JG Greener, I Filippis, MJE Sternberg Structure 25 (3), 546-558, 2017 | 46 | 2017 |
| Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins JG Greener, DT Jones PloS one 16 (9), e0256990, 2021 | 33 | 2021 |
| Using AlphaFold for rapid and accurate fixed backbone protein design L Moffat, JG Greener, DT Jones Biorxiv, 2021.08. 24.457549, 2021 | 32 | 2021 |
| Differentiable simulation to develop molecular dynamics force fields for disordered proteins JG Greener Chemical Science 15 (13), 4897-4909, 2024 | 18 | 2024 |
| BioStructures. jl: read, write and manipulate macromolecular structures in Julia JG Greener, J Selvaraj, BJ Ward Bioinformatics 36 (14), 4206-4207, 2020 | 11 | 2020 |
| Fast protein structure searching using structure graph embeddings JG Greener, K Jamali bioRxiv, 2022.11. 28.518224, 2022 | 10 | 2022 |
| Near-complete protein structural modelling of the minimal genome JG Greener, N Desai, SM Kandathil, DT Jones arXiv preprint arXiv:2007.06623, 2020 | 3 | 2020 |
| On the design space between molecular mechanics and machine learning force fields Y Wang, K Takaba, MS Chen, M Wieder, Y Xu, T Zhu, JZH Zhang, ... arXiv preprint arXiv:2409.01931, 2024 | 2 | 2024 |
| Reversible molecular simulation for training classical and machine learning force fields JG Greener arXiv preprint arXiv:2412.04374, 2024 | | 2024 |
| Molecular mechanism of Mad2 conformational conversion promoted by the Mad2-interaction motif of Cdc20 CWH Yu, ES Fischer, JG Greener, J Yang, Z Zhang, SMV Freund, ... bioRxiv, 2024.03. 03.583158, 2024 | | 2024 |
