| Structure-based protein function prediction using graph convolutional networks V Gligorijević, PD Renfrew, T Kosciolek, JK Leman, D Berenberg, ... Nature communications 12 (1), 3168, 2021 | 519 | 2021 |
| OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization G Ahdritz, N Bouatta, S Kadyan, Q Xia, W Gerecke, TJ O’Donnell, ... Biorxiv, 2022.11. 20.517210, 2022 | 98 | 2022 |
| Function-guided protein design by deep manifold sampling V Gligorijević, D Berenberg, S Ra, A Watkins, S Kelow, K Cho, R Bonneau bioRxiv, 2021.12. 22.473759, 2021 | 35 | 2021 |
| Sequence-structure-function relationships in the microbial protein universe J Koehler Leman, P Szczerbiak, PD Renfrew, V Gligorijevic, D Berenberg, ... Nature communications 14 (1), 2351, 2023 | 21 | 2023 |
| Protein remote homology detection and structural alignment using deep learning T Hamamsy, JT Morton, R Blackwell, D Berenberg, N Carriero, ... Nature biotechnology, 1-11, 2023 | 19 | 2023 |
| Protein structural alignments from sequence JT Morton, CEM Strauss, R Blackwell, D Berenberg, V Gligorijevic, ... BioRxiv, 2020.11. 03.365932, 2020 | 16 | 2020 |
| Openfold: Retraining alphafold2 yields new insights into its learning mechanisms and capacity for generalization. bioRxiv G Ahdritz, N Bouatta, S Kadyan, Q Xia, W Gerecke, TJ O’Donnell, ... Preprint, 2022 | 14 | 2022 |
| Structure-based protein function prediction using graph convolutional networks. Nat Commun 12: 3168 V Gligorijevic, PD Renfrew, T Kosciolek, JK Leman, D Berenberg, ... | 11 | 2021 |
| Structure-based function prediction using graph convolutional networks. bioRxiv V Gligorijevic, PD Renfrew, T Kosciolek, JK Leman, K Cho, T Vatanen, ... | 10 | 2019 |
| TM-Vec: template modeling vectors for fast homology detection and alignment T Hamamsy, JT Morton, D Berenberg, N Carriero, V Gligorijevic, ... bioRxiv, 2022.07. 25.501437, 2022 | 9 | 2022 |
| Efficient crowd exploration of large networks: The case of causal attribution D Berenberg, JP Bagrow Proceedings of the ACM on Human-Computer Interaction 2 (CSCW), 1-25, 2018 | 8 | 2018 |
| OpenProteinSet: Training data for structural biology at scale G Ahdritz, N Bouatta, S Kadyan, L Jarosch, D Berenberg, I Fisk, A Watkins, ... Advances in Neural Information Processing Systems 36, 2024 | 5 | 2024 |
| Protein discovery with discrete walk-jump sampling NC Frey, D Berenberg, K Zadorozhny, J Kleinhenz, J Lafrance-Vanasse, ... arXiv preprint arXiv:2306.12360, 2023 | 5 | 2023 |
| Multi-segment preserving sampling for deep manifold sampler D Berenberg, JH Lee, S Kelow, JW Park, A Watkins, V Gligorijević, ... arXiv preprint arXiv:2205.04259, 2022 | 3 | 2022 |
| Hera Vlamakis, et al V Gligorijevic, PD Renfrew, T Kosciolek, JK Leman, D Berenberg, ... Structure-based function prediction using graph convolutional networks …, 2020 | 3 | 2020 |
| Learning protein family manifolds with smoothed energy-based models NC Frey, D Berenberg, J Kleinhenz, I Hotzel, J Lafrance-Vanasse, ... ICLR 2023 Workshop on Physics for Machine Learning, 2023 | 2 | 2023 |
| Neural language representations predict outcomes of scientific research JP Bagrow, D Berenberg, J Bongard arXiv preprint arXiv:1805.06879, 2018 | 1 | 2018 |
| Inferring the size of the causal universe: features and fusion of causal attribution networks D Berenberg, JP Bagrow arXiv preprint arXiv:1812.06038, 2018 | | 2018 |
| Supporting Material for “Neural language representations predict outcomes of scientific research” JP Bagrow, D Berenberg, J Bongard | | 2018 |
| Exploiting language models for protein discovery with latent walk-jump sampling SP Mahajan, NC Frey, D Berenberg, J Kleinhenz, R Bonneau, ... | | |
Vladimir GligorijevicPrescient Design, Genentech, Research & Early Development (gRED)Verified email at gene.com
