| Evolutionary-scale prediction of atomic level protein structure with a language model Z Lin, H Akin, R Rao, B Hie, Z Zhu, W Lu, N Smetanin, R Verkuil, O Kabeli, ... bioRxiv, 2022 | 2811* | 2022 |
| Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences A Rives, S Goyal, J Meier, D Guo, M Ott, CL Zitnick, J Ma, R Fergus bioRxiv, 622803, 2019 | 2332 | 2019 |
| Modular organization of cellular networks AW Rives, T Galitski Proceedings of the National Academy of Sciences 100 (3), 1128-1133, 2003 | 987 | 2003 |
| MSA Transformer R Rao, J Liu, R Verkuil, J Meier, JF Canny, P Abbeel, T Sercu, A Rives bioRxiv, 2021 | 659 | 2021 |
| Language models enable zero-shot prediction of the effects of mutations on protein function J Meier, R Rao, R Verkuil, J Liu, T Sercu, A Rives bioRxiv, 2021 | 607 | 2021 |
| Learning inverse folding from millions of predicted structures C Hsu, R Verkuil, J Liu, Z Lin, B Hie, T Sercu, A Lerer, A Rives bioRxiv, 2022 | 392 | 2022 |
| Transformer protein language models are unsupervised structure learners R Rao, J Meier, T Sercu, S Ovchinnikov, A Rives bioRxiv, 2020 | 337 | 2020 |
| Derivation of genetic interaction networks from quantitative phenotype data BL Drees, V Thorsson, GW Carter, AW Rives, MZ Raymond, ... Genome biology 6 (4), 1-10, 2005 | 157 | 2005 |
| Simulating 500 million years of evolution with a language model T Hayes, R Rao, H Akin, NJ Sofroniew, D Oktay, Z Lin, R Verkuil, VQ Tran, ... bioRxiv, 2024.07. 01.600583, 2024 | 124 | 2024 |
| Language models generalize beyond natural proteins R Verkuil, O Kabeli, Y Du, BIM Wicky, LF Milles, J Dauparas, D Baker, ... bioRxiv, 2022 | 102 | 2022 |
| Energy-based models for atomic-resolution protein conformations Y Du, J Meier, J Ma, R Fergus, A Rives International Conference on Learning Representations, 2020 | 61 | 2020 |
| A high-level programming language for generative protein design B Hie, S Candido, Z Lin, O Kabeli, R Rao, N Smetanin, T Sercu, A Rives bioRxiv, 2022 | 36 | 2022 |
| Deep generative models create new and diverse protein structures Z Lin, T Sercu, Y LeCun, A Rives Machine Learning for Structural Biology Workshop, NeurIPS, 2021 | 21 | 2021 |
| Neural Potts Model T Sercu, R Verkuil, J Meier, B Amos, Z Lin, C Chen, J Liu, Y LeCun, ... bioRxiv, 2021 | 12 | 2021 |
| Seq2MSA: A language model for protein sequence diversification P Sturmfels, R Rao, R Verkuil, Z Lin, O Kabeli, T Sercu, A Lerer, A Rives | 2 | 2022 |
