| 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, ... Science 379 (6637), 1123-1130, 2023 | 5579* | 2023 |
| Evaluating protein transfer learning with TAPE R Rao, N Bhattacharya, N Thomas, Y Duan, P Chen, J Canny, P Abbeel, ... Advances in neural information processing systems 32, 2019 | 1255 | 2019 |
| 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 Advances in neural information processing systems 34, 29287-29303, 2021 | 1008 | 2021 |
| MSA transformer RM Rao, J Liu, R Verkuil, J Meier, J Canny, P Abbeel, T Sercu, A Rives International conference on machine learning, 8844-8856, 2021 | 960 | 2021 |
| 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, ... Science 387 (6736), 850-858, 2025 | 809 | 2025 |
| Transformer protein language models are unsupervised structure learners R Rao, J Meier, T Sercu, S Ovchinnikov, A Rives Biorxiv, 2020.12. 15.422761, 2020 | 449 | 2020 |
| GPU accelerated t-distributed stochastic neighbor embedding DM Chan, R Rao, F Huang, JF Canny Journal of Parallel and Distributed Computing 131, 1-13, 2019 | 245* | 2019 |
| 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, 2022.12. 21.521526, 2022 | 60 | 2022 |
| 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 |
| Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features A Harmalkar, R Rao, Y Richard Xie, J Honer, W Deisting, J Anlahr, ... MAbs 15 (1), 2163584, 2023 | 48 | 2023 |
| Single layers of attention suffice to predict protein contacts N Bhattacharya, N Thomas, R Rao, J Dauparas, PK Koo, D Baker, ... Biorxiv, 2020.12. 21.423882, 2020 | 39 | 2020 |
| Interpreting potts and transformer protein models through the lens of simplified attention N Bhattacharya, N Thomas, R Rao, J Dauparas, PK Koo, D Baker, ... PACIFIC SYMPOSIUM ON BIOCOMPUTING 2022, 34-45, 2021 | 30 | 2021 |
| ZPD teaching strategies for deep reinforcement learning from demonstrations D Seita, D Chan, R Rao, C Tang, M Zhao, J Canny arXiv preprint arXiv:1910.12154, 2019 | 15 | 2019 |
| Seq2MSA: a language model for protein sequence diversification P Sturmfels, R Rao, R Verkuil, Z Lin, T Sercu, A Lerer, A Rives Machine Learning in Structural Biology Workshop, NeurIPS 2022, 2022 | 5 | 2022 |
| Quality and relevance metrics for selection of multimodal pretraining data R Rao, S Rao, E Nouri, D Dey, A Celikyilmaz, B Dolan Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 5 | 2020 |
| Training, Evaluating, and Understanding Evolutionary Models for Protein Sequences RM Rao University of California, Berkeley, 2021 | | 2021 |
Tom SercuEvolutionaryScaleVerified email at evolutionaryscale.ai
