Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

Cited by (1,005)

Items 501–600 of 1,005

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   Su, Jin, Li, Zhikai, Tao, Tianli, Han, Chenchen, He, Yan, Dai, Fengyuan, Yuan, Qingyan, Gao, Yuan, Si, Tong, Zhang, Xuting et al. · 2026 · Nature Biotechnology
9. Pretraining model for biological sequence data
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    Chu, Lee‐Shin, Ruffolo, Jeffrey A., Harmalkar, Ameya, Gray, Jeffrey J. · 2024 · Protein Science
14. GlyNet: a multi-task neural network for predicting protein–glycan interactions
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19. Conditional generative modeling for de novo protein design with hierarchical functions
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    Sizhen Li, Saeed Moayedpour, Ruijiang Li, Michael Bailey, Saleh Riahi, Lorenzo Kogler-Anele, Milad Miladi, Jacob C. Miner, Dinghai Zheng, Jun Wang et al. · 2023 · bioRxiv (Cold Spring Harbor Laboratory)
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    Salvatore Candido, Thomas Hayes, Alexander Derry, Roshan Rao, Zeming Lin, Robert Verkuil, Bryan Z. Wu, Jin Sub Lee, Elise S. Bruguera, Jehan A. Keval et al. · 2026 · bioRxiv (Cold Spring Harbor Laboratory)
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    2023
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    Harding-Larsen, David, Funk, Jonathan, Madsen, Niklas Gesmar, Gharabli, Hani, Acevedo-Rocha, Carlos G., Mazurenko, Stanislav, Welner, Ditte Hededam · 2024 · Biotechnology Advances
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    Ken Lin, Xiong-Wen Quan, Chengcai Jin, Zhuangwei Shi, Jinglong Yang · 2022 · Frontiers in Genetics
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    Fan Hu, Yishen Hu, Weihong Zhang, Huazhen Huang, Yi Feng Pan, Peng Yin · 2023 · Advanced Science
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    Florian Haselbeck, Maura John, Yuqi Zhang, Jonathan Pirnay, J. Werner, Rubén D. Costa, Dominik G. Grimm · 2023 · NAR Genomics and Bioinformatics
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    Yidong Song, Qianmu Yuan, Huiying Zhao, Yuedong Yang · 2023 · Briefings in Bioinformatics
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    Sicheng Zhang, Jun Li, Shi‐Jie Chen · 2024 · Biophysical Journal
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    Doo Nam Kim, Andrew D. McNaughton, Neeraj Kumar · 2024 · Bioengineering
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    Fan Zhang, Jinfeng Li, Zhenguo Wen, Chun Fang · 2024 · Computational Biology and Chemistry
33. Enhanced Protein Secondary Structure Prediction Through Multi-View Multi-Feature Evolutionary Deep Fusion Method
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    Magdalena Topolska, Antoni Beltran, Ben Lehner · 2025 · Nature Communications
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43. Multimodal pretraining for unsupervised protein representation learning
    Viet Thanh Duy Nguyen, Truong Son Hy · 2024 · Biology Methods and Protocols
44. Natural Language Processing Methods for the Study of Protein–Ligand Interactions
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45. Variant effect predictions capture some aspects of deep mutational scanning experiments
    Jonas Reeb, Theresa Wirth, Burkhard Rost · 2020 · BMC Bioinformatics
46. T4SEfinder: a bioinformatics tool for genome-scale prediction of bacterial type IV secreted effectors using pre-trained protein language model
    Yumeng Zhang, Yangming Zhang, Yi Xiong, Hui Wang, Zixin Deng, Jiangning Song, Hong‐Yu Ou · 2021 · Briefings in Bioinformatics
47. QuoteTarget : A sequence‐based transformer protein language model to identify potentially druggable protein targets
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48. Is Transfer Learning Necessary for Protein Landscape Prediction?
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49. Self-supervised learning on millions of pre-mRNA sequences improves sequence-based RNA splicing prediction
    Ken Chen, Yue Zhou, Maolin Ding, Yu Wang, Zhixiang Ren, Yuedong Yang · 2023 · bioRxiv (Cold Spring Harbor Laboratory)
50. PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations
    Yu, Hengshi, Qian, Weizhou, Song, Yuxuan, Welch, Joshua D · 2025 · Molecular Systems Biology
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53. Machine learning approaches for biomolecular, biophysical, and biomaterials research.
    C. Rickert, O. Lieleg · 2022 · Biophysical Reviews
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    Pawel Pratyush, Suresh Pokharel, Hiroto Saigo, Dukka B. KC · 2023 · BMC Bioinformatics
55. Domain-PFP allows protein function prediction using function-aware domain embedding representations
    Nabil Ibtehaz, Yuki Kagaya, Daisuke Kihara · 2023 · Communications Biology
56. Unbiased organism-agnostic and highly sensitive signal peptide predictor with deep protein language model
    Jun-Bo Shen, Qinze Yu, Shen-Xue Chen, Qingxiong Tan, Jing-Chao Li, Yu Li · 2023 · Nature Computational Science
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    Jiajia Liu, Mengyuan Yang, Yankai Yu, Haixia Xu, Tiangang Wang, Kang Li, Xiaobo Zhou · 2026 · Briefings in Bioinformatics
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    Anna Carbery, Martin Buttenschoen, R. Skyner, F. von Delft, Charlotte Mary Deane · 2024 · Journal of Cheminformatics
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    Amit Ranjan, Adam Bess, Chris Alvin, Supratik Mukhopadhyay · 2024 · Journal of Chemical Information and Modeling
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    Ruofan Jin, Qing Ye, Jike Wang, Zheng Cao, Dejun Jiang, Tianyue Wang, Yu Kang, Wanting Xu, Chang‐Yu Hsieh, Tingjun Hou · 2024 · Briefings in Bioinformatics
61. Machine-Learning-Guided Peptide Drug Discovery: Development of GLP-1 Receptor Agonists with Improved Drug Properties
    Jens Christian Froslev Nielsen, Claudia U. Hjørringgaard, Mads Mørup Nygaard, Anita Wester, Lisbeth Elster, Trine Porsgaard, Randi Bonke Mikkelsen, Silas Anselm Rasmussen, Andreas Nygaard Madsen, Morten Schlein et al. · 2024 · Journal of Medicinal Chemistry
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    Rohit Singh, Chiho Im, Yu Qiu, Brian C. Mackness, Abhinav Gupta, Taylor Joren, Samuel Sledzieski, Lena Erlach, Maria Wendt, Yves Fomekong‐Nanfack et al. · 2024 · Proceedings of the National Academy of Sciences
63. Artificial intelligence-assisted next-generation biomaterials: From design and preparation to medical applications
    Bixia Zhou, Xin Li, Yuchen Pan, Bingfang He, Bingbing Gao · 2025 · Colloids and Surfaces B Biointerfaces
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    Tian Cai, Hansaim Lim, Kyra Alyssa Abbu, Yue Qiu, Ruth Nussinov, Lei Xie · 2021 · Journal of Chemical Information and Modeling
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    Yuzhi Xu, Jiankai Ge, Cheng‐Wei Ju · 2023 · Energy Advances
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    Shuya Li, Tingzhong Tian, Ziting Zhang, Ziheng Zou, Dan Yang Zhao, Jianyang Zeng · 2023 · Cell Systems
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    Jeffrey A. Ruffolo, Lee‐Shin Chu, Sai Pooja Mahajan, Jeffrey J. Gray · 2022 · bioRxiv (Cold Spring Harbor Laboratory)
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    Janani Durairaj, Dick de Ridder, Aalt D. J. van Dijk · 2022 · Computational and Structural Biotechnology Journal
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    Muhammad Arif, Saleh Musleh, Huma Fida, Tanvir Alam · 2024 · Scientific Reports
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    2024
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    Xiaoshuai Zhang, Huannan Guo, Fan Zhang, Xuan Wang, Kaitao Wu, Shizheng Qiu, Bo Liu, Yadong Wang, Yang Hu, Junyi Li · 2021 · Briefings in Bioinformatics
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    Zhixin Dou, Yuqing Sun, Xukai Jiang, Xiuyun Wu, Yingjie Li, Bin Gong, Lushan Wang · 2023 · Acta Biochimica et Biophysica Sinica
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    Quintin Pope, Rohan Varma, Christine A. Tataru, Maude M. David, Xiaoli Z. Fern · 2025 · PLoS Computational Biology
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    Chris Avery, John Patterson, Tyler J. Grear, Theodore D. Frater, Donald J. Jacobs · 2022 · Biomolecules
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    Anowarul Kabir, Amarda Shehu · 2022 · Biomolecules
89. Survey of Protein Sequence Embedding Models
    Chau Thi Thuy Tran, Siddharth Khadkikar, Aleksey Porollo · 2023 · International Journal of Molecular Sciences
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    Andres M Bran, Philippe Schwaller · 2024 · Springer eBooks
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    Nicholas Bhattacharya, Neil Thomas, Roshan Rao, Justas Dauparas, Peter K. Koo, David A. Baker, Yun S. Song, Sergey Ovchinnikov · 2020 · bioRxiv (Cold Spring Harbor Laboratory)
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