ProGen2: Exploring the boundaries of protein language models

Cited by (496)

Items 201–300 of 496

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2. Pseudo-perplexity in one fell swoop for protein fitness estimation
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3. Fine-tuning of conditional Transformers improves in silico enzyme prediction and generation
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4. Benchmarking Generative AI Protein Models Reveals Differences Between Structural and Sequence-based Approaches
   Alexander J Barnett, Rajendra KC, Pratikshya Pandey, Pamodha Somasiri, Kirsten A. Fairfax, Sandy S. C. Hung, Alex William Hewitt · 2026 · Genomics Proteomics & Bioinformatics
5. Improvements in viral gene annotation using large language models and soft alignments
   William Harrigan, Barbra D. Ferrell, K. Eric Wommack, Shawn W. Polson, Zachary D. Schreiber, Mahdi Belcaid · 2024 · BMC Bioinformatics
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7. Efficient and accurate sequence generation with small-scale protein language models
   Yaiza Serrano, Sergi Rodà, Vı́ctor Guallar, Alexis Molina · 2023 · bioRxiv (Cold Spring Harbor Laboratory)
8. Efficient inference, training, and fine-tuning of protein language models
   Çelik, Muhammed Hasan, Xie, Xiaohui · 2025 · iScience
9. Multi-Modal CLIP-Informed Protein Editing
   Mingze Yin, Hanjing Zhou, Yiheng Zhu, Miao Lin, Yixuan Wu, Jialu Wu, Hongxia Xu, Chang‐Yu Hsieh, Tingjun Hou, Jintai Chen et al. · 2024 · Health Data Science
10. GeoEvoBuilder: A deep learning framework for efficient functional and thermostable protein design
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12. Approaches for accelerating microbial gene function discovery using artificial intelligence
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13. Compressing the collective knowledge of ESM into a single protein language model
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14. AI ‐designed OpenCRISPR ‐1 performs robust knockout, base editing, and prime editing in rice
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15. The evolution of computation-driven paradigms in targeted peptide drug design: From predictive modeling to generative AI and clinical translation
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16. Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction
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17. AMPCliff: Quantitative definition and benchmarking of activity cliffs in antimicrobial peptides
    Kewei Li, Yuqian Wu, Yinheng Li, Yutong Guo, Yanwen Kong, Yan Wang, Yiyang Liang, Yusi Fan, Lan Huang, Ruochi Zhang et al. · 2025 · Journal of Advanced Research
18. Likelihood-based Fine-tuning of Protein Language Models for Few-shot Fitness Prediction and Design
    Alex Hawkins‐Hooker, Shikha Surana, Jack Simons, Jakub Kmec, Oliver Bent, Paul Duckworth · 2024 · bioRxiv (Cold Spring Harbor Laboratory)
19. Beware of Data Leakage from Protein LLM Pretraining
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20. Isolation of structural constraints by ensemble-conditioned protein sequence design
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21. Unified Deep Learning of Molecular and Protein Language Representations with T5ProtChem
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22. Designing diverse and high-performance proteins with a large language model in the loop
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23. Rational protein engineering using an omni-directional multipoint mutagenesis generation pipeline
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24. RESP2: An Uncertainty Aware Multi‐Target Multi‐Property Optimization AI Pipeline for Antibody Discovery
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25. Advancing biological taxonomy in the AI era: deep learning applications, challenges, and future directions
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26. Bridging scales: machine learning for the rational design and modelling of shape memory polymers
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27. A Comprehensive Review of Transformer-Based Language Models for Protein Sequence Analysis and Design
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28. Protein foundation models: a comprehensive survey
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29. Genomic language model-based genomic prediction in plant breeding
    Ganesh Alagarasan, Hui Li, Yunbi Xu, José Luis Crossa, Rajeev Kumar Varshney · 2026 · Trends in Plant Science
30. Entropy-driven zero-shot deep learning model selection for viral proteins
    Yuanxi Yu, Fan Jiang, Bozitao Zhong, Liang You Hong, Mingchen Li · 2025 · Physical Review Research
31. Considering the ethics of large machine learning models in the chemical sciences
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32. STAR: A Web Server for Assisting Directed Protein Evolution with Machine Learning
    Likun Yang, Xiaoli Liang, Na Zhang, Lu Lu · 2023 · ACS Omega
33. Predicting Immune Escape with Pretrained Protein Language Model Embeddings
    Kyle Swanson, Howard Y. Chang, James Zou · 2022 · bioRxiv (Cold Spring Harbor Laboratory)
34. Retrieved Sequence Augmentation for Protein Representation Learning
    Chang Ma, Haiteng Zhao, Linjin Zheng, Jiayi Xin, Qintong Li, Lijun Wu, Zhihong Deng, Lu Yang, Qi Liu, Lingpeng Kong · 2023 · bioRxiv (Cold Spring Harbor Laboratory)
35. Evaluating data partitioning strategies for accurate prediction of protein-ligand binding free energy changes in mutated proteins
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36. Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications
    Jianxin Tang, Daohong Gong, Honglin Li, Shiliang Li · 2026 · Acta Pharmaceutica Sinica B
37. Generative AI for synthetic biology: Designing biological parts, circuits, and genomes
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38. Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning
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39. AI-Designed Peptides as Tools for Biochemistry
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40. The convergence of AI-driven engineering biology and emerging technologies advancing globally networked autonomous biofoundries
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41. From Likelihood to Fitness: Improving Variant Effect Prediction in Protein and Genome Language Models
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42. AI-Empowered Genome Decoding: Applications of Large Language Models in Genomics
    Shaopeng Li, Weiliang Fan, Yu Zhou · 2025 · Frontiers of digital education.
43. MutPNI: A Unified Model Architecture for Accurately Predicting the Effects of Mutations on Protein-Nucleic Acid Interactions
    Zihao Yan, Fang Ge, Ying Zhang, Yan He, Yan Liu, Yiheng Zhu, Jiangning Song, Dong‐Jun Yu · 2025 · Journal of Chemical Information and Modeling
44. Interpretable Machine Learning for Protein Science: Structure, Function, and Interactions
    Luiz Felipe Vecchietti, Minji Lee, Begench Hangeldiyev, Bryan Nathanael Wijaya, Hyunkyu Jung, Hahnbeom Park, Tae‐Kyun Kim, Meeyoung Cha, Ho Min Kim · 2026 · ACM Computing Surveys
45. Foundation Models for Translational Cancer Biology
    Kevin K. Tsang, Sophia Kivelson, Jose Miguel Acitores Cortina, Aditi Kuchi, Jacob S. Berkowitz, Hongyu Liu, Apoorva Srinivasan, Nadine A. Friedrich, Yasaman Fatapour, Nicholas P. Tatonetti · 2025 · Annual Review of Biomedical Data Science
46. FLIGHTED: Inferring fitness landscapes from noisy high-throughput experimental data
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47. A cellular selection identifies elongated flavodoxins that support electron transfer to sulfite reductase
    Albert Truong, Dru Myerscough, Ian J. Campbell, Joshua T. Atkinson, Jonathan J. Silberg · 2023 · Protein Science
48. Protein Language Model Supervised Precise and Efficient Protein Backbone Design Method
    Bo Zhang, Kexin Liu, Zhuoqi Zheng, Yunfeiyang Liu, Junxi Mu, Ting Wei, Haifeng Chen · 2023 · bioRxiv (Cold Spring Harbor Laboratory)
49. MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training
    Bo Chen, Zhilei Bei, Xingyi Cheng, Pan Li, Jie Tang, Le Song · 2024 · bioRxiv (Cold Spring Harbor Laboratory)
50. Manufacturing-Aware Generative Model Architectures Enable Biological Sequence Design and Synthesis at Petascale
    Eli N. Weinstein, Mattia G. Gollub, Andrei Slabodkin, Cameron L. Gardner, Kerry Dobbs, Xiao-Bing Cui, Alan N. Amin, George McDonald Church, Elizabeth Wood · 2024 · bioRxiv (Cold Spring Harbor Laboratory)
51. Controllable All-Atom Protein Generation with Latent Diffusion
    Amy X. Lu, Jason Yang, Yueming Long, Theophile Lambert, Wilson Yan, Sarah A. Robinson, Kevin Yang, Vladimir Gligorijević, Kyunghyun Cho, Richard Bonneau et al. · 2024 · Research Square
52. Quantitative mapping of antigen specificity in adaptive immune repertoire embedding spaces
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53. Designing AI-programmable therapeutics with the EDEN family of foundation models
    Geraldene Munsamy, Gavin Ayres, Carla Greco, Keith Y K Kam, Gus Minto-Cowcher, John St John, Tanggis Bohnuud, Matthew H. Bakalar, William Chow, Robert J. Pecoraro et al. · 2026 · bioRxiv (Cold Spring Harbor Laboratory)
54. Kinase-substrate prediction using an autoregressive model
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55. A roadmap for T cell receptor-peptide-bound major histocompatibility complex binding prediction by machine learning: glimpse and foresight
    Furong Qi, Qiang Huang, Xuan Yao, Yingyin Cao, Yunyun Shen, Yihan Ren, Zhe Liu, Zheng Zhang · 2025 · Briefings in Bioinformatics
56. AbEgDiffuser: Antibody Sequence-Structure Codesign with Equivariant Graph Neural Networks and Diffusion Models
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57. Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances
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58. Biotechnology meets AI: Accelerating next-generation of microbial breeding for food biomanufacturing
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59. ProtRAP-LM: Efficient Protein Relative Accessibility Prediction and Proteome-wide Membrane Protein Screening
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60. Cross-species gene redesign leveraging ortholog information and generative modeling
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61. Controllable Protein Design by Prefix-Tuning Protein Language Models
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62. Generative β-hairpin design using a residue-based physicochemical property landscape
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63. Retrieved Sequence Augmentation for Protein Representation Learning
    Chang Ma, Haiteng Zhao, Lin Zheng, Jiayi Xin, Qintong Li, Lijun Wu, Zhihong Deng, Yang Young Lu, Qi Liu, Sheng Wang et al. · 2024 · Conference on Empirical Methods in Natural Language Processing (EMNLP)
64. Protein Language Model Predicts Mutation Pathogenicity and Clinical Prognosis
    Xiangling Liu, Xinyu Yang, Linkun Ouyang, Guibing Guo, Jin Su, Ruibin Xi, Ke Yuan, Fajie Yuan · 2022 · bioRxiv (Cold Spring Harbor Laboratory)
65. Building Representation Learning Models for Antibody Comprehension
    Justin Barton, Aretas Gaspariunas, Jacob D. Galson, Jinwoo Leem · 2023 · Cold Spring Harbor Perspectives in Biology
66. BindingGYM: A Large-Scale Mutational Dataset Toward Deciphering Protein-Protein Interactions
    Wei Lu, Jixian Zhang, Ming Gu, Shuangjia Zheng · 2024 · bioRxiv (Cold Spring Harbor Laboratory)
67. Deep models of protein evolution in time generate realistic evolutionary trajectories and functional proteins
    Antoine Koehl, Sebastian Prillo, Matthew Liu, J. T. Xiong, Lillian Weng, David F. Savage, Yun S. Song · 2026 · bioRxiv (Cold Spring Harbor Laboratory)
68. Ab-initio amino acid sequence design from protein text description with ProtDAT
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69. Pre-trained language models for protein and molecular design
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70. A Weighted Voting Approach for Traditional Chinese Medicine Formula Classification Using Large Language Models: Algorithm Development and Validation Study
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71. Heuristic multi-site optimization for protein sequence design using Masked Protein Language Models
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72. Zero-shot deep learning with multi-objective optimization improves thermostability of zearalenone hydrolase and xylanase
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77. Expanding the scope of protein language modeling to protein-protein interactions with MSA Pairformer
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78. Swarms of large language model agents for protein sequence design with experimental validation
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80. CPE-Pro: A Structure-Sensitive Deep Learning Method for Protein Representation and Origin Evaluation
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81. Ultrafast classical phylogenetic method beats large protein language models on variant effect prediction
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82. RAG-ESM: Improving Pretrained Protein Language Models via Sequence Retrieval
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83. Inferring context-specific site variation with evotuned protein language models
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84. How well do contextual protein encodings learn structure, function, and evolutionary context?
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88. DPAC: Prediction and Design of Protein-DNA Interactions via Sequence-Based Contrastive Learning
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89. Generating functional and multistate proteins with a multimodal diffusion transformer
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91. In-Context Learning can distort the relationship between sequence likelihoods and biological fitness
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92. ProFam: Open-Source Protein Family Language Modelling for Fitness Prediction and Design
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93. Separating selection from mutation in antibody language models
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94. Semantical and geometrical protein encoding toward enhanced bioactivity and thermostability
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95. BioSeq_Ksite: Multi-perspective feature-driven prediction of protein succinylation based on an adaptive attention module with SSBCE loss strategy
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96. CLTAP: A TAP-binding peptide prediction method using pre-trained large language-embedding models with contrastive learning to enhance contextual co-attention mechanism
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97. Harnessing Machine Learning for Agnostic Biodetection
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98. Predicting Subcutaneous Antibody Bioavailability Using Ensemble Protein Language Models.
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