Protein Large Language Models: A Comprehensive Survey
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866913721473826816 |
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| author | Xiao, Yijia Zhao, Wanjia Zhang, Junkai Jin, Yiqiao Zhang, Han Ren, Zhicheng Sun, Renliang Wang, Haixin Wan, Guancheng Lu, Pan Luo, Xiao Zhang, Yu Zou, James Sun, Yizhou Wang, Wei |
| author_facet | Xiao, Yijia Zhao, Wanjia Zhang, Junkai Jin, Yiqiao Zhang, Han Ren, Zhicheng Sun, Renliang Wang, Haixin Wan, Guancheng Lu, Pan Luo, Xiao Zhang, Yu Zou, James Sun, Yizhou Wang, Wei |
| contents | Protein-specific large language models (Protein LLMs) are revolutionizing protein science by enabling more efficient protein structure prediction, function annotation, and design. While existing surveys focus on specific aspects or applications, this work provides the first comprehensive overview of Protein LLMs, covering their architectures, training datasets, evaluation metrics, and diverse applications. Through a systematic analysis of over 100 articles, we propose a structured taxonomy of state-of-the-art Protein LLMs, analyze how they leverage large-scale protein sequence data for improved accuracy, and explore their potential in advancing protein engineering and biomedical research. Additionally, we discuss key challenges and future directions, positioning Protein LLMs as essential tools for scientific discovery in protein science. Resources are maintained at https://github.com/Yijia-Xiao/Protein-LLM-Survey. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17504 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Protein Large Language Models: A Comprehensive Survey Xiao, Yijia Zhao, Wanjia Zhang, Junkai Jin, Yiqiao Zhang, Han Ren, Zhicheng Sun, Renliang Wang, Haixin Wan, Guancheng Lu, Pan Luo, Xiao Zhang, Yu Zou, James Sun, Yizhou Wang, Wei Biomolecules Artificial Intelligence Computational Engineering, Finance, and Science Computation and Language Machine Learning Protein-specific large language models (Protein LLMs) are revolutionizing protein science by enabling more efficient protein structure prediction, function annotation, and design. While existing surveys focus on specific aspects or applications, this work provides the first comprehensive overview of Protein LLMs, covering their architectures, training datasets, evaluation metrics, and diverse applications. Through a systematic analysis of over 100 articles, we propose a structured taxonomy of state-of-the-art Protein LLMs, analyze how they leverage large-scale protein sequence data for improved accuracy, and explore their potential in advancing protein engineering and biomedical research. Additionally, we discuss key challenges and future directions, positioning Protein LLMs as essential tools for scientific discovery in protein science. Resources are maintained at https://github.com/Yijia-Xiao/Protein-LLM-Survey. |
| title | Protein Large Language Models: A Comprehensive Survey |
| topic | Biomolecules Artificial Intelligence Computational Engineering, Finance, and Science Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2502.17504 |