计算机科学
数据科学
比例(比率)
资源(消歧)
下游(制造业)
管理科学
人工智能
模型验证
机器学习
时间尺度
作者
Qingyu Yang,Jiale Yu,Jie Zheng
出处
期刊:Quantitative Biology
[Springer Science+Business Media]
日期:2025-09-21
卷期号:14 (1): e70013-e70013
被引量:1
摘要
The evolutionary scale modeling (ESM) series is promising to revolutionize protein science and engineering through large language models (LLMs), providing a robust framework for understanding the relationships among sequences, structures, and functions of proteins. Trained on a large number of unlabeled protein sequences, ESM models are able to capture intricate patterns of mutation and conservation, yielding insights into the structural and functional properties of proteins. Despite a growing body of literature surrounding ESM, existing surveys often fail to comprehensively describe its advancements or applications in a focused manner. This survey covers the latest developments of ESM, categorizing them into techniques of using ESM and downstream applications. Approximately 100 papers are selected and analyzed, highlighting recognized and innovative studies that exemplify the impact of ESM. Furthermore, we critically discuss the strengths and limitations of ESM to envision future applications. This review provides a valuable resource for researchers seeking to explore the power of ESM models and the emerging applications of LLMs in biology and medicine.
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