进化规模建模蛋白质语言模型下游应用的调查
Qingyu Yang1, Jiale Yu1, Jie Zheng1,2
1School of Information Science and Technology ShanghaiTech University Shanghai China.
Quantitative biology (Beijing, China)
|February 12, 2026
概括
进化规模建模 (ESM) 使用大型语言模型 (LLM) 来推进蛋白质科学. 本调查详细介绍了ESM技术和应用,提供了对蛋白质结构,功能和未来研究方向的见解.
科学领域:
- 蛋白质科学 蛋白质科学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 进化规模建模 (ESM) 系列利用大语言模型 (LLM) 进行蛋白质科学.
- ESM模型分析了大量未标记的蛋白质序列,以了解序列结构功能关系.
- 现有的文献缺乏对ESM进步和应用的专注,全面的调查.
研究的目的:
- 为进化规模建模 (ESM) 的最新发展提供全面的概述.
- 将ESM的进步分类为技术和下游应用.
- 批判性地评估ESM的优势和局限性,以便在未来进行研究.
主要方法:
- 系统审查和分析大约100篇关于ESM的研究论文.
- 将ESM发展分类为使用技术和应用领域.
- 批判性地讨论ESM的能力和限制.
主要成果:
- 确定利用ESM在蛋白质研究中的关键技术.
- 突出了各种下游应用,证明了ESM的影响.
- 分析了公认和创新的研究,展示了ESM的潜力.
结论:
- 欧洲蛋白质机制为蛋白质科学和工程提供了一个强大的框架.
- 这项调查是探索ESM和LLM在生物学和医学领域的研究人员的宝贵资源.
- 未来ESM在蛋白质研究中的应用是有希望的,需要进一步研究.
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