对预训练的蛋白质语言模型的调查
Suresh Pokharel1, Pawel Pratyush1, Meenal Chaudhari2
1Golisano College of Computing and Information Sciences, Rochester Institute of Technology, Rochester, NY, USA.
Methods in molecular biology (Clifton, N.J.)
|July 2, 2025
概括
蛋白质语言模型 (PLM) 正在彻底改变生物信息学,基于NLP的大型语言模型 (LLM). 这些模型在蛋白质表示和设计方面表现出色,对未来的生物发现有很大的前景.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 生命科学中的人工智能
背景情况:
- 大型语言模型 (LLM) 已经改变了自然语言处理 (NLP).
- 这一成功激发了生物信息学蛋白语言模型 (PLM) 的发展.
- PLM利用大量的蛋白质数据集来捕获结构和功能信息.
研究的目的:
- 探索PLM在蛋白质生物信息学中的演变和影响.
- 总结关键的PLM架构及其应用.
- 突出PLM的新兴趋势和未来潜力.
主要方法:
- 审查PLM来自NLP变压器和LLM的起源.
- 按架构分类显著的PLM (仅编码器,编码器解码器,解码器).
- 检查高级应用程序,如微调,多式联络架构和缩小字母.
主要成果:
- PLM在蛋白质表示和de novo设计方面表现出显著的能力.
- 它们在分类和功能预测等任务中实现了高性能.
- PLM有效地从大规模的培训数据中捕获内在蛋白质信息.
结论:
- PLM代表了蛋白质生物信息学的重大进步.
- 在PLM架构和应用中不断的创新有望解决复杂的生物挑战.
- PLM准备在未来推动蛋白质的理解和工程方面的突破.
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