调整大语言模型和几何深度模型对蛋白质表示的对齐
Dong Shu1, Bingbing Duan2, Kai Guo3
1Northwestern University, Computer Science Department, Evanston, IL 60201, USA.
Patterns (New York, N.Y.)
|June 9, 2025
概括
将大语言模型 (LLM) 与蛋白质的几何深度模型 (GDM) 对齐至关重要. 更好的调整,特别是与结构数据的调整,提高了LLM的性能,减少了蛋白质科学中的错误.
科学领域:
- 计算生物学和生物信息学
- 人工智能在药物发现和蛋白质科学中的应用.
- 机器学习用于结构生物学.
背景情况:
- 大型语言模型 (LLM) 在生物应用中表现有前途.
- 几何深度模型 (GDM) 擅长捕获蛋白质中的结构信息.
- 整合这些模型需要了解它们的表示对齐.
研究的目的:
- 调查蛋白质域中LLM和GDM的多模式表示之间的对齐.
- 从模型和蛋白质的角度确定影响这种对齐的因素.
- 提出加强调整及其对下游任务影响的战略.
主要方法:
- 对三种LLM与四种蛋白质专业GDM进行综合评估.
- 基于模型架构,蛋白质特征和嵌入策略的对齐分析.
- 拟议的对齐增强技术的实验验证.
主要成果:
- 使用图形和3D结构数据的GDM显示与LLMs的优异对齐.
- 较大的LLM表现出增强的对齐能力.
- 蛋白质稀有性显著影响了对齐;增加GDM嵌入尺寸和使用双层投影头提高了对齐质量.
结论:
- 在LLM和GDM之间实现对齐是可以实现的,并可以通过特定的架构和数据策略进行优化.
- 对蛋白质特定数据的微调LLM和增强GDM嵌入是有效的方法.
- 改进的对齐直接与增强的下游任务性能和减少蛋白质重点多式联络LLM中的幻觉相关.
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