预训练的语言模型用于蛋白质和分子设计
Erdong Zhang1, Zilin Pan1, Zequan Yao1
1Intelligent Medical Research Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China. zhangerd@mail2.sysu.edu.cn.
Physical chemistry chemical physics : PCCP
|June 27, 2025
概括
预训练语言模型 (PLM) 正通过将自然语言处理技术应用于生物序列来彻底改变生物研究. 这些模型对蛋白质研究和药物设计应用有很大的前景.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 机器学习在生物学中的应用
背景情况:
- 预训练的语言模型 (PLM) 在自然语言理解方面表现出显著的能力.
- 生物序列与自然语言具有结构上的相似性,使PLM在生命科学中的应用成为可能.
研究的目的:
- 总结一下PLM在生物研究中的不断扩大的领域.
- 突出示范PLM,数据集及其在预测和生成任务中的应用.
主要方法:
- 利用生物序列和自然语言之间的结构平行.
- 在大型未标记的生物序列数据集上预先培训PLM.
- 为特定的生物研究任务微调PLM.
主要成果:
- 在生物研究应用中,PLM表现出了卓越的性能.
- 在蛋白质研究和药物设计中成功应用PLM.
- 在预测和生成生物任务中表现出潜力.
结论:
- PLM为生物研究挑战提供了强大的新解决方案.
- PLM与生物数据的整合是一个快速增长的领域.
- 对于药物发现和蛋白质工程的未来进步,PLM具有显著的希望.
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