自动驾驶汽车,社交网络,ChatGPT和DALL-E2背后的技术如何改变结构生物学
1International institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland.
概括
包括卷积神经网络 (CNN) 和大型语言模型 (LLM) 在内的深度学习模型正在彻底改变蛋白质结构预测和设计. 这些先进的AI工具为理解和工程蛋白质提供了新的可能性.
科学领域:
- 计算生物学 计算生物学
- 人工智能的人工智能
- 结构生物学 结构生物学
背景情况:
- 深度神经网络 (NN) 在文本 (例如,ChatGPT) 和图像 (例如,DALL-E2) 领域表现出显著的表现.
- 关键的NN架构包括卷积NN (CNN),大语言模型 (LLM),否定扩散概率模型 (DDPM) /噪声条件评分网络 (NCSN) 和图形NN (GNN).
- 这些模型对计算机视觉,自然语言处理和网络管理产生了重大影响.
研究的目的:
- 审查深度学习技术在蛋白质结构生物学中的应用.
- 以突出蛋白质结构预测,逆折叠,蛋白质设计和由深度学习驱动的小分子设计方面的进展.
- 为实验结构生物学家提供深度学习方法的入门教程.
主要方法:
- 将蛋白质视为序列 (文本),图像或残留物图.
- 应用CNN用于像图像的蛋白质表示.
- 使用LLM进行基于序列的蛋白质分析和设计.
- 使用DDPMs/NCSNs和GNNs进行结构预测和分子设计.
主要成果:
- 在蛋白质结构预测方面,CNN,LLM,DDPM/NCSN和GNN已经取得了重大进展.
- 这些深度学习方法正在实现反向折叠和新型蛋白质设计的突破.
- 在使用深度学习设计小分子方面取得了重大进展.
结论:
- 深度学习为解决蛋白质结构生物学中的复杂问题提供了强大的工具.
- 人工智能的整合,特别是NN,正在改变蛋白质的研究和设计.
- 本综述为有兴趣利用深度学习的结构生物学家提供了基本的理解.
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