通过深度学习预测蛋白质结构:一个深入的审查
Yajie Meng1, Zhuang Zhang1, Chang Zhou1
1College of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, China.
Frontiers in pharmacology
|April 18, 2025
概括
深度学习显著提升了蛋白质结构预测,这对于药物发现至关重要. 本综述指导使用深度学习工具和大型语言模型来准确确定蛋白质结构.
科学领域:
- 计算生物学 计算生物学
- 生物化学 生物化学
- 药物发现 药物发现 药物发现
背景情况:
- 准确的蛋白质结构对于理解生物功能和开发治疗方法至关重要.
- 确定蛋白质结构的实验方法往往是缓慢的,昂贵的和劳动密集的.
- 在已知的蛋白质序列数量和实验确定结构之间存在很大的差距.
研究的目的:
- 在蛋白质结构预测中提供深度学习应用的全面审查.
- 引导研究人员利用深度学习方法和工具.
- 讨论大型语言模型在这个领域的发展中的作用.
主要方法:
- 对深度学习用于蛋白质结构预测的现有文献的审查.
- 对蛋白质结构数据的相关数据库的探索.
- 分析最先进的深度学习模型,包括大型语言模型.
主要成果:
- 深度学习提供了一种强大的计算方法,以弥合蛋白质结构确定方面的差距.
- 最近的进展包括复杂的深度学习模型和适合此任务的大型语言模型.
- 数据库和工具对于有效应用这些方法至关重要.
结论:
- 深度学习正在彻底改变蛋白质结构预测,加速药物发现.
- 该审查强调了当前的方法和该领域的未来方向.
- 讨论了使用人工智能预测蛋白质结构的挑战和机会.
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