标题： 用于蛋白质结构预测与设计的深度学习方法 显示英文标题

标题： Advanced Deep Learning Methods for Protein Structure Prediction and Design

摘要： 在AlphaFold获得诺贝尔奖后，深度学习用于蛋白质预测再次成为热门话题。 我们全面探讨应用于蛋白质结构预测和设计的先进深度学习方法。 首先检查预测架构的最新创新，并详细讨论如基于扩散的框架和新型成对注意力模块等改进。 本文分析了包括结构生成、评估指标、多序列比对处理和网络架构在内的关键组件，从而展示了计算蛋白质建模的最先进技术。 后续章节关注实际应用，展示了从单个蛋白质预测到复杂生物分子相互作用的案例研究。 深入探讨了提高预测准确性的策略以及将深度学习技术与实验验证相结合的方法。 后面的部分回顾了蛋白质设计的产业格局，突出了人工智能在生物技术中的变革性作用，并讨论了新兴的市场趋势和未来的挑战。 附录提供了数据库和开源工具等重要资源，使本书成为研究人员和学生的宝贵参考。 显示英文摘要

摘要： After AlphaFold won the Nobel Prize, protein prediction with deep learning once again became a hot topic. We comprehensively explore advanced deep learning methods applied to protein structure prediction and design. It begins by examining recent innovations in prediction architectures, with detailed discussions on improvements such as diffusion based frameworks and novel pairwise attention modules. The text analyses key components including structure generation, evaluation metrics, multiple sequence alignment processing, and network architecture, thereby illustrating the current state of the art in computational protein modelling. Subsequent chapters focus on practical applications, presenting case studies that range from individual protein predictions to complex biomolecular interactions. Strategies for enhancing prediction accuracy and integrating deep learning techniques with experimental validation are thoroughly explored. The later sections review the industry landscape of protein design, highlighting the transformative role of artificial intelligence in biotechnology and discussing emerging market trends and future challenges. Supplementary appendices provide essential resources such as databases and open source tools, making this volume a valuable reference for researchers and students.