面向可控蛋白质序列设计的生成式人工智能：综述
Generative AI for Controllable Protein Sequence Design: A Survey
AI总结：
本综述系统回顾了生成式AI在可控蛋白质序列设计中的最新进展，涵盖基础任务、关键生成模型与优化算法、具体设计任务应用，并指出了当前未解决的挑战与未来研究机遇。
摘要：
设计具有目标功能的新型蛋白质序列是蛋白质工程的核心主题，影响着药物发现和酶工程等广泛领域。然而，由于时间和资金限制，在这一庞大的组合搜索空间中进行探索仍然是一项严峻挑战。随着人工智能的变革性进展，特别是在生成模型和优化算法领域，这一局面正在迅速演变，推动蛋白质设计领域迈向前所未有的革命。在本综述中，我们系统回顾了生成式人工智能在可控蛋白质序列设计方面的最新进展。作为铺垫，我们首先概述了蛋白质序列设计中的基础任务（涉及相关约束条件），并介绍了关键的生成模型和优化算法。随后，我们对每项设计任务进行了深入回顾，并讨论了相关应用。最后，我们指出了尚未解决的挑战，并强调了值得深入探索的研究机遇。
英文摘要：
The design of novel protein sequences with targeted functionalities underpins a central theme in protein engineering, impacting diverse fields such as drug discovery and enzymatic engineering. However, navigating this vast combinatorial search space remains a severe challenge due to time and financial constraints. This scenario is rapidly evolving as the transformative advancements in AI, particularly in the realm of generative models and optimization algorithms, have been propelling the protein design field towards an unprecedented revolution. In this survey, we systematically review recent advances in generative AI for controllable protein sequence design. To set the stage, we first outline the foundational tasks in protein sequence design in terms of the constraints involved and present key generative models and optimization algorithms. We then offer in-depth reviews of each design task and discuss the pertinent applications. Finally, we identify the unresolved challenges and highlight research opportunities that merit deeper exploration.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
