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<p>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 have been propelling the protein design field into a new era. 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 in silico evaluation approaches and pertinent applications. Finally, we identify the unresolved challenges and highlight research opportunities that merit deeper exploration.</p>

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Generative AI for controllable protein sequence design: A survey

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月3日

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  • Yiheng Zhu,
  • Zitai Kong,
  • Jialu Wu,
  • Mingze Yin,
  • Weize Liu,
  • Yuqiang Han,
  • Hongxia Xu,
  • Chang-Yu Hsieh,
  • Tingjun Hou,
  • Jian Wu

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 have been propelling the protein design field into a new era. 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 in silico evaluation approaches and pertinent applications. Finally, we identify the unresolved challenges and highlight research opportunities that merit deeper exploration.