发表机构
Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本综述指出蛋白质生成建模虽用生成模型提升了结构采样效率，但存在非物理特征与分布外失效问题，提出尊重大分子物理原理、结合物理表示与实验数据是功能蛋白质设计生成建模的基础。
AI 中文摘要
在蛋白质建模中，物理方程似乎已被直接在结构数据上训练的生成模型所取代。通过学习从噪声到数据的映射，从头采样蛋白质结构的效率大幅提升。然而，这类模型也可能学习到非物理特征，且在蛋白质设计中典型的分布外场景下会失效。本综述强调了蛋白质生成建模流程中物理原理的体现之处，以及未对大分子系统中与物理相关的组分进行建模时遗留的问题。我们提出一种观点：尊重大分子系统的 underlying 物理原理——日益通过基于物理的学习表示，并利用实验数据更新生成模型——是实现功能蛋白质设计生成建模的基础。
英文摘要
Physical equations in protein modeling appear to have been replaced by generative models trained directly on structure data. By learning a mapping from noise to data, sampling de novo protein structures has become much more efficient. However, such models can also learn non-physical features and break down with out-of-distribution settings which are typical in protein design campaigns. In this review, we highlight where the physics persists in protein generative modeling pipelines and the issues that linger when physically relevant components of a macromolecular system are left unmodeled. We present a perspective that respecting the underlying physics of macromolecular systems, increasingly through learned representations that are physically grounded and updating generative models with experimental data, is foundational to generative modeling for functional protein design.
