AnchorPose：通过介观粒度姿态生成实现几何感知的MOF组装

中文摘要 AI 辅助

从给定的构建块预测金属有机框架（MOF）结构需要恢复它们在周期性晶体中的位置和取向。旋转误差的空间效应是几何依赖且各向异性的。相同的角度误差根据块的大小、形状和旋转轴可产生不同的原子位移。因此，仅凭角度误差而不参考特定块几何，无法完全描述姿态误差的空间后果。我们引入了AnchorPose，一个介观粒度的姿态生成框架，将这种几何依赖性纳入其生成表示中。它通过一小部分代表性原子来表示每个块，将它们的局部几何与当前空间状态相结合，并使用贝叶斯流网络生成它们的坐标。已知的原子对应关系使得刚性对齐能够恢复完整的构建块姿态，并将几何一致的点返回到生成过程。这种设计将点级空间预测与块级结构约束联系起来。几何参与姿态状态及其预测，而刚性重建保留了块内结构，无需将所有原子坐标视为组装变量。在MOF基准上，AnchorPose在单候选匹配率上优于所比较的块级和全原子基线。

英文摘要

Predicting metal-organic framework (MOF) structures from given building blocks requires recovering their positions and orientations in a periodic crystal. The spatial effects of rotation errors are geometry-dependent and anisotropic. The same angular error can produce different atomic displacements depending on block size, shape, and rotation axis. Angular error alone, without reference to the specific block geometry, therefore cannot fully describe the spatial consequences of a pose error. We introduce AnchorPose, a meso-grained pose generation framework that incorporates this geometric dependence into its generative representation. It represents each block through a small set of representative atoms, combines their local geometry with the current spatial state, and generates their coordinates with Bayesian Flow Networks. Known atom correspondences enable rigid alignment to recover complete building-block poses and return geometrically consistent points to the generation process. This design connects point-level spatial prediction with block-level structural constraints. Geometry participates in the pose state and its prediction, while rigid reconstruction preserves intra-block structure without treating all atomic coordinates as assembly variables. On the MOF benchmark, AnchorPose improves single-candidate match rates over the compared block-level and all-atom baselines.

发表机构

• Institute of Physics, Chinese Academy of Sciences（中国科学院物理研究所）
• University of the Chinese Academy of Sciences（中国科学院大学）
• Tsinghua University（清华大学）

机构由 AI 辅助整理，请以论文原文为准。

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27 pages