AnchorPose: Geometry-Aware MOF Assembly through Meso-Grained Pose Generation

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 explicitly incorporates building-block geometry. AnchorPose uses a small set of representative atoms as anchors, combines local shape with the current spatial state for pose prediction, and generates anchor positions with Bayesian Flow Networks. Rigid alignment with the known local geometry then recovers the poses of complete building blocks. This representation connects point-level spatial prediction with block-level structural constraints, bringing block geometry directly into generation while preserving intra-block structure without treating all atomic coordinates as assembly variables. Experiments on the MOF structure prediction benchmark show that AnchorPose achieves higher structure match rates than existing methods.