  Generating desirable molecular structures in 3D is a fundamental problem for
drug discovery. Despite the considerable progress we have achieved, existing
methods usually generate molecules in atom resolution and ignore intrinsic
local structures such as rings, which leads to poor quality in generated
structures, especially when generating large molecules. Fragment-based molecule
generation is a promising strategy, however, it is nontrivial to be adapted for
3D non-autoregressive generations because of the combinational optimization
problems. In this paper, we utilize a coarse-to-fine strategy to tackle this
problem, in which a Hierarchical Diffusion-based model (i.e.~HierDiff) is
proposed to preserve the validity of local segments without relying on
autoregressive modeling. Specifically, HierDiff first generates coarse-grained
molecule geometries via an equivariant diffusion process, where each
coarse-grained node reflects a fragment in a molecule. Then the coarse-grained
nodes are decoded into fine-grained fragments by a message-passing process and
a newly designed iterative refined sampling module. Lastly, the fine-grained
fragments are then assembled to derive a complete atomic molecular structure.
Extensive experiments demonstrate that HierDiff consistently improves the
quality of molecule generation over existing methods
