  Molecule generation, especially generating 3D molecular geometries from
scratch (i.e., 3D \textit{de novo} generation), has become a fundamental task
in drug designs. Existing diffusion-based 3D molecule generation methods could
suffer from unsatisfactory performances, especially when generating large
molecules. At the same time, the generated molecules lack enough diversity.
This paper proposes a novel diffusion model to address those two challenges.
  First, interatomic relations are not in molecules' 3D point cloud
representations. Thus, it is difficult for existing generative models to
capture the potential interatomic forces and abundant local constraints. To
tackle this challenge, we propose to augment the potential interatomic forces
and further involve dual equivariant encoders to encode interatomic forces of
different strengths. Second, existing diffusion-based models essentially shift
elements in geometry along the gradient of data density. Such a process lacks
enough exploration in the intermediate steps of the Langevin dynamics. To
address this issue, we introduce a distributional controlling variable in each
diffusion/reverse step to enforce thorough explorations and further improve
generation diversity.
  Extensive experiments on multiple benchmarks demonstrate that the proposed
model significantly outperforms existing methods for both unconditional and
conditional generation tasks. We also conduct case studies to help understand
the physicochemical properties of the generated molecules.
