MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule
  Diffusion Generation
Xingang Peng Jiaqi Guan Qiang Liu Jianzhu Ma
Deep generative models have recently achieved superior performance in 3D
molecule generation. Most of them first generate atoms and then add chemical
bonds based on the generated atoms in a post-processing manner. However, there
might be no corresponding bond solution for the temporally generated atoms as
their locations are generated without considering potential bonds. We define
this problem as the atom-bond inconsistency problem and claim it is the main
reason for current approaches to generating unrealistic 3D molecules. To
overcome this problem, we propose a new diffusion model called MolDiff which
can generate atoms and bonds simultaneously while still maintaining their
consistency by explicitly modeling the dependence between their relationships.
We evaluated the generation ability of our proposed model and the quality of
the generated molecules using criteria related to both geometry and chemical
properties. The empirical studies showed that our model outperforms previous
approaches, achieving a three-fold improvement in success rate and generating
molecules with significantly better quality.