MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation
This addresses a key bottleneck in generating realistic 3D molecules for drug discovery or materials science, representing a novel method rather than an incremental improvement.
The paper tackles the atom-bond inconsistency problem in 3D molecule generation, where atoms are generated without considering bonds, leading to unrealistic molecules; it proposes MolDiff, a diffusion model that generates atoms and bonds simultaneously, achieving a three-fold improvement in success rate and better molecule quality.
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.