Denoising diffusion models have shown great potential in multiple research
areas. Existing diffusion-based generative methods on de novo 3D molecule
generation face two major challenges. Since majority heavy atoms in molecules
allow connections to multiple atoms through single bonds, solely using
pair-wise distance to model molecule geometries is insufficient. Therefore, the
first one involves proposing an effective neural network as the denoising
kernel that is capable to capture complex multi-body interatomic relationships
and learn high-quality features. Due to the discrete nature of graphs,
mainstream diffusion-based methods for molecules heavily rely on predefined
rules and generate edges in an indirect manner. The second challenge involves
accommodating molecule generation to diffusion and accurately predicting the
existence of bonds. In our research, we view the iterative way of updating
molecule conformations in diffusion process is consistent with molecular
dynamics and introduce a novel molecule generation method named
Geometric-Facilitated Molecular Diffusion (GFMDiff). For the first challenge,
we introduce a Dual-Track Transformer Network (DTN) to fully excevate global
spatial relationships and learn high quality representations which contribute
to accurate predictions of features and geometries. As for the second
challenge, we design Geometric-Facilitated Loss (GFLoss) which intervenes the
formation of bonds during the training period, instead of directly embedding
edges into the latent space. Comprehensive experiments on current benchmarks
demonstrate the superiority of GFMDiff.

提出了一种名为几何促进分子扩散（GFMDiff）的新型分子生成方法，通过引入双轨 Transformer 网络（DTN）和几何促进损失（GFLoss）解决了构成二元边和模型分子几何形状的多体间相互关系的建模问题。在现有基准上的全面实验证明了 GFMDiff 的优越性。