Denoising diffusion probabilistic models (DDPMs) have recently taken the
field of generative modeling by storm, pioneering new state-of-the-art results
in disciplines such as computer vision and computational biology for diverse
tasks ranging from text-guided image generation to structure-guided protein
design. Along this latter line of research, methods such as those of Hoogeboom
et al. 2022 have been proposed for unconditionally generating 3D molecules
using equivariant graph neural networks (GNNs) within a DDPM framework. Toward
this end, we propose GCDM, a geometry-complete diffusion model that achieves
new state-of-the-art results for 3D molecule diffusion generation by leveraging
the representation learning strengths offered by GNNs that perform
geometry-complete message-passing. Our results with GCDM also offer preliminary
insights into how physical inductive biases impact the generative dynamics of
molecular DDPMs. The source code, data, and instructions to train new models or
reproduce our results are freely available at
this https URL.

提出了 GCDM 模型，该模型采用几何完整的消息传递图神经网络，在 DDPM 框架内宣称新的 3D 分子扩散生成的最新成果，并提供了与分子 DDPM 生成动态相关的物理归纳偏差的初步见解。