  Molecular representation learning has shown great success in advancing
AI-based drug discovery. The core of many recent works is based on the fact
that the 3D geometric structure of molecules provides essential information
about their physical and chemical characteristics. Recently, denoising
diffusion probabilistic models have achieved impressive performance in 3D
molecular representation learning. However, most existing molecular diffusion
models treat each atom as an independent entity, overlooking the dependency
among atoms within the molecular substructures. This paper introduces a novel
approach that enhances molecular representation learning by incorporating
substructural information within the diffusion process. We propose a novel
diffusion model termed SubGDiff for involving the molecular subgraph
information in diffusion. Specifically, SubGDiff adopts three vital techniques:
i) subgraph prediction, ii) expectation state, and iii) k-step same subgraph
diffusion, to enhance the perception of molecular substructure in the denoising
network. Experimentally, extensive downstream tasks demonstrate the superior
performance of our approach. The code is available at
https://github.com/youjibiying/SubGDiff.
