Hypergraph-guided Complex Structure Enhancement for Accurate 3D Molecule Generation

To accelerate the development of drug discovery, de novo 3D molecule generation has achieved many advances, aiming to synthesise accurate and reasonable 3D molecules from the given noise. Since molecules usually involve complex and diverse structures, it is challenging to precisely construct atoms and corresponding relations. Currently, most methods mainly employ flow- or diffusion-based techniques to effectively improve the quality of 3D molecules. However, they usually rely on atom-level and pairwise relation modelling, which may be insufficient for explicitly capturing higher-order chemical structures, e.g., aromatic rings, resulting in unstable patterns, unrealistic scaffolds, and degraded chemical validity in the generated molecules. To this end, inspired by the powerful relationship modelling capabilities of hypergraphs, we explore exploiting hypergraphs to perform higher-order structure enhancement. And a method of HSA-Mol3D is proposed. Concretely, HSA-Mol3D introduces a dynamic hypergraph branch into the generative backbone to model higher-order chemical structures. We further construct a staged and stabilised hypergraph fusion strategy in conjunction to make such higher-order information stable and effective throughout generation. Our proposed method enhances the stability of higher-order structures by progressively integrating hypergraph information into the generation process of the primary flow field model. Experiments show that our method achieves competitive performance on drug-like molecular generation, with particularly strong results in scaffold quality and the investigation of novel, higher-order skeletal medicinal molecules with superior properties.