MolFusion: Multimodal Fusion Learning for Molecular Representations via Multi-granularity Views

Artificial intelligence advances drug design by predicting drug properties through the encoding of drug molecules. Since different molecular representations contain complementary information, a large amount of research is currently dedicated to the fusion of these representations. However, current multimodal molecular studies rely more on single-granularity methods, resulting in the loss of atomic-level information between representations. Inspired by the success of multi-granularity approaches, we introduce MolFusion, an innovative method for multi-granularity molecular representation fusion. MolFusion comprises two core components: MolSim for molecular-level fusion and AtomAlign for atomic-level fusion. Comprehensive experiments show that our method outperforms all powerful baselines in terms of average performance across both classification and regression tasks, with a particular enhancement in regression tasks. Our code and dataset will be available on https://github.com/Mengqi97/MolFusion.