  Artificial Intelligence predicts drug properties by encoding drug molecules,
aiding in the rapid screening of candidates. Different molecular
representations, such as SMILES and molecule graphs, contain complementary
information for molecular encoding. Thus exploiting complementary information
from different molecular representations is one of the research priorities in
molecular encoding. Most existing methods for combining molecular
multi-modalities only use molecular-level information, making it hard to encode
intra-molecular alignment information between different modalities. To address
this issue, we propose a multi-granularity fusion method that is MolFusion. The
proposed MolFusion consists of two key components: (1) MolSim, a
molecular-level encoding component that achieves molecular-level alignment
between different molecular representations. and (2) AtomAlign, an atomic-level
encoding component that achieves atomic-level alignment between different
molecular representations. Experimental results show that MolFusion effectively
utilizes complementary multimodal information, leading to significant
improvements in performance across various classification and regression tasks.
