  Molecular property prediction is a key component of AI-driven drug discovery
and molecular characterization learning. Despite recent advances, existing
methods still face challenges such as limited ability to generalize, and
inadequate representation of learning from unlabeled data, especially for tasks
specific to molecular structures. To address these limitations, we introduce
DIG-Mol, a novel self-supervised graph neural network framework for molecular
property prediction. This architecture leverages the power of contrast learning
with dual interaction mechanisms and unique molecular graph enhancement
strategies. DIG-Mol integrates a momentum distillation network with two
interconnected networks to efficiently improve molecular characterization. The
framework's ability to extract key information about molecular structure and
higher-order semantics is supported by minimizing loss of contrast. We have
established DIG-Mol's state-of-the-art performance through extensive
experimental evaluation in a variety of molecular property prediction tasks. In
addition to demonstrating superior transferability in a small number of
learning scenarios, our visualizations highlight DIG-Mol's enhanced
interpretability and representation capabilities. These findings confirm the
effectiveness of our approach in overcoming challenges faced by traditional
methods and mark a significant advance in molecular property prediction.
