Hierarchical Molecular Structure Network with Co-attention Mechanism for Drug-Drug Interaction Prediction

Drug-drug interactions (DDIs) are crucial in pharmacology, as they can either enhance therapeutic effects or lead to harmful adverse reactions when drugs are co-administered. Accurate prediction of DDIs is essential for ensuring drug safety and efficacy. Traditional DDI prediction methods often rely on manual domain knowledge, which is labor-intensive and time-consuming. Previous computational approaches have struggled with explainability and failed to effectively capture the multilevel structural information of drug molecules, especially when analyzing substructural components. Additionally, the lack of interaction between different structural levels often results in incomplete representations of drug molecules. In this work, we propose a novel Hierarchical Molecular Structure Representation Learning Network based on a Co-attention Mechanism (HMSN-CAM) for DDI prediction. HMSN-CAM encodes motif structures and extracts hierarchical molecular representations at the atom, motif, and molecule levels. These multi-level representations are then integrated using a co-attention mechanism to predict DDIs. Our extensive evaluations demonstrate that HMSN-CAM significantly outperforms state-of-the-art methods across multiple benchmarks. The model achieves over 98% accuracy on two datasets under the transduction setting, with performance metrics exceeding 99% for most evaluation criteria. Notably, HMSN-CAM also improves DDI prediction for pairs involving previously unseen drugs, yielding a 2.75% accuracy improvement over current methods. These results highlight the potential of HMSN-CAM for enhancing the prediction of drug interactions and improving drug safety.