Drugs-drugs interactions(DDI) are entities composed of different chemical substructures
(functional groups). In existing methods that predict drug–drug interactions based on the usage of
substructures, each node is considered the center of a substructure, and adjacent nodes eventually
become centers of similar substructures, resulting in redundancy. Furthermore, the significant differ-
ences in structure and properties among compounds can lead to unrelated pairings, making it difficult
to integrate information. This heterogeneity negatively affects the prediction results. To address
these issues, we propose a drug–drug interaction prediction method based on substructure signature
learning (DDI-SSL). This method extracts useful information from local subgraphs surrounding drugs
and effectively utilizes substructures to assist in predicting drug side effects. Additionally, a deep
clustering algorithm is used to aggregate similar substructures, allowing any individual subgraph to
be reconstructed using this set of global signatures. Furthermore, we developed a layer-independent
collaborative attention mechanism to model the mutual influence between drugs, generating signal
strength scores for each class of drugs to mitigate noise caused by heterogeneity. Finally, we evaluated
DDI-SSL on a comprehensive dataset and demonstrated improved performance in DDI prediction
compared to state-of-the-art methods.