D3I-COMCF: Dissimilarity-driven dual-interaction via co-occurrence motifs and complementary fragments for drug-drug...

Drug-drug interaction (DDI) prediction seeks to identify pharmacodynamic and pharmacokinetic interactions arising from the co-administration of multiple drugs with differing physicochemical properties. However, most existing methods predominantly focus on global molecular or atomic topologies, overlooking the critical role of dynamically derived functional substructures (motifs) in modulating drug activity and the occurrence and strength of DDI. To this end, we propose a dissimilarity-driven dual-interaction via co-occurrence motifs and complementary fragments (D3I-COMCF) framework that deeply integrates molecular-, motif-, and atomic-level information for accurate DDI prediction. Specifically, we construct, for the first time, a co-occurrence motifs molecular interaction graph (CMMIG) that unifies the global molecular structure, local physicochemical properties of motifs, and molecule-motif interaction topology. By weighting rare, dissimilar but information-rich motifs, D3I-COMCF was guided to focus on more discriminative functional units. Moreover, based on the maximum common substructure mapping and its dissimilar one-hop neighbor extension, we dynamically incorporated complementary fragments attached to the shared backbone into a fragment interaction graph, revealing electronic complementarity and spatial cooperativity with computational efficiency. Finally, hierarchical multi-block integration of the three levels enables cross-scale joint representation of drug pairs. Extensive experiments on multiple public datasets verified the effectiveness of D3I-COMCF, which achieved strong and stable performance under standard benchmark protocols and showed promising cold-start generalization and interpretability. Additional hard-negative analysis further indicates that D3I-COMCF remains sensitive to known positive DDIs, while controlling false positives among structurally similar candidate pairs not annotated as known positives remains challenging.