Fusion-driven graph representation enhancement for predicting interactions of new drugs

Accurate prediction of drug–drug interactions (DDIs) for newly synthesized compounds enables early, in-silico safety screening in drug discovery and formulary review. We target the cold-start regime, where (i) new compounds are topologically isolated on external biomedical knowledge graphs (KGs) and on the DDI graph, and (ii) sparse supervision hampers the learning of discriminative representations. We propose an early-fusion method (LINCS-DDI) that inserts shared substructure nodes to connect a molecular-fingerprint knowledge graph with the DDI graph, turning structural similarity into topological links, and providing two-hop connectivity directly from the Simplified Molecular Input Line Entry System (SMILES) without prior inclusion in external KGs. Building on this substrate, we introduce Native Dual-View Contrastive Learning (NDV-CL): within a single pass of a flow-based graph neural network (GNN), forward and reverse message-passing representations of the same drug pair are treated as deterministic positives, while label-guided negatives (screened using only training-split interaction labels) are mined within the induced subgraph, improving representation quality without stochastic augmentations. Under strict cold-start settings on two open-source datasets, LINCS-DDI improves macro-F1 by up to 4.1% over the best baseline and reduces contrastive overhead by up to 66%. These properties make the approach suitable for routine, large-scale preclinical DDI triage, prioritizing high-risk combinations for wet-lab validation, and informing pharmacovigilance pipelines. • Cold-start DDI prediction without reliance on external Bio-KGs. • Early fusion via MF-KG builds two-hop connectivity from substructure. • Native dual-view contrastive learning avoids random augmentations. • Improved macro-F1 by up to 4.1% on DrugBank & TWOSIDES. • 66% lower contrastive-learning cost vs. random augmentation.