biostudies-literatureUnknown21(1)Amiri MDrug-drug interactions (DDI) represent a significant clinical challenge in modern healthcare, contributing to over 125,000 deaths annually in the United States alone. Current computational approaches face substantial limitations in capturing long-range molecular dependencies and generalizing to novel drug combinations. Traditional Graph Neural Networks (GNNs) suffer from over-smoothing and locality bias, while sequence-based methods fail to adequately represent three-dimensional molecular structures. To address these limitations, we propose Graph Former-CL, a novel deep learning framework that synergistically combines Graph Transformer architecture with contrastive learning for DDI prediction. Our approach features four key innovations: (1) a hierarchical Graph Transformer with position-awPloS onee0339971https://www.ebi.ac.uk/biostudies/studies/S-EPMC12857962biostudies-literatureGraph former-CL: A novel graph transformer with contrastive learning framework for enhanced drug-drug interaction prediction.PMC12857962Amiri MZare OfalseGraph former-CL: A novel graph transformer with contrastive learning framework for enhanced drug-drug interaction prediction.Drug-drug interactions (DDI) represent a significant clinical challenge in modern healthcare, contributing to over 125,000 deaths annually in the United States alone. Current computational approaches face substantial limitations in capturing long-range molecular dependencies and generalizing to novel drug combinations. Traditional Graph Neural Networks (GNNs) suffer from over-smoothing and locality bias, while sequence-based methods fail to adequately represent three-dimensional molecular structures. To address these limitations, we propose Graph Former-CL, a novel deep learning framework that synergistically combines Graph Transformer architecture with contrastive learning for DDI prediction. Our approach features four key innovations: (1) a hierarchical Graph Transformer with position-aw2026-01-01T00:00:00Z20262026-06-09T05:42:47.038Z2026-06-09T03:08:09.302ZS-EPMC128579624161592710.1371/journal.pone.0339971