{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["21(1)"],"submitter":["Amiri M"],"pubmed_abstract":["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-aw"],"journal":["PloS one"],"pagination":["e0339971"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12857962"],"repository":["biostudies-literature"],"pubmed_title":["Graph former-CL: A novel graph transformer with contrastive learning framework for enhanced drug-drug interaction prediction."],"pmcid":["PMC12857962"],"pubmed_authors":["Amiri M","Zare O"],"additional_accession":[]},"is_claimable":false,"name":"Graph former-CL: A novel graph transformer with contrastive learning framework for enhanced drug-drug interaction prediction.","description":"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-aw","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026","modification":"2026-06-09T05:42:47.038Z","creation":"2026-06-09T03:08:09.302Z"},"accession":"S-EPMC12857962","cross_references":{"pubmed":["41615927"],"doi":["10.1371/journal.pone.0339971"]}}