Graph former-CL: A novel graph transformer with contrastive learning framework for enhanced drug-drug interaction prediction.

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

SUBMITTER: Amiri M 

PROVIDER: S-EPMC12857962 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature