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

## TL;DR

This paper introduces a new deep learning framework called Graph Former-CL to better predict drug-drug interactions using graph transformers and contrastive learning.

## Contribution

The novel framework combines graph transformers with contrastive learning to improve drug-drug interaction prediction and generalization.

## Key findings

- Graph Former-CL achieves 98.2% accuracy on DrugBank and 89.4% on TWOSIDES.
- The model shows 85.6% accuracy for novel drugs in inductive settings.
- It outperforms state-of-the-art methods with statistically significant improvements.

## 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-aware multi- head self-attention to capture both local and global molecular patterns, (2) a domain-specific contrastive learning module with molecular augmentation strategies, (3) a cross-modal fusion mechanism integrating SMILES sequences with graph representations, and (4) an adaptive pooling strategy for multi-scale molecular representation. Comprehensive evaluation on four benchmark datasets demonstrates superior performance, with Graph Former-CL achieving 98.2% accuracy on DrugBank and 89.4% on TWOSIDES, both representing statistically significant improvements (p < 0.001) over state-of-the-art methods. Notably, the framework achieves 85.6% accuracy for novel drugs in inductive settings, demonstrating robust generalization capabilities essential for real-world clinical applications.