GraphTransDTI: A novel hybrid framework combining graph transformer and CNN-BiLSTM for enhanced Drug-Protein Interaction prediction

Abstract

Drug-protein interaction (DTI) prediction is a pivotal step in the drug discovery and repurposing process, helping to minimize experimental costs and time. However, existing deep learning methods often face limitations in simultaneously capturing the spatial structure of drug molecules and the deep contextual correlation with protein sequences. To address this issue, we propose GraphTransDTI, a synergistic hybrid framework that integrates a Graph Transformer to represent drug graph structures, a CNN-BiLSTM network to encode protein sequence context, and a Cross-Attention mechanism to model cross-domain interactions. Comprehensive experiments on two benchmark datasets, KIBA and Davis, across three rigorous scenarios: random splits, cold drug splits, and cold target splits demonstrate that GraphTransDTI achieves competitive performance compared to current state-of-the-art baseline models. Our findings confirm that the strategic combination of graph structural information and sequential attention mechanisms significantly enhances prediction accuracy and robustness in cold-start scenarios, offering a reliable and well-validated approach for high-precision virtual drug screening systems.