Accurately predicting drug-protein interactions (DPIs) is critical in computational biomedicine, as it facilitates the understanding of molecular pharmacodynamics and accelerates drug discovery. Existing prediction methods fall into two main categories, atomic-scale structure-based approaches, which exhibit strong cross-domain generalization but limited within-domain accuracy, and molecular-scale network-based methods, which perform well in within-domain scenarios but struggle with cross-domain generalization. A key challenge lies in effectively integrating multi-scale information to overcome the limitations of single-scale models. To address this, we propose MGDPI, a multi-scale representation learning framework enhanced by graph regularization. MGDPI introduces a graph regularization loss function based on network information to guide the learning of drug and protein representations from structural data. By seamlessly integrating multi-scale information, MGDPI significantly improves both generalization and predictive performance. Experimental results demonstrate that MGDPI outperforms state-of-the-art models in within-domain prediction accuracy under various imbalance scenarios on the BioSNAP dataset. Furthermore, MGDPI achieves superior performance across three independent cross-domain datasets, highlighting its exceptional generalizability and predictive power. These findings underscore MGDPI’s potential not only as a robust DPI prediction tool but also as an effective solution to data imbalance and cross-domain challenges.