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However, the lack of a clear and reasonable co\u2010assembly strategy has hindered the wide application and promotion of drug\u2010co assembly. This paper introduces a deep learning\u2010based sonosensitizer\u2010drug interaction (SDI) model to predict the particle size of the drug mixture. To analyze the factors influencing the particle size after mixing, the graph neural network is employed to capture the atomic, bond, and structural features of the molecules. A multi\u2010scale cross\u2010attention mechanism is designed to integrate the feature representations of different scale substructures of the two drugs, which not only improves prediction accuracy but also allows for the analysis of the impact of molecular structures on the predictions. Ablation experiments evaluate the impact of molecular properties, and comparisons with other machine and deep learning methods show superiority, achieving 90.00% precision, 96.00% recall, and 91.67% F1\u2010score. 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