Abstract: The increase in cancer drug resistance poses an enormous challenge in implementing effective therapeutic interventions. Combination therapy has emerged as an effective method to combat this resistance, but traditional methods for identifying viable drug combinations are often cumbersome and resource intensive. Recently, computational models have been developed to simplify the prediction of viable drug combinations, thereby improving the efficiency of this field of research. However, many existing models treat drug combinations independently, ignoring the crucial interaction dynamics between them. Moreover, these models fail to exploit the complementary insights provided by cell line multiomics data. In this work, we propose MVCASyn, an innovative deep learning model that predicts synergistic drug combinations. Compared with existing models, MVCASyn combines a dual-view representation learning module to precisely extract the multilevel features of atomic interactions, and adopts a cross-attention mechanism to fuse cell line multiomics data. Our experimental results show that MVCASyn consistently outperforms the current advanced models across all the evaluation metrics. Visualization experiments of drug atomic importance scores further emphasize the ability of MVCASyn to identify key drug substructures. A case study experiment also confirms that MVCASyn is effective in practical applications. The code of MVCASyn is publicly accessible at https://doi.org/10.57760/sciencedb.31476.