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.

The complexity of cancer frequently results in diverse therapeutic responses among patients with the same cancer type undergoing identical treatments. Additionally, the development of anti-cancer drugs faces significant challenges due to extended timelines and high attrition rates during the process. Advances in machine learning, combined with the availability of extensive drug research databases, have facilitated the emergence of computational approaches designed to predict drug responses more accurately. However, the noise and uncertainty introduced by incomplete records across different biological data sources pose challenges, ultimately constraining the predictive capabilities of these models. To overcome these limitations, this study introduces a novel multi-modal learning-based drug response prediction framework, DSGPred. By constructing dynamic sample graphs, DSGPred accounts for missing modality types and quantities within individual samples, enabling a more granular understanding of data completeness. The framework integrates multi-modal heterogeneous graph convolutional networks with advanced fusion modules to deeply capture and synthesize diverse drug and cell line features. Additionally, DSGPred employs an interaction-focused feature extraction module to learn dual interaction modes, thereby enhancing the richness of drug-cell line interaction embeddings. Experimental evaluations on the benchmark and independent datasets indicate that DSGPred consistently surpasses existing methods in predictive performance. Furthermore, tests involving previously unseen drugs and cell lines validate DSGPred’s generalization ability. Practical applicability is further underscored through case studies, highlighting its utility in real-world scenarios, and offering robust predictions and insights for drug response prediction and personalized therapy design. The codes for DSGPred are available at https://github.com/zhc940702/DSGPred/.