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/.