Accurate prediction of compound protein interactions (CPIs) is crucial for drug discovery. However, existing deep learning-based methods suffer from hidden biases and poor cross-domain generalization, leading to spurious correlations and inadequate representation of unseen compound-protein pairs. To address these limitations, we propose FuseMine, a multimodal deep learning framework that jointly leverages molecular structures and biological sequences for reliable CPI prediction. Specifically, FuseMine adopts a dual-representation strategy for each molecule. It employs a convolutional encoder to capture structural features, combined with pretrained large language models for extracting semantic information from sequences. We propose a novel Multi-modal Feature Orchestration Aggregation (MFOA) module that enables deep and synergistic fusion between the structural features and the sequential semantics of molecules, effectively capturing the complementary patterns across modalities. Additionally, we design a Reduction Differential Feature Mining (RDFM) module to further enhance the representation of discriminative features, thereby improving the model’s generalization capability. Extensive experiments on multiple benchmark datasets demonstrate that our framework consistently outperforms state-of-the-art methods in both intra-domain and cross-domain scenarios. These results highlight the synergistic value of combining structural and sequential data for CPIs.