Accurate drug-target interaction (DTI) prediction is essential for computational drug discovery, yet existing models often rely on pre-defined molecular descriptors or sequence-based embeddings with limited generalizability. We propose Tensor-DTI, a contrastive learning framework that integrates multimodal embeddings from molecular graphs, protein language models, and binding site predictions to improve interaction modeling. Tensor-DTI employs a Siamese Dual Encoder architecture, enabling it to capture both chemical and structural interaction features while distinguishing interacting from non-interacting pairs. Evaluations on multiple DTI benchmarks, including BIOSNAP, BindingDB, DAVIS, and PLINDER, demonstrate that Tensor-DTI outperforms existing sequence-based and graph-based models. Additionally, we assess its generalization to unseen drugs and proteins and explore its applicability to protein-RNA and peptide-protein interactions. Our findings highlight the benefits of integrating structural information with contrastive objectives to enhance interaction prediction accuracy and model interpretability.