CrossLinker: Aligning Relational and Sequential Contexts for Drug-Target Interaction Prediction in Cold-Start and Few-Shot Scenarios

Accurate prediction of drug-target interactions (DTIs) is foundational to drug development. Over the years, representation learning methods based on sequences and relational knowledge have shown considerable promise in this field. However, DTI prediction remains a challenging task, particularly in cold-start settings and few-shot scenarios involving novel drugs or proteins. Therefore, we propose a novel DTI prediction framework. To enhance the model's generalization in settings with scarce labels and unseen entities, we introduce a link-based contrastive learning strategy. Instead of aligning entity-level global features, this strategy aligns fine-grained local features derived from both the sequence and relational modalities. Complementing this, we introduce a link-based cross-attention mechanism. This mechanism captures contextual features specific to individual drug-protein pairs conditioned on different links, providing necessary local features for contrastive learning strategies. Our model was evaluated on both cold-start and few-shot datasets involving unseen drugs or proteins, and significantly outperformed state-of-the-art (SOTA) methods. Furthermore, when evaluated in conventional data-rich settings, our model still demonstrates superior performance over current approaches.