Bridging Sequence and Structure with Unified Domain Adaptation for Drug-Target Interaction Prediction

Drug-target interaction (DTI) prediction is pivotal for accelerating drug discovery, yet existing methods struggle with generalization under cross-domain distribution shifts. Conventional approaches typically rely on static protein representations and assume distributional consistency between source and target domains, often resulting in poor calibration when encountering novel protein families or scaffolds. To address this issue, we present $\textbf{Bi}$-view $\textbf{Fold}$-aware prediction (BiFold), a novel unified interaction-adaptive framework that enhances generalization via dual-view representation learning and calibration-aware adaptation. BiFold synergistically models sequence and structural evidence, introducing a routing module to dynamically fuse the two views at the feature-channel level and exploit complementary information. Furthermore, we design a calibration-aware training principle that aligns domain statistics and encourages flatter source solutions. Crucially, target supervision is introduced only after a forward-only warm-up phase to prevent reinforcement of miscalibrated early predictions, effectively mitigating confirmation bias in pseudo-label learning. Extensive experiments demonstrate that BiFold consistently outperforms the state-of-the-art methods across diverse datasets in both in-domain and cross-domain settings. Additionally, interpretability analysis reveals that the routing module partitions the feature space into view-dominant and view-neutral subspaces, providing concrete evidence of adaptive multi-modal complementarity.