Capturing induced-fit effects: A geometry-aware and interpretable framework for robust drug-target affinity prediction.

score (0.787 on KIBA) and a Concordance Index (CI) of 0.902. Crucially, visualization analyses reveal that the model learns discriminative interaction patterns that align with biological intuition, providing interpretable insights into the binding mechanism. These results suggest that explicitly modeling interaction dynamics and representation geometry is essential for robust and explainable DTA prediction. The source code and datasets are available at https://github.com/ycqmyxh/DCR-DTA.