HYPE-DTI: VALIDATION-GUARDED KNOWLEDGE EXCHANGE FOR LOW-LABEL DRUG–TARGET INTERACTION PREDICTION

Predicting drug–target interactions (DTIs) from few labeled examples requires models to combine molecular, sequence, and relational evidence without amplifying errors during cross-view exchange. Existing dual-view methods integrate these sources, but often couple information transfer with model updating and final fusion, making it difficult to reject unreliable transfers. We introduce Hype-DTI, a dual-expert framework that validates each stage of collaboration. A relation expert provides selected pseudo-labels to a hybrid self expert, the reverse-distillation update is retained only after validation, and the learned gate is deployed only when it outperforms the selected expert. Across three five-fold low-label benchmarks, with each fold containing 10 positive and 10 negative training pairs, Hype-DTI improves mean AUPR over MoseDTI by 6.10 points on AGO, 0.49 on Blocker, and 4.60 on E-. It also outperforms MoseDTI in every matched semi-inductive fold involving unseen drugs, targets, or both. On DrugCentral, Hype-DTI reaches $86.78\pm 0.38\%$ AUPR, the highest score among evaluated methods that do not use additional task-related DTI labels. ConPLex reaches $88.41\pm 0.58\%$ with labeled DUD-E pretraining, illustrating the benefit of stronger external supervision. These results support validation-guarded exchange in the evaluated low-label and semi-inductive DTI settings, while highlighting the effect of external supervision on cross-dataset performance.