respectively. Although a performance gap between the seen and unseen proteins was observed, our model outperformed the other models in both cases. This observation further supports the effectiveness of our method in improving its generalizability. Similar results were obtained using the BioPrint dataset (Figure ). Nevertheless, we selected unlabeled data with more ambiguous scores (0.2 ≤ 𝑓 ! (𝑥 = % ) ≤ 0.5 in the case of kinase families) as the added negative samples, which are hereafter referred to as near-boundary samples. To validate the effectiveness of the adopted score threshold, we compared the learning process on the ChEMBL dataset and the predictive performance on the Davis dataset with two other score thresholds: 𝑓 ! (𝑥 = % ) ≤ 0.2 and 𝑓 ! (𝑥 = % ) ≤ 0.5. The learning process of the model at the last iteration demonstrated that the validation ROC-AUC using confident negative samples (𝑓 ! (𝑥 = % ) ≤ 0.2) reached a plateau earlier than that using near-boundary samples (Figure ). Meanwhile, the PR-AUC score of the model using near-boundary samples was better than that using confident samples (Figure ). The model self-trained with the threshold of 𝑓 ! (𝑥 = % ) ≤ 0.5 showed intermediate behaviors (Figure ). These results indicated that the near-boundary samples are more informative than the confident samples for model training; thus, selecting nearboundary samples is crucial for achieving high generalizability. Because confident samples are overly distant from positive interactions, they constitute an easy-to-learn dataset, which is supported by the rapid convergence of the validation ROC-AUC (Figure ). This easy-to-learn situation would result in the poor generalizability (Figure ), which is compatible with the situation discussed by Yaseen et al. when using similaritycontrolled negative samples . Similar behavior was also observed in the GPCR models (Figure ).