Episodic Relational Context Learning for Protein-Protein Interaction Prediction

Protein-protein interaction (PPI) prediction is essential for understanding cellular processes and disease mechanisms. However, existing learning-based methods struggle in the Neither-Seen (NS) setting, where neither protein in a test pair appears in the training data. To understand this limitation, we analyze the failure modes of existing baselines under the NS setting. Our analysis shows that, beyond the well-recognized effect of sequence similarity, the absence of PPI network topology for unseen proteins constitutes another key source of performance degradation. In this paper, our core idea is to induce relational context for unseen proteins by linking them to the training PPI network rather than leaving them isolated. To this end, we propose Episodic Relational Context Learning (ERCL) to improve PPI prediction generalization, particularly under the NS setting. For each query pair, ERCL connects the retrieved anchors from the PPI network to the query proteins via virtual edges, forming a relational context graph. This graph is fed into a relation-aware graph encoder and classified at the graph level, with the resulting label serving as the PPI prediction. To learn relational context from isolation, we construct each training episode by masking the observed interactions of query proteins, turning seen proteins into pseudo-unseen ones. On the cross-species benchmark with exclusively NS testing pairs, our method achieves state-of-the-art performance on all five testing species, with particularly pronounced gains on species more phylogenetically distant from the human training data. Moreover, our model achieves these gains with 585$\times$ fewer parameters than strong baselines such as PLM-interact, while being up to 29$\times$ faster in both training and inference.