ReMatch: Learning to Rank Alternative Pairings for RNA–Protein Interaction Prediction

Predicting RNA–protein interactions from sequence can help reveal how RNAs are processed, localized, and translated. Although pretrained sequence models and expressive interaction architectures provide rich molecular features, generalization to unfamiliar RNAs and proteins remains challenging. Training often relies on an uneven mix of evidence, that is, observed interactions have experimental support, but many negatives are untested pairs formed by re-pairing molecules. Treating these alternatives as confirmed negatives can suppress plausible interactions, especially when hard-example selection favors high-scoring pairs. These uncertain alternatives can still be useful, however, if they provide comparisons rather than additional negative labels. We introduce ReMatch, short for Ranking-enhanced Matching, which combines binary classification with bidirectional ranking. For each observed interaction, ranking teaches the model to prefer that pair over alternatives formed by replacing either molecule. The comparisons act on the classifier’s interaction log-odds, allowing an alternative to remain above the interaction boundary while the observed pair receives a higher score. Across five validation folds, adding ranking improves accuracy by approximately 4.7 and 2.5 percentage points on two benchmarks with unseen RNA and protein identities, with AUROC gains on both. Additional classification supervision under the same hard-pair selection policy does not reproduce these gains, supporting the value of learning relative preferences beyond selecting difficult examples. Ranking also improves preference for observed molecular assignments in existing predictors, extending the benefit of this supervision across interaction architectures. Code and preprocessing pipelines are available at https://anonymous.4open.science/r/rematch/.