Reliable evaluation and learning in multi-input biological association prediction

Multi-input association prediction is central to many key problems in computational biology, spanning tasks from drug-target, protein-protein, and virus-host interactions to higher-order challenges such as drug synergy modeling and peptide-major histocompatibility complex-T cell receptor binding prediction. Yet, widely used benchmarks often overestimate performance by enabling models to exploit degree ratio shortcut learning, while alternative out-of-distribution splits are overly restrictive and impractical. Here, we introduce an entity-balanced evaluation framework that systematically neutralizes shortcut signals by balancing positive and negative associations at the entity level. This enables fairer assessments that reflect genuine relational learning and extend naturally from pairwise to multi-entity problems. We further present UnbiasNet, a model-agnostic training strategy that cycles through diverse entity-balanced sub-training sets, removing access to degree ratio bias and enhancing robustness. Applied to drug-target, drug synergy, and virus-host prediction, our framework reveals the extent of shortcut reliance in existing methods while enabling consistent identification of meaningful biological associations. Furthermore, we demonstrate that removing access to degree ratio shortcuts directs models toward biologically meaningful features, improving both robustness and interpretability, thereby setting a rigorous foundation for future methodological progress.