EvoGraph: synergizing evolutionary semantics and tail-aware graph contrastive learning for protein-protein interaction prediction

Accurate prediction of multilabel protein-protein interactions is pivotal for deciphering molecular mechanisms and accelerating drug discovery. However, existing methods are often hindered by the inherent long-tailed distribution of interaction types, leading to poor generalization on rare but biologically critical categories. To address this challenge, we propose EvoGraph, a novel framework that synergizes deep evolutionary semantics with tail-aware graph representation learning. Specifically, EvoGraph first leverages the pretrained protein language model to establish a semantic foundation, compensating for sparse signals in rare classes. Driven by the critical need to disentangle complex interaction types and mitigate head-class dominance, we integrate two core mechanisms. To resolve the structural heterogeneity of diverse interaction types, we design a multi-view graph neural network encoder that aggregates type-specific topological contexts. Simultaneously, to counter label imbalance, we introduce a tail-aware contrastive learning module, which regularizes the latent space by clustering minority samples, effectively preventing them from being overshadowed by head classes. Finally, these features are synthesized via a hybrid fusion strategy and optimized with an asymmetric loss. Extensive experiments on four benchmark datasets demonstrate that EvoGraph significantly outperforms state-of-the-art baselines, achieving substantial improvements in identifying tail interaction types without compromising overall performance. The implementation of EvoGraph is available at https://github.com/AlphaZYL/EvoGraph.