A Lightweight Curriculum and Contrastive Learning Framework for Protein-Protein Interaction Prediction

Protein-protein interaction (PPI) prediction is essential for understanding cellular functions and enabling applications in drug development and disease research. PPI networks exhibit multi-scale structural and semantic heterogeneity and introduce representational bias under topological imbalances, leading to insufficient exploitation of subgraph-level semantic complexity. Moreover, many existing approaches rely heavily on external data during modeling, resulting in high computational costs for large-scale inferences. In this paper, we propose JCCLPPI, a joint curriculum- and contrastive-learning framework for lightweight PPI prediction. First, we implement a PPI network-structure encoding module designed to mitigate topological bias and learn biologically interpretable representations without relying on handcrafted features or external annotations. Next, we propose a motif-based curriculum learning module that incrementally introduces training samples according to their structural-semantic complexity, thereby enhancing model robustness to long-tail distributions and structural heterogeneity. Finally, our approach incorporates two graph neural network-based modules during PPI inference to perform local structural modeling and global context encoding, facilitating multi-scale feature extraction. Experiments conducted on two widely used human PPI benchmark datasets, SHS27k and SHS148k, demonstrate that JCCLPPI improves model generalization. It achieves an approximate 4% increase in micro-F1 score compared to state-of-the-art methods, while also improving computational efficiency by reducing memory consumption by 76% and inference time by 35%. Furthermore, JCCLPPI provides a scalable basis for therapeutic target prioritization and early-stage drug discovery.