SCMPPI: Supervised Contrastive Multimodal Framework for Predicting Protein-Protein Interactions

Protein-Protein Interaction (PPI) prediction is a key task in uncovering cellular functional networks and disease mechanisms. However, traditional experimental methods are time-consuming and costly, and existing computational models face challenges in cross-modal feature fusion, robustness, and false-negative suppression. In this paper, we propose a novel supervised contrastive multimodal framework, SCMPPI, for PPI prediction. By integrating protein sequence features (AAC, DPC, CKSAAP-ESMC) with PPI network topology information (Node2Vec graph embedding), and combining an improved supervised contrastive learning strategy, SCMPPI significantly enhances PPI prediction performance. For the PPI task, SCMPPI introduces a negative sample filtering mechanism and modifies the contrastive loss function, effectively optimizing multimodal features. Experiments on eight benchmark datasets, including yeast, human, and this http URL, show that SCMPPI outperforms existing state-of-the-art methods (such as DF-PPI and TAGPPI) in key metrics such as accuracy ( 98.01%) and AUC (99.62%), and demonstrates strong generalization in cross-species prediction (AUC > 99% on multi-species datasets). Furthermore, SCMPPI has been successfully applied to CD9 networks, the Wnt pathway, and cancer-specific networks, providing a reliable tool for disease target discovery. This framework also offers a new paradigm for multimodal biological information fusion and contrastive learning in collaborative optimization for various combined predictions.