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arXiv · 2504.02698

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

Shengrui XU·Tianchi Lu·Zikun Wang·Jixiu Zhai
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Abstract

Protein-protein interaction (PPI) prediction plays a pivotal role in deciphering cellular functions and disease mechanisms. To address the limitations of traditional experimental methods and existing computational approaches in cross-modal feature fusion and false-negative suppression, we propose SCMPPI-a novel supervised contrastive multimodal framework. By effectively integrating sequence-based features (AAC, DPC, ESMC-CKSAAP) with network topology (Node2Vec embeddings) and incorporating an enhanced contrastive learning strategy with negative sample filtering, SCMPPI achieves superior prediction performance. Extensive experiments on eight benchmark datasets demonstrate its state-of-the-art accuracy(98.13%) and AUC(99.69%), along with excellent cross-species generalization (AUC>99%). Successful applications in CD9 networks, Wnt pathway analysis, and cancer-specific networks further highlight its potential for disease target discovery, establishing SCMPPI as a powerful tool for multimodal biological data analysis.

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Shengrui XU, Tianchi Lu, Zikun Wang, Jixiu Zhai. 2025-04-03. SCMPPI: Supervised Contrastive Multimodal Framework for Predicting Protein-Protein Interactions. https://arxiv.org/abs/2504.02698

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