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

Shengrui XU, Tianchi Lu, Zikun Wang, Jixiu Zhai
4/3/2025
cs.LGcs.AIq-bio.QM

No official code found. Best implementation: xshengrui/SCMPPI (7 stars, last commit 2025-06-16).

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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Code Implementations(1)

xshengrui/SCMPPICommunity76%

A Supervised Contrastive Multimodal Framework for Predicting Protein-Protein Interactions

74PythonApr 3, 20251 years ago

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Cite this paper

@article{xu2025scmppi,
  title  = {SCMPPI: Supervised Contrastive Multimodal Framework for Predicting Protein-Protein Interactions},
  author = {Shengrui XU and Tianchi Lu and Zikun Wang and Jixiu Zhai},
  year   = {2025},
  eprint = {2504.02698},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.02698v3}
}

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