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MSIGR-PLA: Integrating Multi-Scale Interaction and Global Representations for Protein-Ligand Affinity Prediction
IEEE Journal of Biomedical and Health Informatics
|August 10, 2026
Summary
This study introduces MSIGR-PLA, a new framework for predicting protein-ligand affinity (PLA). It improves accuracy by integrating local and global features, outperforming existing methods in drug discovery.
Area of Science:
- Computational chemistry
- Structural biology
- Drug discovery
Background:
- Accurate protein-ligand affinity (PLA) prediction is vital for drug discovery.
- Current methods struggle with local interaction features and global representations, limiting predictive accuracy.
Purpose of the Study:
- To develop an integrative framework, MSIGR-PLA, for enhanced PLA prediction.
- To improve accuracy by combining multi-scale local interaction features with global protein-ligand representations.
Main Methods:
- MSIGR-PLA utilizes two feature encoders: a local encoder with a multi-scale dynamic interaction (MSDI) module (GCN, cross-attention, Graph Transformer) and a global encoder (ESM-2 for protein, CNN-Transformer for ligand sequence, GIN for ligand structure).
Main Results:
- MSIGR-PLA outperformed existing methods on four benchmark datasets, showing 3.7%-9.0% improvement in Pearson correlation coefficient (R).
- Ablation studies confirmed the effectiveness of key modules, and a case study highlighted the MSDI module's ability to identify key binding residues.
Conclusions:
- MSIGR-PLA offers a significant advancement in PLA prediction accuracy.
- The framework's ability to integrate multi-scale local and global features provides a more robust approach for drug discovery applications.