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. 2025 Apr 8;20(4):e0320465.
doi: 10.1371/journal.pone.0320465. eCollection 2025.

Edge-enhanced interaction graph network for protein-ligand binding affinity prediction

Dinghai Yang  1 , Linai Kuang  1 , An Hu  1
Affiliations

Edge-enhanced interaction graph network for protein-ligand binding affinity prediction

Dinghai Yang et al. PLoS One. .

Abstract

Protein-ligand interactions are crucial in drug discovery. Accurately predicting protein-ligand binding affinity is essential for screening potential drugs. Graph neural networks have proven highly effective in modeling spatial relationships and three-dimensional structures within intermolecular. In this paper, we introduce a graph neural network-based model named EIGN to predict protein-ligand binding affinity. The model consists of three main components: the normalized adaptive encoder, the molecular information propagation module, and the output module. Experimental results indicate that EIGN achieves root mean squared error of 1.126 and Pearson correlation coefficient of 0.861 on CASF-2016. Additionally, our model outperforms state-of-the-art methods on CASF-2013, CASF-2016, and the CSAR-NRC set, showing exceptional accuracy and robust generalization ability. To further validate the effectiveness of EIGN, we conducted several experiments, including ablation studies, feature importance analysis, data similarity analysis, and others, to evaluate its performance and applicability.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. The three types of graphs used in EIGN.
Fig 2
Fig 2. Model architecture.
(A) Overall framework of EIGN. (B) Inter-molecular message passing structure.
Fig 3
Fig 3. Scatter plots of predicted values (y-axis) versus actual values (x-axis) for EIGN on the validation set, CASF-2013, and CASF-2016.
Fig 4
Fig 4. Prediction performance of EIGN and four variants on CASF-2013 and CASF-2016.
Fig 5
Fig 5. Comparison of prediction performance of EIGN, GIGN, and IGN on the CSAR-HIQ-set.
Fig 6
Fig 6. Contribution analysis of the node feature components in the model.
(A) Ablation study. (B) GNNExplainer.
Fig 7
Fig 7. The impact of the proportion of similar samples in the training set on model prediction performance.
Fig 8
Fig 8. Visualization of the performance of EIGN, GIGN, and IGN on the PYGM target.
(A) Experimentally resolved protein structure. (B) Alphafold3-predicted protein structure.
Fig 9
Fig 9. Scatter plots of the prediction results of EIGN, GIGN, and IGN on the CDK2 target.
See this image and copyright information in PMC

References

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