Prediction of Drug-Drug Interaction Using an Attention-Based Graph Neural Network on Drug Molecular Graphs
Abstract
:1. Introduction
2. Materials and Methods
2.1. Datasets
2.2. Problem Formulation
2.3. GNN-DDI Model
Algorithms 1 The pseudo-code of GNN-DDI |
input: Molecular graph of drug x and its original features of atomic nodes Molecular graph of drug y and its original features of atomic nodes output: Probability score of drug pair 1: Initialize parameter sets in GNN-DDI. 2: for k in K: 3: Compute and based on Equations (1) to (3). 4: SAGPooling based on Equation (4) to obtain and in layer k. 5: end for 6: Concatenate k-hops and based on Equation (5) to obtain and . 7: Concatenate and to obtain the latent feature vector of a drug pair 8: Feed feature vector into the predictor to get probability score . |
2.3.1. Feature Extractor
2.3.2. Feature Aggregation for Drug Pairs
2.3.3. MLP Predictor
2.4. Cross-Validation Strategy and Assessment Metrics
3. Results and Discussion
3.1. Parameter Setting
3.2. Results of GNN-DDI and Five Other Methods in the First Prediction Scenario
3.3. Results of GNN-DDI and Four Other Methods in the Second Prediction Scenario
3.4. Effects of Using Different Feature Extraction Approaches
3.5. Interpretability Case Studies
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
DDIs | Drug-Drug Interactions |
GAT | Graph Attention Network |
MLP | Multi-Layer Perception |
MACCSkeys | Molecular ACCess System keys |
ATC | Anatomical Therapeutic Chemical classification |
DBP | Drug-Binding Protein |
AUC | Area Under the receiver operating characteristic Curve |
AUPR | Area Under the Precision-Recall curve |
ACC | ACCuracy |
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Methods | AUC | AUPR | Recall | Precision | ACC | F1 |
---|---|---|---|---|---|---|
Vilar 1 [53] | 0.707 | 0.262 | 0.495 | 0.253 | 0.719 | 0.334 |
Vilar2 [54] | 0.826 | 0.533 | 0.569 | 0.515 | 0.862 | 0.540 |
LP [23] | 0.851 | 0.799 | 0.685 | 0.729 | 0.809 | 0.706 |
Zhang [15] | 0.954 | 0.841 | 0.788 | 0.717 | 0.934 | 0.751 |
DPDDI | 0.956 | 0.907 | 0.810 | 0.754 | 0.940 | 0.840 |
GNN-DDI | 0.936 | 0.930 | 0.920 | 0.823 | 0.863 | 0.869 |
AUC | AUPR | Recall | Precision | ACC | F1 | |
---|---|---|---|---|---|---|
Pubchem features | 0.920 | 0.928 | 0.880 | 0.862 | 0.905 | 0.883 |
MACCSkeys features | 0.930 | 0.924 | 0.879 | 0.864 | 0.901 | 0.882 |
DBP features | 0.862 | 0.875 | 0.803 | 0.757 | 0.89 | 0.819 |
ATC features | 0.888 | 0.895 | 0.834 | 0.811 | 0.871 | 0.840 |
GNN-DDI features | 0.936 | 0.930 | 0.920 | 0.823 | 0.861 | 0.869 |
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Feng, Y.-H.; Zhang, S.-W. Prediction of Drug-Drug Interaction Using an Attention-Based Graph Neural Network on Drug Molecular Graphs. Molecules 2022, 27, 3004. https://doi.org/10.3390/molecules27093004
Feng Y-H, Zhang S-W. Prediction of Drug-Drug Interaction Using an Attention-Based Graph Neural Network on Drug Molecular Graphs. Molecules. 2022; 27(9):3004. https://doi.org/10.3390/molecules27093004
Chicago/Turabian StyleFeng, Yue-Hua, and Shao-Wu Zhang. 2022. "Prediction of Drug-Drug Interaction Using an Attention-Based Graph Neural Network on Drug Molecular Graphs" Molecules 27, no. 9: 3004. https://doi.org/10.3390/molecules27093004