Papers › Tri-graph Information Propagation for Polypharmacy Side Effect Prediction
Tri-graph Information Propagation for Polypharmacy Side Effect Prediction
Hao Xu, Shengqi Sang, Haiping Lu
The use of drug combinations often leads to polypharmacy side effects (POSE). A recent method formulates POSE prediction as a link prediction problem on a graph of drugs and proteins, and solves it with Graph Convolutional Networks (GCNs). However, due to the complex relationships in POSE, this method has high computational cost and memory demand. This paper proposes a flexible Tri-graph Information Propagation (TIP) model that operates on three subgraphs to learn representations progressively by propagation from protein-protein graph to drug-drug graph via protein-drug graph. Experiments show that TIP improves accuracy by 7%+, time efficiency by 83×, and space efficiency by 3×.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Link Prediction | Decagon | TIP | AUPRC | 0.890 | #2 of 2 | Archive leaderboard | report |
| Link Prediction | Decagon | TIP | AUROC | 0.914 | #2 of 2 | Archive leaderboard | report |
| Link Prediction | Decagon | TIP | mAP@50 | 0.890 | #2 of 2 | Archive leaderboard | report |
| Pose Prediction | SUN-Mem | TIP-sum PPM-GGM-DDM-DF | AP50 | 89 | #1 of 1 | Archive leaderboard | report |
| Pose Prediction | SUN-Mem | TIP-sum PPM-GGM-DDM-DF | AUPRC | 89 | #1 of 1 | Archive leaderboard | report |
| Pose Prediction | SUN-Mem | TIP-sum PPM-GGM-DDM-DF | AUROC | 0.914 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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