Tri-graph Information Propagation for Polypharmacy Side Effect Prediction

28 Jan 2020  ·  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$\times$, and space efficiency by 3$\times$.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Link Prediction Decagon TIP AUROC 0.914 # 2
AUPRC 0.890 # 2
mAP@50 0.890 # 1
Pose Prediction SUN-Mem TIP-sum PPM-GGM-DDM-DF AUPRC 89 # 1
AUROC 0.914 # 1
AP50 89 # 1