Abstract
Polypharmacy is the combined use of multiple drugs, widely adopted in medicine to treat patients that suffer of complex diseases. Therefore, it is important to have reliable tools able to predict if the activity of a drug could unfavorably change when combined with others. State-of-the-art methods face this problem as a link prediction task on a multilayer graph describing drug-drug interactions (DDI) and protein-protein interactions (PPI), since it has been demonstrated to be the most effective representation. Graph Convolutional Networks (GCN) are the method most commonly chosen in recent research for this problem. We propose to improve the performance of GCN on this link prediction task through the addition of a novel relation-wise Graph Attention Network (GAT), used to assign different weight to the different relationships in the multilayer graph. We experimentally demonstrate that the proposed GCN, compared with other recent methods, is able to achieve a state-of-the-art performance on a publicly available polypharmacy side effect network.
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Carletti, V., Foggia, P., Greco, A., Roberto, A., Vento, M. (2021). Predicting Polypharmacy Side Effects Through a Relation-Wise Graph Attention Network. In: Torsello, A., Rossi, L., Pelillo, M., Biggio, B., Robles-Kelly, A. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2021. Lecture Notes in Computer Science(), vol 12644. Springer, Cham. https://doi.org/10.1007/978-3-030-73973-7_12
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