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HyGNN: Drug-Drug Interaction Prediction via Hypergraph Neural Network. (arXiv:2206.12747v2 [q-bio.QM] UPDATED)
July 15, 2022, 1:11 a.m. | Khaled Mohammed Saifuddin, Briana Bumgardner, Farhan Tanvir, Esra Akbas
cs.LG updates on arXiv.org arxiv.org
Drug-Drug Interactions (DDIs) may hamper the functionalities of drugs, and in
the worst scenario, they may lead to adverse drug reactions (ADRs). Predicting
all DDIs is a challenging and critical problem. Most existing computational
models integrate drug-centric information from different sources and leverage
them as features in machine learning classifiers to predict DDIs. However,
these models have a high chance of failure, especially for the new drugs when
all the information is not available. This paper proposes a novel Hypergraph …
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