  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
Neural Network (HyGNN) model based on only the SMILES string of drugs,
available for any drug, for the DDI prediction problem. To capture the drug
similarities, we create a hypergraph from drugs' chemical substructures
extracted from the SMILES strings. Then, we develop HyGNN consisting of a novel
attention-based hypergraph edge encoder to get the representation of drugs as
hyperedges and a decoder to predict the interactions between drug pairs.
Furthermore, we conduct extensive experiments to evaluate our model and compare
it with several state-of-the-art methods. Experimental results demonstrate that
our proposed HyGNN model effectively predicts DDIs and impressively outperforms
the baselines with a maximum ROC-AUC and PR-AUC of 97.9% and 98.1%,
respectively.
