A dual graph neural network for drug–drug interactions prediction based on molecular structure and interactions

Expressive molecular representation plays critical roles in researching drug design, while effective methods are beneficial to learning molecular representations and solving related problems in drug discovery, especially for drug-drug interactions (DDIs) prediction. Recently, a lot of work has been put forward using graph neural networks (GNNs) to forecast DDIs and learn molecular representations. However, under the current GNNs structure, the majority of approaches learn drug molecular representation from one-dimensional string or two-dimensional molecular graph structure, while the interaction information between chemical substructure remains rarely explored, and it is neglected to identify key substructures that contribute significantly to the DDIs prediction. Therefore, we proposed a dual graph neural network named DGNN-DDI to learn drug molecular features by using molecular structure and interactions. Specifically, we first designed a directed message passing neural network with substructure attention mechanism (SA-DMPNN) to adaptively extract substructures. Second, in order to improve the final features, we separated the drug-drug interactions into pairwise interactions between each drug’s unique substructures. Then, the features are adopted to predict interaction probability of a DDI tuple. We evaluated DGNN–DDI on real-world dataset. Compared to state-of-the-art methods, the model improved DDIs prediction performance. We also conducted case study on existing drugs aiming to predict drug combinations that may be effective for the novel coronavirus disease 2019 (COVID-19). Moreover, the visual interpretation results proved that the DGNN-DDI was sensitive to the structure information of drugs and able to detect the key substructures for DDIs. These advantages demonstrated that the proposed method enhanced the performance and interpretation capability of DDI prediction modeling.Author summary: Drug-drug interactions (DDIs) may cause adverse effects that damage the body. Therefore, it is critical to predict potential drug-drug interactions. The majority of the prediction techniques still rely on the similarity hypothesis for drugs, sometimes neglect the molecular structure, and fail to include the interaction information between chemical substructure when predicting DDIs. We exploited this idea to develop and confirm the role that molecular structure and interaction information between chemical substructure play in DDIs prediction. The model includes a molecular substructure extraction framework to explain why substructures contribute differently to DDIs prediction, and a co-attention mechanism to explain why the interaction information between chemical substructure can improve DDIs prediction. Compared to state-of-the-art methods, the model improved the performance of DDIs prediction on real-world dataset. Furthermore, it could identify crucial components of treatment combinations that might be efficient against the emerging coronavirus disease 2019 (COVID-19).