DGANDDI: Double Generative Adversarial Networks for Drug-Drug Interaction Prediction

Co-administration of multiple drugs may cause adverse drug interactions and side effects that damage the body. Therefore, accurate prediction of drug-drug interaction (DDI) events is of great importance. Recently, many computational methods have been proposed for predicting DDI associated events. However, most existing methods merely considered drug associated attribute information or topological information in DDI network, ignoring the complementary knowledge between them. Therefore, to effectively explore the complementarity of drug attribute and topological information of DDI network, we propose a deep learning model based adversarial learning strategy, which is named as DGANDDI. In DGANDDI, we design a two-GAN architecture to deeply capture the complementary knowledge between drug attribute and topological information of DDI network, thus more comprehensive drug representations can be learned. We conduct extensive experiments on real world dataset. The experimental results show that DGANDDI can effectively predict DDI occurrence and outperforms the comparison of the state-of-the-art models. We also perform ablation studies that demonstrate that DGANDDI is effective and that it is robust in DDI prediction tasks, even in the case of a scarcity of labeled DDIs.

Automated summary

To accurately predict drug-drug interaction (DDI) events, we propose a deep learning model named DGANDDI, which utilizes an adversarial learning strategy. DGANDDI incorporates drug attribute and topological information of DDI network using a two-GAN architecture, enabling more comprehensive drug representations. Experimental results demonstrate that DGANDDI effectively predicts DDI occurrence and outperforms state-of-the-art models.