Asymmetric Drug–Drug Interaction Prediction Based on Generative Adversarial Networks and Knowledge Graph

Abstract

Drug–drug interaction (DDI) prediction remains a significant challenge due to the complexity of biological systems and the growing demand for precise predictions. Recent advances in deep learning have been successfully applied to DDI prediction. However, the asymmetrical nature of DDIs is always neglected, which can lead to some information loss during the feature learning process. To address the issue, a novel DDI prediction method based on knowledge graph and generative adversarial network, KGGAN-DDI, is proposed to predict potential DDIs. First, a knowledge graph embedding module is designed to capture and encode asymmetric associations between drug pairs, which can enhance the feature representation and contextual relevance of drug interactions. Then, a dual-generator GAN is adopted to produce realistic samples and improve the prediction accuracy further. Moreover, a least squares loss function is utilized to mitigate the vanishing gradient problem, thereby providing smoother gradients and enhancing the efficiency and stability of the optimization process. Extensive experiments demonstrate that the proposed KGGAN-DDI performs better than state-of-the-art methods. Case study further shows the effectiveness of KGGAN-DDI.