Luo Huimin, Zhu Chunli, Wang Jianlin, Zhang Ge, Luo Junwei, Yan Chaokun
School of Computer and Information Engineering, Henan University, Kaifeng, China.
Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China.
Front Pharmacol. 2024 Feb 7;15:1337764. doi: 10.3389/fphar.2024.1337764. eCollection 2024.
Accurately identifying novel indications for drugs is crucial in drug research and discovery. Traditional drug discovery is costly and time-consuming. Computational drug repositioning can provide an effective strategy for discovering potential drug-disease associations. However, the known experimentally verified drug-disease associations is relatively sparse, which may affect the prediction performance of the computational drug repositioning methods. Moreover, while the existing drug-disease prediction method based on metric learning algorithm has achieved better performance, it simply learns features of drugs and diseases only from the drug-centered perspective, and cannot comprehensively model the latent features of drugs and diseases. In this study, we propose a novel drug repositioning method named RSML-GCN, which applies graph convolutional network and reinforcement symmetric metric learning to predict potential drug-disease associations. RSML-GCN first constructs a drug-disease heterogeneous network by integrating the association and feature information of drugs and diseases. Then, the graph convolutional network (GCN) is applied to complement the drug-disease association information. Finally, reinforcement symmetric metric learning with adaptive margin is designed to learn the latent vector representation of drugs and diseases. Based on the learned latent vector representation, the novel drug-disease associations can be identified by the metric function. Comprehensive experiments on benchmark datasets demonstrated the superior prediction performance of RSML-GCN for drug repositioning.
准确识别药物的新适应症在药物研发中至关重要。传统的药物研发成本高昂且耗时。计算药物重定位可为发现潜在的药物 - 疾病关联提供有效策略。然而，已知的经实验验证的药物 - 疾病关联相对较少，这可能会影响计算药物重定位方法的预测性能。此外，虽然现有的基于度量学习算法的药物 - 疾病预测方法取得了较好的性能，但它仅从以药物为中心的角度学习药物和疾病的特征，无法全面建模药物和疾病的潜在特征。在本研究中，我们提出了一种名为RSML - GCN的新型药物重定位方法，该方法应用图卷积网络和强化对称度量学习来预测潜在的药物 - 疾病关联。RSML - GCN首先通过整合药物和疾病的关联信息与特征信息构建药物 - 疾病异质网络。然后，应用图卷积网络（GCN）来补充药物 - 疾病关联信息。最后，设计具有自适应边距的强化对称度量学习来学习药物和疾病的潜在向量表示。基于学习到的潜在向量表示，可以通过度量函数识别新的药物 - 疾病关联。在基准数据集上的综合实验证明了RSML - GCN在药物重定位方面具有卓越的预测性能。
