School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China.
School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China.
J Chem Inf Model. 2024 Jun 24;64(12):4928-4937. doi: 10.1021/acs.jcim.4c00737. Epub 2024 Jun 5.
Drug repositioning is a strategy of repurposing approved drugs for treating new indications, which can accelerate the drug discovery process, reduce development costs, and lower the safety risk. The advancement of biotechnology has significantly accelerated the speed and scale of biological data generation, offering significant potential for drug repositioning through biomedical knowledge graphs that integrate diverse entities and relations from various biomedical sources. To fully learn the semantic information and topological structure information from the biological knowledge graph, we propose a knowledge graph convolutional network with a heuristic search, named KGCNH, which can effectively utilize the diversity of entities and relationships in biological knowledge graphs, as well as topological structure information, to predict the associations between drugs and diseases. Specifically, we design a relation-aware attention mechanism to compute the attention scores for each neighboring entity of a given entity under different relations. To address the challenge of randomness of the initial attention scores potentially impacting model performance and to expand the search scope of the model, we designed a heuristic search module based on Gumbel-Softmax, which uses attention scores as heuristic information and introduces randomness to assist the model in exploring more optimal embeddings of drugs and diseases. Following this module, we derive the relation weights, obtain the embeddings of drugs and diseases through neighborhood aggregation, and then predict drug-disease associations. Additionally, we employ feature-based augmented views to enhance model robustness and mitigate overfitting issues. We have implemented our method and conducted experiments on two data sets. The results demonstrate that KGCNH outperforms competing methods. In particular, case studies on lithium and quetiapine confirm that KGCNH can retrieve more actual drug-disease associations in the top prediction results.
药物重定位是一种将已批准的药物重新用于治疗新适应症的策略，它可以加速药物发现过程，降低开发成本，并降低安全性风险。生物技术的进步极大地加快了生物数据的生成速度和规模，通过整合来自各种生物医学来源的不同实体和关系的生物医学知识图谱，为药物重定位提供了巨大的潜力。为了充分学习生物知识图谱中的语义信息和拓扑结构信息，我们提出了一种带有启发式搜索的知识图卷积网络，命名为 KGCNH，它可以有效地利用生物知识图谱中实体和关系的多样性以及拓扑结构信息，来预测药物和疾病之间的关联。具体来说，我们设计了一种关系感知注意力机制，为给定实体的每个不同关系的相邻实体计算注意力得分。为了解决初始注意力得分的随机性可能影响模型性能的挑战，并扩大模型的搜索范围，我们设计了一个基于 Gumbel-Softmax 的启发式搜索模块，该模块使用注意力得分作为启发式信息，并引入随机性来帮助模型探索药物和疾病的更优嵌入。在这个模块之后，我们得出关系权重，通过邻居聚合获得药物和疾病的嵌入，然后预测药物-疾病关联。此外，我们采用基于特征的增强视图来增强模型的稳健性并减轻过拟合问题。我们已经实现了我们的方法，并在两个数据集上进行了实验。结果表明，KGCNH 优于竞争方法。特别是关于锂和喹硫平的案例研究证实，KGCNH 可以在最高预测结果中检索到更多实际的药物-疾病关联。
