Knowledge Graph Convolutional Network with Heuristic Search for Drug Repositioning

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.