Motivation: Drug repurposing is a viable solution for reducing the time and
cost associated with drug development. However, thus far, the proposed drug
repurposing approaches still need to meet expectations. Therefore, it is
crucial to offer a systematic approach for drug repurposing to achieve cost
savings and enhance human lives. In recent years, using biological
network-based methods for drug repurposing has generated promising results.
Nevertheless, these methods have limitations. Primarily, the scope of these
methods is generally limited concerning the size and variety of data they can
effectively handle. Another issue arises from the treatment of heterogeneous
data, which needs to be addressed or converted into homogeneous data, leading
to a loss of information. A significant drawback is that most of these
approaches lack end-to-end functionality, necessitating manual implementation
and expert knowledge in certain stages. Results: We propose a new solution,
HGTDR (Heterogeneous Graph Transformer for Drug Repurposing), to address the
challenges associated with drug repurposing. HGTDR is a three-step approach for
knowledge graph-based drug re-purposing: 1) constructing a heterogeneous
knowledge graph, 2) utilizing a heterogeneous graph transformer network, and 3)
computing relationship scores using a fully connected network. By leveraging
HGTDR, users gain the ability to manipulate input graphs, extract information
from diverse entities, and obtain their desired output. In the evaluation step,
we demonstrate that HGTDR performs comparably to previous methods. Furthermore,
we review medical studies to validate our method's top ten drug repurposing
suggestions, which have exhibited promising results. We also demon-strated
HGTDR's capability to predict other types of relations through numerical and
experimental validation, such as drug-protein and disease-protein
inter-relations.

使用 HGTDR（异构图转换器用于药物再利用）提出了一种三步方法，通过构建异构知识图、利用异构图转换器网络以及使用全连接网络计算关系得分，来解决药物再利用面临的挑战，并验证了 HGTDR 能够预测药物 - 蛋白质和疾病 - 蛋白质等其他类型的关系。