Graph reasoning method enhanced by relational transformers and knowledge distillation for drug-related side effect prediction.

ABSTRACT: Identifying the side effects related to drugs is beneficial for reducing the risk of drug development failure and saving the drug development cost. We proposed a graph reasoning method, RKDSP, to fuse the semantics of multiple connection relationships, the local knowledge within each meta-path, the global knowledge among multiple meta-paths, and the attributes of the drug and side effect node pairs. We constructed drug-side effect heterogeneous graphs consisting of the drugs, side effects, and their similarity and association connections. Multiple relational transformers were established to learn node features from diverse meta-path semantic perspectives. A knowledge distillation module was constructed to learn local and global knowledge of multiple meta-paths. Finally, an adaptive convolu

SUBMITTER: Bai H 

PROVIDER: S-EPMC11126883 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature