Drug Repositioning Based on Expert Knowledge Augmented Graph Neural Network.

Abstract

Drug repositioning is critical in accelerating drug discovery, which identifies new indications for existing drugs by modeling drug-disease associations. Compared to traditional methods, graph neural networks (GNNs) have recently gained widespread attention due to their ability to effectively aggregate information from neighboring nodes in drug-disease heterogeneous graphs. The GNN-based methods need effective node embeddings for information aggregation. However, they generate the node embeddings by random initialization, rather than incorporating the high-quality expert knowledge involving biological mechanisms in the databases. This limits their capacity to generate interpretable node embeddings aligned with expert knowledge. To bridge this gap, we develop a novel framework dubbed DReKGNN (Drug Repositioning based on expert Knowledge augmented Graph Neural Network). To be specific, DReKGNN will first adopt large language models (LLMs) as a semantic bridge between expert knowledge and GNNs. To ensure the accuracy of expert knowledge, DReKGNN does not rely on prompt templates in LLMs to generate knowledge descriptions for drugs and diseases. Instead, it extracts expert knowledge directly from the DrugBank and OMIM databases. The effective node embeddings with interpretable semantic information will be generated from expert knowledge descriptions involving biological mechanisms by LLMs. Then, we demonstrate that there is a need to mitigate noise when LLM node embeddings serving drug repositioning prediction tasks. Considering this design need, we integrate GNNs with LLM node embeddings by a mean aggregation strategy. The experiment results of performance comparison and case study show the effectiveness of DReKGNN in predicting drug-disease associations. The code is available at https://github.com/csubigdata-Organization/DReKGNN.