  Drug repositioning holds great promise because it can reduce the time and
cost of new drug development. While drug repositioning can omit various R&D
processes, confirming pharmacological effects on biomolecules is essential for
application to new diseases. Biomedical explainability in a drug repositioning
model can support appropriate insights in subsequent in-depth studies. However,
the validity of the XAI methodology is still under debate, and the
effectiveness of XAI in drug repositioning prediction applications remains
unclear. In this study, we propose GraphIX, an explainable drug repositioning
framework using biological networks, and quantitatively evaluate its
explainability. GraphIX first learns the network weights and node features
using a graph neural network from known drug indication and knowledge graph
that consists of three types of nodes (but not given node type information):
disease, drug, and protein. Analysis of the post-learning features showed that
node types that were not known to the model beforehand are distinguished
through the learning process based on the graph structure. From the learned
weights and features, GraphIX then predicts the disease-drug association and
calculates the contribution values of the nodes located in the neighborhood of
the predicted disease and drug. We hypothesized that the neighboring protein
node to which the model gave a high contribution is important in understanding
the actual pharmacological effects. Quantitative evaluation of the validity of
protein nodes' contribution using a real-world database showed that the high
contribution proteins shown by GraphIX are reasonable as a mechanism of drug
action. GraphIX is a framework for evidence-based drug discovery that can
present to users new disease-drug associations and identify the protein
important for understanding its pharmacological effects from a large and
complex knowledge base.
