Review of 'Explainable drug repurposing via path-based knowledge graph completion'

The article titled "Explainable drug repurposing via path-based knowledge graph completion" by Ana Jimenez et al. is focused on using artificial intelligence and knowledge graphs for drug repurposing. The study aims to find new therapeutic applications for existing drugs using AI and knowledge graphs. The methodology makes use of paths within the knowledge graph for drug repurposing. This approach not only predicts which diseases can be treated by a drug but also provides a biological rationale for the predictions. The method delves into how paths, which are sequences of nodes and relationships in the graph, and metapaths, a specific sequence of node and relation types, are used in this process. The paper outlines different strategies for generating these paths, highlighting their importance in making the drug repurposing predictions interpretable and meaningful for further research.

Here is a review based on the provided criteria:

• The model's focus on paths from compounds to diseases might limit its ability to uncover new, less obvious drug-disease associations, potentially leading to a focus on already known cases. The inclusion of different node types such as genes, pathways, side effects, or anatomies might mitigate this to some extent, but the primary focus remains on compounds and diseases.
• The model is trained to give high scores to diseases known to be treated with the compound. This training approach could potentially lead to overfitting, where the model might excel in identifying known cases but struggle with novel or less obvious associations. A discussion about this issue is suggested for inclusion in the main text.
• The selection of examples using Epirubicin, Paclitaxel, and Prednisone could be justified in the main text.
• The choice of knowledge graph used for training XG4Repo and the effect of changing this knowledge graph are not discussed, which is significant for its adaptability to different datasets and scenarios.

The work is original, providing new insights into explainable AI for drug repurposing. Its significance lies in offering a framework (XG4Repo) that combines prediction accuracy and interpretability, potentially impacting how researchers approach drug repurposing.