  The paper utilizes the graph embeddings generated for entities of a large
biomedical database to perform link prediction to capture various new
relationships among different entities. A novel node similarity measure is
proposed that utilizes the graph embeddings and link prediction scores to find
similarity scores among various drugs which can be used by the medical experts
to recommend alternative drugs to avoid side effects from original one.
Utilizing machine learning on knowledge graph for drug similarity and
recommendation will be less costly and less time consuming with higher
scalability as compared to traditional biomedical methods due to the dependency
on costly medical equipment and experts of the latter ones.
