  The complete characterization of enzymatic activities between molecules
remains incomplete, hindering biological engineering and limiting biological
discovery. We develop in this work a technique, Enzymatic Link Prediction
(ELP), for predicting the likelihood of an enzymatic transformation between two
molecules. ELP models enzymatic reactions catalogued in the KEGG database as a
graph. ELP is innovative over prior works in using graph embedding to learn
molecular representations that capture not only molecular and enzymatic
attributes but also graph connectivity.
  We explore both transductive (test nodes included in the training graph) and
inductive (test nodes not part of the training graph) learning models. We show
that ELP achieves high AUC when learning node embeddings using both graph
connectivity and node attributes. Further, we show that graph embedding for
predicting enzymatic links improves link prediction by 24% over
fingerprint-similarity-based approaches. To emphasize the importance of graph
embedding in the context of biochemical networks, we illustrate how graph
embedding can also guide visualization.
  The code and datasets are available through
https://github.com/HassounLab/ELP.
