  Drug discovery and development is a complex and costly process. Machine
learning approaches are being investigated to help improve the effectiveness
and speed of multiple stages of the drug discovery pipeline. Of these, those
that use Knowledge Graphs (KG) have promise in many tasks, including drug
repurposing, drug toxicity prediction and target gene-disease prioritisation.
In a drug discovery KG, crucial elements including genes, diseases and drugs
are represented as entities, whilst relationships between them indicate an
interaction. However, to construct high-quality KGs, suitable data is required.
In this review, we detail publicly available sources suitable for use in
constructing drug discovery focused KGs. We aim to help guide machine learning
and KG practitioners who are interested in applying new techniques to the drug
discovery field, but who may be unfamiliar with the relevant data sources. The
datasets are selected via strict criteria, categorised according to the primary
type of information contained within and are considered based upon what
information could be extracted to build a KG. We then present a comparative
analysis of existing public drug discovery KGs and a evaluation of selected
motivating case studies from the literature. Additionally, we raise numerous
and unique challenges and issues associated with the domain and its datasets,
whilst also highlighting key future research directions. We hope this review
will motivate KGs use in solving key and emerging questions in the drug
discovery domain.
