A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge
  Graph Perspective
  Drug discovery and development is an extremely complex process, with high
attrition contributing to the costs of delivering new medicines to patients.
Recently, various machine learning approaches have been proposed and
investigated to help improve the effectiveness and speed of multiple stages of
the drug discovery pipeline. Among these techniques, it is especially those
using Knowledge Graphs that are proving to have considerable promise across a
range of tasks, including drug repurposing, drug toxicity prediction and target
gene-disease prioritisation. In such a knowledge graph-based representation of
drug discovery domains, crucial elements including genes, diseases and drugs
are represented as entities or vertices, whilst relationships or edges between
them indicate some level of interaction. For example, an edge between a disease
and drug entity might represent a successful clinical trial, or an edge between
two drug entities could indicate a potentially harmful interaction.
  In order to construct high-quality and ultimately informative knowledge
graphs however, suitable data and information is of course required. In this
review, we detail publicly available primary data sources containing
information suitable for use in constructing various drug discovery focused
knowledge graphs. We aim to help guide machine learning and knowledge graph
practitioners who are interested in applying new techniques to the drug
discovery field, but who may be unfamiliar with the relevant data sources.
Overall we hope this review will help motivate more machine learning
researchers to explore combining knowledge graphs and machine learning to help
solve key and emerging questions in the drug discovery domain.
