Natural Language Processing for Drug Discovery Knowledge Graphs:
  promises and pitfalls
J. Charles G. Jeynes Tim James Matthew Corney
Building and analysing knowledge graphs (KGs) to aid drug discovery is a
topical area of research. A salient feature of KGs is their ability to combine
many heterogeneous data sources in a format that facilitates discovering
connections. The utility of KGs has been exemplified in areas such as drug
repurposing, with insights made through manual exploration and modelling of the
data. In this article, we discuss promises and pitfalls of using natural
language processing (NLP) to mine unstructured text typically from scientific
literature as a data source for KGs. This draws on our experience of initially
parsing structured data sources such as ChEMBL as the basis for data within a
KG, and then enriching or expanding upon them using NLP. The fundamental
promise of NLP for KGs is the automated extraction of data from millions of
documents a task practically impossible to do via human curation alone.
However, there are many potential pitfalls in NLP-KG pipelines such as
incorrect named entity recognition and ontology linking all of which could
ultimately lead to erroneous inferences and conclusions.