  Text-based representations of chemicals and proteins can be thought of as
unstructured languages codified by humans to describe domain-specific
knowledge. Advances in natural language processing (NLP) methodologies in the
processing of spoken languages accelerated the application of NLP to elucidate
hidden knowledge in textual representations of these biochemical entities and
then use it to construct models to predict molecular properties or to design
novel molecules. This review outlines the impact made by these advances on drug
discovery and aims to further the dialogue between medicinal chemists and
computer scientists.
