Exploring chemical space using natural language processing methodologies for drug discovery

Abstract

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. The application of natural language processing methodologies to analyze text-based representations of molecular structures opens new doors in deciphering the information-rich domain of biochemistry toward the discovery and design of novel drugs.