Chapter 13 - Analysis of Chemical Spaces: Implications for Drug Repurposing

Abstract

With the slow decrease in the number of approved novel drugs in recent years, drug repurposing has become a viable strategy for drug development. However, many drug repurposing approaches do not fully utilize the rapidly growing volume of relevant drug-related information including chemical structures, biological activities, etc. The analysis of large sets of chemical structures is usually performed in the framework of the chemical space concept. Here we present a review of approaches to chemical space analysis and discuss their potential application in the field of drug repurposing. First, a brief description of existing databases useful for drug repurposing is given. The discussion of the chemical space analysis methods classified by the type of chemical space representation starts with the network-based approaches as they are used most widely for drug repurposing. Then the “classical” approaches to chemical space analysis, such as dimensionality reduction methods (principal component analysis, Kohonen maps, generative topographic mapping), along with the recently developed methods using neural network “latent” chemical space representation, are presented. Finally, the scaffold-based approaches are considered. The growth of data about complex chemical and biological systems, as well as the development of new algorithms for chemical space analysis will undoubtedly enhance the drug repurposing projects.