Drug Discovery and Drug Repositioning Using Computational Methods

The success rate of drug discovery has been extremely low recently. As an efficient drug discovery strategy, drug repositioning (also called drug repurposing or drug rescue) has been attracting attention. In recent biomedical science, it has become possible to obtain omics information such as the genome, transcriptome, proteome, metabolome, phenome, and interactome, enabling us to comprehensively analyze various molecules and diseases. At the same time, advances in technologies such as combinatorial chemistry and high-content screening have led to the accumulation of chemical and physiological activity information on a vast number of compounds and drugs. Such biomedical big data are useful resources for drug discovery and drug repositioning. This chapter reviews the recent trends in computational methods for drug discovery and drug repositioning using various big data and machine learning (a fundamental technology of artificial intelligence).