Big Data to Expand the Antimicrobial Therapeutic Arsenal

Abstract

The current big data era has vast amounts of biomedical data that present great interest in processes related to the search of new drugs. Due to the information available in open access databases, many efforts have been directed towards the application of in silico discovery strategies aimed at drug discovery and drug repurposing. These approaches could be useful in finding solutions for antimicrobial resistance, which already causes 700,000 deaths every year. Mathematical prediction models provide a time- and cost-effective solution to this global health threat, as they save all the resources necessary for the development of the molecule. The literature provides computational techniques that have been successfully used in the discovery and repurposing of drugs. For this reason, the aim of this article is to present the different types of prediction models that have been used for the discovery and repurposing of new antimicrobial drugs and to prove the efficiency of these strategies to provide new molecules and therapeutic opportunities to drugs found in biomedical datasets.