From Genome to Drugs: New Approaches in Antimicrobial Discovery

Decades of successful use of antibiotics is currently challenged by the emergence of increasingly resistant bacterial strains. Novel drugs are urgently required but, in a scenario where private investment in the development of new antimicrobials is declining, efforts to combat drug-resistant infections become a worldwide public health problem. Reasons behind unsuccessful new antimicrobial development projects range from inadequate selection of the molecular targets to a lack of innovation. In this context, increasingly available omics data for multiple pathogens has created new drug discovery and development opportunities to fight infectious diseases. Identification of an appropriate molecular target is currently accepted as a critical step of the drug discovery process. Here, we review how diverse layers of multi-omics data in conjunction with structural/functional analysis and systems biology can be used to prioritize the best candidate proteins. Once the target is selected, virtual screening can be used as a robust methodology to explore molecular scaffolds that could act as inhibitors, guiding the development of new drug lead compounds. This review focuses on how the advent of omics and the development and application of bioinformatics strategies conduct a big-data era that improves target selection and lead compound identification in a cost-effective and shortened timeline.

Automated summary

This review discusses the challenges posed by antibiotic-resistant bacterial strains and the declining private investment in developing new antimicrobials. It highlights the importance of utilizing multi-omics data, structural/functional analysis, and systems biology to prioritize candidate proteins for drug discovery, as well as the use of virtual screening to explore potential inhibitors and guide the development of new drug lead compounds. The advent of omics and the application of bioinformatics strategies in the big-data era have improved target selection and lead compound identification in a cost-effective and shortened timeline.