The role and potential of computer-aided drug discovery strategies in the discovery of novel antimicrobials

Abstract

Antimicrobial resistance (AMR) has become more of a concern in recent decades, particularly in infections associated with global public health threats. The development of new antibiotics is crucial to ensuring infection control and eradicating AMR. Although drug discovery and development are essential processes in the transformation of a drug candidate from the laboratory to the bedside, they are often very complicated, expensive, and time-consuming. The pharmaceutical sector is continuously innovating strategies to reduce research costs and accelerate the development of new drug candidates. Computer-aided drug discovery (CADD) has emerged as a powerful and promising technology that renews the hope of researchers for the faster identification, design, and development of cheaper, less resource-intensive, and more efficient drug candidates. In this review, we discuss an overview of AMR, the potential, and limitations of CADD in AMR drug discovery, and case studies of the successful application of this technique in the rapid identification of various drug candidates. This review will aid in achieving a better understanding of available CADD techniques in the discovery of novel drug candidates against resistant pathogens and other infectious agents.

Keywords: Antimicrobial resistance (AMR); Computer-aided drug discovery (CADD); Ligand-based virtual screening (LBVS); Machine learning; Molecular docking and scoring; Pharmacophore; Quantitative structure-activity relationships (QSAR); Structure-based virtual screening (SBVS); Virtual screening.