New Antibiotics from Computational Models. Potential <i>in silico</i> Strategies Against Antimicrobial Resistance

Antimicrobial resistance is one of the most significant public health threats today. The need for new antimicrobials against multidrug-resistant infections is growing. The development of computational models capable of predicting new drug-target interactions is an interesting strategy to reposition already known drugs into potential antimicrobials. The objective of this review has been to compile the latest advances in the development of computational models capable of identifying drugs already registered by the Food and Drug Administration for other indications with potential capacity to be applied as antimicrobials. We present studies that apply in silico methods such as machine learning, molecular docking, molecular dynamics and deep learning. Some of these studies have in vitro/in vivo results that demonstrate the reliability of this computational methodology in terms of the identification of effective molecules and new targets of interest in the treatment of infections. In addition, we present the methods that are under development and their future prospects in terms of the search for new antimicrobials. We highlight the need to implement these strategies in the research of effective drugs in the treatment of infectious diseases, as well as to continue to improve the available models and approaches to gain an advantage against the rapid emergence of AMR.