Antimicrobial resistance (AMR) 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 was 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 and to continue to improve the available models and approaches to gain an advantage against the rapid emergence of AMR.

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.

Human papillomavirus (HPV) is responsible for the most common sexually transmitted infection worldwide. Cervical cancer (CC) is a severe disease that still has a high mortality rate in many countries. Persistent infection by the human papillomavirus is the necessary cause of premalignant lesions, which are precursors of CC. Screening with cytology or Pap smear have been successful in the last few years, but scientific evidence suggests that these techniques should be replaced with HPV DNA detection as the initial test when screening. There are several commercial techniques approved by the FDA for the detection of high oncological risk HPV genotypes, which have been evaluated for the population screening of 30-65-year-old women. The aim of this article is to review some virologic aspects of HPV and its relationship with cervical cancer screening. Consequently, we suggest that a new algorithm based on individualized detection of genotypes 16 and 18 should be implemented in countries where there is no population-based screening.