Antimicrobial resistance crisis: could artificial intelligence be the solution?

Abstract

Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed. The discovery and introduction of novel antibiotics are time-consuming and expensive. According to WHO’s report of antibacterial agents in clinical development, only 18 novel antibiotics have been approved since 2014. Therefore, novel antibiotics are critically needed. Artificial intelligence (AI) has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics. Here, we first summarized recently marketed novel antibiotics, and antibiotic candidates in clinical development. In addition, we systematically reviewed the involvement of AI in antibacterial drug development and utilization, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well as resistance mechanism prediction, and antibiotic stewardship.

Navigating Antibacterial Frontiers: A Panoramic Exploration of Antibacterial Landscapes, Resistance Mechanisms, and Emerging Therapeutic Strategies

“…This analysis also demonstrates potential areas of growth in this field through, for example, targeting other bacteria apart from the ones previously mentioned; analysis of potential antibacterials in the realm of biocompatibility, cytotoxicity, stability, and metabolites; increasing the use of AI in antibacterial resistance studies, screening, and development of a larger scope of potential antibiotics of different classes; application of AI in nanoparticle and nanomaterial construction, use, and screening as potential antibacterial agents; the application of certain statistical and AI models, such as regression analysis and random forest ML algorithms; and the drastic need for improving the processing of all the information generated by this new technology. Various reviews discussing the use of AI and its limitations in resistance studies and drug discovery have been published. ,,− …”

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

“…Moreover, the development of the novel antimicrobial halicin that has demonstrated in vitro and in vivo efficacy against the highpriority pathogen Acinetobacter baumannii was supported with machine learning models [4]. This drug was repurposed, as originally it was being developed as an anti-diabetic agent; however, an AI-supported analysis showed it has a bactericidal effect after screening via the Drug Repurposing Hub [2].…”

“…These technologies are rapidly being incorporated across various disciplines and almost every research area to accelerate scientific discoveries. As they allow the optimal utilisation of large amounts of data, for example, clinical and laboratory data, and can support evidence-based decision making, they substantially save time on research [1][2][3][4].…”