Artificial Intelligence in Drug Discovery and Development Against Antimicrobial Resistance: A Narrative Review

Abstract

Antimicrobial resistance (AMR) presents a formidable global health challenge, jeopardizing the efficacy of current antibiotics and posing a substantial threat to public health. The escalating prevalence of AMR demands innovative solutions. However, the traditional drug discovery process for combating AMR is marked by significant costs, prolonged timelines, frequent inefficacies, and numerous developmental hurdles. This narrative review explores the potential role of artificial intelligence (AI) in addressing AMR through drug discovery and development. It assesses the current state of AMR, critiques the limitations of conventional drug discovery methods, and elucidates the opportunities and advancements afforded by AI. The review delves into various AI applications, encompassing machine learning, deep learning, and language models, for the identification of novel antimicrobial agents, optimization of drug design, and prediction of AMR mechanisms. Additionally, it examines the integration of AI with high-throughput screening, genomics, and proteomics to expedite the discovery and development of new antimicrobial compounds. The review concludes by addressing challenges and ethical considerations linked to AI implementation in AMR research, emphasizing the imperative for collaborative efforts among scientists, policymakers, and healthcare professionals to effectively combat AMR.