Artificial Intelligence in Antimicrobial Resistance: From Prediction to Precision Therapeutics

Antimicrobial resistance (AMR) is an urgent and growing concern in global health, threatening the effectiveness of antibiotics and the management of infectious diseases worldwide. The rapid emergence of multidrug-resistant organisms has outpaced the development of new antimicrobial agents, leading to critical gaps in therapeutic options. While traditional drug discovery approaches have made significant contributions, their ability to counteract evolving resistance mechanisms remains insufficient. The advent of artificial intelligence (AI) has sparked a new wave of research, with machine learning and data-driven analytics delivering transformative insights into resistance epidemiology, genomic surveillance, and phenotypic prediction. However, there remains a fundamental gap between predictive modeling and the translation of these findings into tangible therapeutics.This Research Topic aims to bridge this divide by advancing AI-driven approaches that not only predict antimicrobial resistance but also facilitate the discovery and optimization of new therapies. Central goals include integrating multi-omics data, structural biology, and cheminformatics with advanced AI methodologies to deepen mechanistic understanding and inform the design of effective antimicrobial agents. Submissions are encouraged that address key questions such as: • How can AI improve the accuracy and interpretation of resistance mechanisms? • What novel computational frameworks can drive drug repurposing or precision therapeutic design? • What are the best practices for ensuring generalizability, reproducibility?• Clinical relevance in AI-powered antibiotic discovery?The scope of this Research Topic is focused yet interdisciplinary, encompassing both foundational research and translational applications of AI in combating AMR. We welcome manuscripts that bring forward experimental, computational, and methodological advances at the interface of AI and antimicrobial research. To gather further insights in this evolving field, we welcome articles addressing, but not limited to, the following themes:• AI-based prediction of antimicrobial resistance using genomic and multi-omics data• Integration of structural biology and machine learning for target identification• AI-driven QSAR and virtual screening for antimicrobial discovery• Drug repurposing strategies using AI and network-based approaches• Host–pathogen interaction modeling using machine learning• Explainable AI and interpretability in AMR modelingAppendix: We welcome original research articles, reviews, perspectives, and methodological papers. Topic editors: Dr Yogesh Kumar, Saurabh Kumar, Ajeet Sharma, Sugandh Kumar Source: https://www.frontiersin.org/research-topics/80278/artificial-intelligence-in-antimicrobial-resistance-from-prediction-to-precision-therapeutics