Innovative approaches to therapeutic target discovery amid the global challenge of antimicrobial resistance

Antimicrobial resistance is a constant threat to global public health, requiring innovative strategies for therapeutic target identification. Hence, this narrative review discusses the application of structural modeling and artificial intelligence in the functional prediction of proteins encoded by multidrug-resistant bacterial genomes. Tools such as AlphaFold and RoseTTAFold have enabled high-accuracy three-dimensional structure prediction, facilitating the annotation of hypothetical proteins and the identification of conserved domains and catalytic sites. These computational approaches bridge the gap between genomic data and biological function, accelerating drug discovery and guiding design of new antimicrobial bioactive compounds. Despite notable advances, several challenges have persisted regarding experimental validation and genomic variability, revealing an opportunity to integrate artificial intelligence-driven modeling with bioinformatics as a transformative method for better understanding resistance mechanisms and prioritizing novel therapeutic targets. This review highlights how artificial intelligence bridges bacterial genomics and antimicrobial drug discovery. Its relevance stems from the validation of computational approaches that transcend the constraints of conventional lab-based biology, allowing for the pinpoint identification of catalytic sites in resistant strains. By outlining the current landscape and validation hurdles, this study offers a strategic framework for the fast-tracked, cost-effective prioritization of therapeutic targets, making it a vital resource for tackling emerging pathogens.