The escalating crisis of antimicrobial resistance claims nearly 5 million lives annually. Resistant infections now account for 4. 95 million deaths worldwide and economic losses projected to reach 300 billion by 2030. Despite this urgent threat, traditional antibiotic discovery has declined precipitously. New chemical entity approvals have fallen by over 50%, while existing therapeutics are rapidly rendered obsolete by sophisticated bacterial resistance mechanisms including extended-spectrum β-lactamases, carbapenemases, and multidrug efflux pumps. Bio-based products have historically provided humanity’s most transformative antibiotics, yet conventional discovery pipelines face insurmountable bottlenecks. A total of 99. 9% of environmental microbes remain unculturable. Biosynthetic gene clusters are predominantly silent under laboratory conditions, and dereplication efforts achieve only 2 to 5% annotation rates. This review presents a comprehensive examination of how artificial intelligence (AI) is revolutionizing bio-based product-based antibacterial discovery. We analyze AI-driven genome mining tools that have identified over 170, 000 biosynthetic gene clusters across bacterial genomes, deep learning architectures achieving 88. 5% bioactivity prediction accuracy, and generative models delivering experimental hit rates exceeding 50%—representing 50- to 90-fold improvements over traditional screening. Through validated case studies spanning in silico prediction to in vivo efficacy, we demonstrate that AI integration is not merely accelerating discovery but fundamentally transforming our capacity to access nature’s previously inaccessible chemical diversity in the fight against antimicrobial resistance.
Gudepu et al. (Fri,) studied this question.