Reimagining antimicrobial resistance: AI-driven predictive epidemiology and the C-AMRE framework for next-generation antibiotic discovery.

Antimicrobial Resistance (AMR) has evolved from a clinically observed phenomenon into a complex, dynamic, and partially predictable evolutionary process. Traditional approaches centered on phenotypic detection and retrospective surveillance are increasingly inadequate to address the accelerating pace of resistance emergence. This review presents a paradigm shift toward predictive antimicrobial science, driven by the convergence of Evolutionary Intelligence (EI), Artificial Intelligence (AI), genomic surveillance, molecular simulation, and digital twin technologies. Leveraging whole-genome sequencing (WGS) and resistome analytics, AI models can identify latent resistance determinants and forecast evolutionary trajectories before clinical manifestation, enabling a transition from reactive to anticipatory intervention strategies. Central to this transformation is the concept of the Computational Antimicrobial Resistance Ecosystem (C-AMRE), an integrated, multi-layered framework that unifies data acquisition, predictive modeling, mechanistic simulation, and clinical feedback into a continuous learning system. Within this ecosystem, molecular simulations provide mechanistic insights into resistance at atomic and systems levels, while AI-driven pharmacology enables the design of novel antibiotics, antimicrobial peptides, and Nano-Adjuvants through generative and optimization-based approaches. The incorporation of digital twins further advances precision medicine by simulating patient-specific infection dynamics, pharmacokinetics/pharmacodynamics (PK-PD), and resistance evolution in real time, thereby enabling adaptive and personalized therapeutic strategies. Across micro-, meso-, and macro-scales, these technologies collectively redefine AMR as a systems-level phenomenon that can be modeled, predicted, and strategically managed. However, challenges related to data integration, model interpretability, validation, ethical governance, and global accessibility remain critical barriers to implementation. Despite these limitations, the integration of AI and computational frameworks positions antimicrobial research at the forefront of a new era, where antibiotics are no longer static interventions but adaptive components of intelligent, continuously evolving systems. This review highlights the transition from detection to prediction and ultimately to adaptive intervention, emphasizing the role of computational ecosystems in shaping the future of sustainable antimicrobial therapy.