Navigating the Eukaryotic Paradox: Insights for AI-Driven Computational Strategies Against Antifungal Resistance

The escalating crisis of invasive fungal infections and increasing antifungal resistance demand rapid therapeutic innovation. Given the critically narrow clinical arsenal, artificial intelligence (AI) and machine learning (ML) have emerged as pivotal strategies to accelerate drug repurposing, providing a rapid alternative to de novo drug discovery. However, translational success in medical mycology faces a major biological hurdle: the "eukaryotic paradox," the extensive genomic, metabolic, and structural homology between fungal cells and human hosts. This review provides a critical and comparative analysis of AI-driven drug repurposing as a therapeutic strategy. This review characterizes the hierarchies of structural barriers, biophysical transport bottlenecks, and computational failure modes across bacterial, viral, and fungal pathogens to explain why medical mycology remains one of the most difficult frontiers in computational pharmacology. It reviews limitations of fungal cell walls, biofilm EPS matrices, morphologic dynamic dimorphism, and scarcity of high-resolution 3D protein structures for AI models. Next-generation computational approaches and experimental platforms, ranging from physics-informed neural networks and transcriptomic profiling to microfluidic organ-on-a-chip validation systems, are also described to overcome these resistance mechanisms. Finally, this review emphasizes the importance of the development of tailored, physics-informed AI paradigms for effective therapeutic strategies against emerging resistant fungal pathogens.