Systematic review highlights AI's role in predictive modeling and safety assessment of nanoparticles.
Antimicrobial resistance (AMR) remains a global health emergency, demanding innovative and effective alternatives to traditional antibiotics. Nanoparticles (NPs), particularly metal, metal-oxide, and plant-derived variants, offer robust antimicrobial capabilities. However, optimization and safety assessment persist as major obstacles. Recent advancements in artificial intelligence (AI) and machine learning (ML) have shown promise for predictive formulation, mechanistic understanding, and toxicity modeling of antimicrobial NPs. This systematic review presents a synthesis of cutting-edge AI-assisted methodologies covering predictive modeling, data-driven design, and high-throughput screening highlighted by examples and nanostructured surfaces. Challenges and future directions are also discussed.
No takes yet. Share an insight, caveat, or question.
Chintha et al. (2025) studied this question.