AI-Driven Insights into Antimicrobial Nanoparticles: A Brief Review

Author(s)	Muthaiah Chintha, Raghavendar Uppari, Balaswamy Puligilla
Country	India
Abstract	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.
Keywords	Nanoparticles, Antimicrobial Resistance (AMR), Artificial Intelligence (AI), Machine Learning (ML), Green Synthesis, Toxicity Prediction
Published In	Conference / Special Issue (Volume 7 \| Issue 5) - One Day National Seminar on “Computational Science: The Intersection of Math, Physics, Chemistry and Computer Science” (SVGASCA-2025) (October 2025)
Published On	2025-10-08
DOI	https://doi.org/10.36948/ijfmr.SVGASCA-2025.1111
Short DOI	https://doi.org/g9543s

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