Author(s) :

Rajeev Ranjan.

Volume/Issue :

Abstract :

The global rise of antimicrobial resistance (AMR) has necessitated the urgent development of alternative therapeutic strategies to combat resistant pathogens. Nanomaterials have gained significant attention due to their intrinsic antimicrobial properties and potential to disrupt microbial structures, offering a promising avenue for the development of next-generation antibiotics. However, the vast combinatorial possibilities of nanomaterial properties, such as size, shape, surface charge, and functionalization, present a significant challenge in identifying optimal synergies that maximize antimicrobial efficacy while minimizing toxicity. Artificial intelligence (AI), particularly machine learning (ML), provides a powerful tool to address this challenge by enabling the systematic discovery and optimization of nanomaterial combinations. By analyzing high-dimensional datasets, AI can predict synergistic combinations of nanomaterials with enhanced bactericidal activity and reduced cytotoxicity. Furthermore, generative AI models, including variational autoencoders and generative adversarial networks, facilitate the de novo design of novel nanomaterial structures with desired antimicrobial properties. AI also plays a critical role in elucidating the mechanisms underlying nanomaterial-microbe interactions, providing insights into the molecular pathways involved in microbial resistance and guiding the design of nanomaterials that minimize resistance development. Despite the promising potential of AI in nanomaterial synergy discovery, challenges such as the quality and diversity of training datasets, model interpretability, and data standardization must be addressed. Collaborative efforts across computational, microbiological, and clinical disciplines are essential to translate AI-driven nanomaterial discoveries into clinically viable antibiotic alternatives for combating AMR.

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