AI-Driven Discovery of Nanomaterial Synergies for Next-Generation Antibiotic Alternatives

Authors- Manasa

Abstract-– The rise of antimicrobial resistance (AMR) is an urgent global health concern, necessitating the development of alternative strategies to combat resistant pathogens. Nanomaterials, with their unique physicochemical properties, offer significant promise as next-generation antimicrobial agents. However, the identification of effective nanomaterial combinations remains a complex challenge due to the vast array of possible properties and interactions. Artificial intelligence (AI), particularly machine learning (ML), has emerged as a powerful tool to navigate this complexity. By analyzing high-dimensional datasets, AI models can predict synergistic combinations of nanomaterials with enhanced antimicrobial efficacy while minimizing toxicity. Moreover, generative AI models, such as variational autoencoders and generative adversarial networks, facilitate the de novo design of novel nanomaterial structures, enabling the exploration of new therapeutic candidates. AI’s integration with omics data also provides mechanistic insights into nanomaterial-microbe interactions, which is crucial for designing materials that are both effective and resistant to resistance development. Despite the promise, challenges remain, including the need for high-quality, standardized datasets and the interpretability of AI models. Future research should focus on overcoming these challenges by developing transparent AI systems and fostering collaboration between computational scientists, microbiologists, and clinicians. This approach holds the potential to accelerate the development of nanomaterial-based antibiotics, offering a new frontier in the fight against AMR.

DOI: /10.61463/ijset.vol.13.issue2.425