Data-Driven Engineering of Antimicrobial Nanomaterials for Food Safety and Biomedical Systems

Area of Science:

• Materials Science and Engineering
• Nanotechnology
• Computational Chemistry and Biology

Background:

• Antimicrobial resistance and biofilm contamination present significant challenges in food safety and medicine.
• Advanced antimicrobial nanomaterials offer tunable properties for enhanced efficacy and adaptable applications.
• Current design methods often rely on inefficient trial-and-error approaches.

Purpose of the Study:

• To review advancements in AI-assisted design of antimicrobial nanomaterials.
• To highlight how data-driven approaches optimize material properties, predict performance, and model toxicity.
• To summarize key nanomaterial systems and their translational applications.

Main Methods:

• Review of AI and machine learning strategies applied to nanomaterial design.
• Analysis of data-driven approaches for predicting antimicrobial activity and optimizing synthesis.
• Integration of explainable AI (XAI) for improved model interpretability and nanotoxicity assessment.

Main Results:

• AI and machine learning significantly accelerate the optimization of antimicrobial nanomaterials.
• Autonomous experimental platforms coupled with AI reduce development time and resource dependency.
• Key nanomaterial classes (nanoparticles, MOFs, nanocarriers, etc.) show promise in diverse applications.

Conclusions:

• AI-enabled frameworks are crucial for the rational design of next-generation antimicrobial nanomaterials.
• Data-driven strategies enhance efficacy, safety, and functional adaptability for food and biomedical sectors.
• Addressing challenges in data quality, model generalizability, and regulatory translation is essential for widespread adoption.