AI-driven toxicity profiling of engineered nanomaterials on human cells

Abstract

Engineered nanomaterials (ENMs) are rapidly emerging as transformative agents in fields such as drug delivery, imaging, and environmental remediation, offering unique properties not seen in bulk materials. Despite their promising applications, concerns have been raised about their potential toxicity to human cells. Traditional methods of evaluating nanomaterial toxicity are often slow, expensive, and fail to fully replicate the complex biological processes that occur in the human body. In recent years, artificial intelligence (AI) has emerged as a powerful tool to accelerate toxicity profiling by enabling high-throughput analysis of data and predictive modeling. This research explores the role of AI in the toxicity assessment of ENMs, with a focus on predicting their effects on human cells. By utilizing machine learning algorithms and integrating multi-omics data, AI can provide a more comprehensive and efficient approach to profiling the toxicological risks of ENMs, facilitating the development of safer nanomaterials.