Predictive Modelling of Nanoparticle Interactions with the Human Microbiome

machine learning

The human microbiome plays an essential role in maintaining health and regulating various physiological processes. As nanoparticles (NPs) are increasingly used in medical, industrial, and consumer applications, there is growing concern regarding their interactions with the human microbiome. Understanding these interactions is crucial for assessing the potential risks and benefits of nanoparticles in health-related applications. Predictive modelling, particularly machine learning (ML), has emerged as a powerful tool to simulate and understand the behaviour of nanoparticles in microbiome environments. This paper explores the role of predictive modelling in studying nanoparticle interactions with the human microbiome, focusing on how these models can help in nanoparticle design, predicting their effects on microbial communities, and guiding safer applications.

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