Authors: Dr. Halifu Zenbe
Abstract: Nanoparticles play a pivotal role in modern biomedical applications, particularly in targeted drug delivery, imaging, and diagnostics. Understanding the complex interactions between nanoparticles and cellular systems is crucial to ensure efficacy, minimize toxicity, and enhance the overall performance of nanomedicine. However, the multifaceted nature of nanoparticle-cell interactions, influenced by numerous physicochemical parameters and cellular heterogeneity, poses a significant challenge for traditional experimental approaches. Machine learning (ML), a subset of artificial intelligence, provides powerful tools for analyzing complex datasets and predicting biological responses to nanoparticles. This paper explores various machine learning methodologies applied to predict nanoparticle-cell interactions, discusses key applications and case studies, addresses the challenges in data acquisition and model validation, and outlines future perspectives to improve predictive accuracy and accelerate nanomedicine development.
DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.545
