Authors: Dr. Nazrin Hidayat
Abstract: The rapid advancement of nanotechnology has led to the widespread development and application of nanomaterials in diverse fields, including medicine, electronics, and environmental science. Despite their numerous benefits, nanomaterials pose potential risks to human health and the environment due to their unique physicochemical properties. Accurate assessment of nanomaterial toxicity is therefore crucial to ensure safe usage and regulatory compliance. Machine learning (ML), a subset of artificial intelligence, offers powerful predictive modeling techniques that can analyze complex datasets to forecast nanomaterial toxicity effectively. This paper explores the role of machine learning in predicting the toxicological effects of nanomaterials, reviews common ML algorithms employed, discusses data challenges, and highlights future prospects for integrating ML-driven toxicity prediction into nanomaterial safety assessment frameworks.
DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.550
