Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-informed Machine Learning.

The safety of inorganic nanoparticles (NPs) remains a critical challenge for their clinical translation. To address this, we developed a machine-learning (ML) framework that predicts NP toxicity both and , leveraging physicochemical properties and experimental conditions. A curated cytotoxicity dataset was used to train and validate binary classification models, with top-performing models undergoing explainability analysis to identify key determinants of toxicity and establish structure-toxicity relationships.