Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO₂ with periodic table descriptors using machine learning approaches.

ABSTRACT: Nanoparticles with their unique features have attracted researchers over the past decades. Heavy metals, upon release and emission, may interact with different environmental components, which may lead to co-exposure to living organisms. Nanoscale titanium dioxide (nano-TiO₂) can adsorb heavy metals. The current idea is that nanoparticles (NPs) may act as carriers and facilitate the entry of heavy metals into organisms. Thus, the present study reports nanoscale quantitative structure-activity relationship (nano-QSAR) models, which are based on an ensemble learning approach, for predicting the cytotoxicity of heavy metals adsorbed on nano-TiO₂ to human renal cortex proximal tubule epithelial (HK-2) cells. The ensemble learning approach implements gradient boosting and b

SUBMITTER: Roy J 

PROVIDER: S-EPMC10509545 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature