Identification of structural features of surface modifiers in engineered nanostructured metal oxides regarding cell uptake through ML-based classification

• Indrasis Dasgupta,
• Totan Das,
• Biplab Das and
• Shovanlal Gayen

Beilstein J. Nanotechnol. 2024, 15, 909–924, doi:10.3762/bjnano.15.75

• , shape, and surface charge of NPs, as well as their surface functionalization. In the current study, classification-based ML models (i.e., Bayesian classification, random forest, support vector classifier, and linear discriminant analysis) have been developed to identify the features/fingerprints that

• governing the cellular uptake of ENMOs. The study will direct scientists in the design of ENMOs of higher cellular uptake efficiency for better therapeutic response. Keywords: Bayesian classification; cellular uptake; machine learning; nanoparticles (NPs); Introduction In recent years, the rapid

• modifiers in the training set (70%) and 21 modifiers in the test set (30%) for the different classification-based QSAR analyses. Bayesian classification study Bayesian classification was carried out via the “Create Bayesian model” protocol in Discovery Studio 3.0 [35]. To develop a model, various