Predicting Nanotoxicity across Materials Using Interpretable Machine Learning and Descriptors Based on the Periodic Table

The biological effects of engineered nanomaterials are largely determined by their intrinsic physicochemical properties; however, predicting these effects across materials with different core compositions remains a significant challenge. In this study, we developed an interpretable machine learning framework to enable cross-material nanotoxicity prediction using descriptors based on the periodic table. A curated data set comprising 1206 entries of metal oxide nanoparticles with cytotoxicity endpoints was constructed from high-quality literature sources. We evaluated multiple machine learning models to systematically analyze their performance using both within-material and cross-material validation strategies. Initial models based solely on experimental conditions and global nanomaterial properties showed high accuracy in within-material prediction but failed to generalize to unseen materials. To overcome this limitation, we introduced elemental-level descriptors, including electronegativity, ionization energy, atomic radius, and oxidation state. Incorporating these fundamental features significantly improved cross-material prediction performance, with coefficient of determination (<i>R</i>²) values from approximately 0.35 to 0.65 for unseen nanomaterials such as CuO, ZnO, MgO, and Cu₂O. Further experimental validation using NiO and Cr₂O₃ nanoparticles on A549 and HUVEC cell lines confirmed the reliability of model predictions and highlighted material- and cell-type-specific toxic responses. Model interpretation and oxidative stress assay provided mechanistic insights into the cytotoxicity induced by the metal oxide nanoparticles. This study demonstrates the feasibility of cross-material toxicity prediction and establishes a scalable and interpretable computational framework for safer nanomaterial design and risk assessment.