Ensemble Machine Learning Models for Multi-Target Nanoparticle Cytotoxicity Prediction

Nanoparticle cytotoxicity prediction can help with early safety screening, though a reliable evaluation is kinda hard when datasets have repeated nanoparticle identities, plus the toxicity labels are very skewed. In this work we put forward a leakage-audited machine-learning pipeline that aims to classify nanoparticle cytotoxicity by using physicochemical, exposure, and biological-response descriptors. The curated dataset has 308 records and 22 engineered features across three toxicity categories, yet the rarest category only has two records, and it does not show up in the frozen, held out test split. We then combine Random Forest and LightGBM models using logistic regression stacking. All preprocessing and resampling are done inside the training partitions, so it stays leakage-checked. For a fair like for like comparison, every model is tested with nanoparticle-grouped cross validation, using macro F1, balanced accuracy, and per class metrics. We also include a supplementary analysis based on the frozen split, but we treat the overall numbers as partial, two-class style evidence, not as a complete three-class validation. Feature importance and case level explanations point to how much biological-response, exposure, and physicochemical variables contribute. Overall the results suggest the pipeline can be made auditable for screening, but they also underline that we still need bigger and more balanced datasets, and independent external validation, before anyone should claim broad generalization or regulatory readiness.