Machine learning-driven QSAR models for predicting the cytotoxicity of five common microplastics

In the field of microplastics (MPs) toxicity prediction, machine learning (ML) computer simulation techniques are showing great potential. In this study, six ML algorithms were utilized to predict the toxicity of MPs on BEAS-2B cells based on quantitative structure-activity relationship (QSAR) models. Comparing the models of different algorithms, the extreme gradient boosting model showed the best fit and prediction performance (R