Direct prediction of bioaccumulation of organic contaminants in plant roots from soils with machine learning models based on molecular structures

Abstract

Root concentration factor is an important substance-specific characterization parameter for plant uptake of organic contaminants from soils in life cycle impact assessment (LCIA); however, the availability of a reliable dataset and building of robust predictive models remain challenging due to the complexity of chemical-soil-plant root interactions. Here we developed end-to-end machine learning models to devolve the interaction complexity by training on a unified dataset with 341 data points covering 72 chemicals. The gradient boosting regression tree (GBRT) model based on the extended connectivity fingerprints (ECFP) demonstrated a superior prediction performance with R-squared of 0.77 and Mean Absolute Error (MAE) of 0.22. In addition, partial dependence analysis was used to determine the nonlinear relationships in the chemical-soil-plant root system. Feature importance analysis revealed the relationship between and chemical topological structures. Stemming from its simplicity and universality, the GBRT-ECFP model provides a promising tool for LCIA to better characterize the human and ecological impacts of chemicals in the environment.