Using Machine Learning to Predict Adverse Effects of Metallic Nanomaterials to Various Aquatic Organisms

The wide production and use of metallic
nanomaterials (MNMs) leads
to increased emissions into the aquatic environments and induces high
potential risks. Experimentally evaluating the (eco)toxicity of MNMs
is time-consuming and expensive due to the multiple environmental
factors, the complexity of material properties, and the species diversity.
Machine learning (ML) models provide an option to deal with heterogeneous
data sets and complex relationships. The present study established
an in silico model based on a machine learning properties-environmental
conditions-multi species-toxicity prediction model (ML-PEMST) that
can be applied to predict the toxicity of different MNMs toward multiple
aquatic species. Feature importance and interaction analysis based
on the random forest method indicated that exposure duration, illumination,
primary size, and hydrodynamic diameter were the main factors affecting
the ecotoxicity of MNMs to a variety of aquatic organisms. Illumination
was demonstrated to have the most interaction with the other features.
Moreover, incorporating additional detailed information on the ecological
traits of the test species will allow us to further optimize and improve
the predictive performance of the model. This study provides a new
approach for ecotoxicity predictions for organisms in the aquatic
environment and will help us to further explore exposure pathways
and the risk assessment of MNMs.