The study aimed to assess the impacts of ionic liquids (ILs) as innovative alternatives to traditional organic solvents on aquatic environments and human health. Five machine learning methods, including multiple linear regression (MLR), partial least squares regression (PLS), random forest regression (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost), were used to construct the prediction models of the toxicity of ILs to D. magna, D.
View Article and Find Full Text PDFA computational framework based on placental gene networks was proposed in this work to improve the accuracy of the placental exposure risk assessment of environmental compounds. The framework quantitatively characterizes the ability of compounds to cross the placental barrier by systematically considering the interaction and pathway-level information on multiple placental transporters. As a result, probability scores were generated for 307 compounds crossing the placental barrier based on this framework.
View Article and Find Full Text PDFAs the one of the most important protein of placental transport of environmental substances, the identification of ABCG2 transport molecules is the key step for assessing the risk of placental exposure to environmental chemicals. Here, residue interaction network (RIN) was used to explore the difference of ABCG2 binding conformations between transportable and non-transportable compounds. The RIN were treated as a kind of special quantitative data of protein conformation, which not only reflected the changes of single amino acid conformation in protein, but also indicated the changes of distance and action type between amino acids.
View Article and Find Full Text PDF