Abstract
Regulating chemicals to protect the environment based on ecotoxicological assessments is a major challenge. However, experimental ecotoxicity tests are time-consuming and expensive, which underscores the need for accurate prediction methods. In this study, we conducted a comprehensive analysis on the application of machine learning and graph-based learning techniques for the ecotoxicological prediction of chemicals. A total of 161 models were constructed using a combination of three molecular representations (Morgan, MACCS, and Mol2vec), six machine learning algorithms (KNN, NB, RF, SVM, XGB, and DNN), and five graph neural networks (GAT, GCN, MPNN, Attentive FP, and FPGNN). In predicting the ecotoxicity of three aquatic taxonomic groups - fish, crustaceans, and algae - GCN achieved the best performance overall. In the same-species predictions, GCN models achieved the highest values of area under the ROC curve (AUC), ranging between 0.982 and 0.992. In cross-species predictions, GAT and GCN achieved the best and second-best performance, respectively. However, both models exhibited a reduction of approximately 17% in AUC values when predicting the fish group while being trained on the same chemical data for the crustaceans and algae groups. Interestingly, cross-species predictions for unseen chemicals are only better off by DNN with the MACCS fingerprint, yielding an AUC of 0.821. Our findings underscore the critical need to further advance computational prediction methods in order to accurately predict the ecotoxicity of chemicals across species. The ecotoxicology prediction web server for fish, algae, and crustaceans is accessible at https://app.cbbio.online/ecotoxicology/home.