A Comparative Evaluation of Machine Learning and Deep Graph Learning for Chemical Ecotoxicological Prediction.

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 be

SUBMITTER: Lou X 

PROVIDER: S-EPMC12391983 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature