An effective machine learning model for rat acute oral toxicity prediction of emerging chemicals: multi-domain applications and structure-activity relationships.

Given the widespread presence of emerging contaminants in the environment, assessing and ensuring their biosafety is urgent. Under the Globally Harmonized System (GHS), the LD parameter of acute oral toxicity (AOT) is crucial for chemical safety classification. Animal testing limitations have highlighted the need for alternative methods, and machine learning offers a new approach to predict LD through quantitative structure-activity relationship (QSAR) models. This study developed and optimized a machine learning model for LD classification of emerging contaminants based on data from more than 6000 known AOT. Using molecular descriptors and fingerprints, the model achieves an accuracy above 0.86 and a recall score over 0.84, outperforming previous models. The model's robustness was confirmed across various types of emerging contaminants. Shapley additive explanations (SHAP) identified key descriptors like BCUTp_1h, ATSC1pe, and SLogP_VSA4, while the information gain (IG) method highlighted alert substructures [P-O, P-S]. These findings suggest that compounds with high polarizability, mean electronegativity and significant surface area may adversely affect rats. This model enhances understanding of acute toxicity mechanisms and serves as a tool for early screening of safer compounds, promoting the design of greener chemicals.