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 LD₅₀ 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 LD₅₀ through quantitative structure-activity relationship (QSAR) models. This study developed and optimized a machine learning model for LD₅₀ 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.