Mei_2026_Pest.Manag.Sci__

BACKGROUND: In silico identifying modes of toxic action (MOAs) of toxic chemicals are of significance to gaining knowledge about the toxic severities and mechanisms of chemicals at the whole-organism level. Currently, existing in silico models commonly focus on a diversity of chemical MOAs to one specific species, without considering their toxic effects to off-target organisms. RESULTS: In this work, we propose a quantitative structure-activity relationship (QSAR)-based multi-label extreme gradient boosting (XGBoost) model to consider the scenario that a pesticide exhibits multiple MOAs on different organisms across trophic levels. In the integral space of 16 taxa-specific MOAs, pesticides are represented by the structural fingerprint MACCSKeys and Morgan, and each of the 16 taxa-specific MoAs is treated as a class label. K-fold cross-validation (k = 10) shows that the proposed multi-label XGBoost model achieves 0.81 micro recall, 0.72 macro recall, 0.55 perfect match ratio, and 69.5% Jaccard accuracy. Among the 16 taxa-specific MOAs, 12 MOAs achieve satisfactory recall rates ranging from 0.643 (aquatic-narcosis) to 0.986 (aquatic acetylcholinesterase inhibition). An external test shows that 84.62% of the herbicides, exhibiting plant photosynthesis inhibition, are correctly recognized. The holdout test shows that the proposed model, though possessing a much higher complexity of label space, outperforms or performs equivalently to existing multi-class model (linear discriminant analysis). CONCLUSION: Computational results show that narcosis majorly exhibits as an independent toxic effect, or an accessory/baseline toxic effect preferentially accompanied by reactive or other specific MOAs, and that the proposed multi-label XGBoost model potentially benefits deriving baseline (narcosis) toxicity models for the studied organisms. 2026 Society of Chemical Industry.