QSAR modelling of pesticide modes of toxic action across trophic levels via multi-label extreme gradient boosting.

内容摘要

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. 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). 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.