Biocides are multi-component products used to control undesired and harmful organisms able to affect human or animal health or to damage natural and manufactured products. Because of their widespread use, aquatic and terrestrial ecosystems could be contaminated by biocides. The environmental impact of biocides is evaluated through eco-toxicological studies with model organisms of terrestrial and aquatic ecosystems. We focused on the development of in silico models for the evaluation of the acute toxicity (EC50) of a set of biocides collected from different sources on the freshwater crustacean Daphnia magna, one of the most widely used model organisms in aquatic toxicology. Toxicological data specific for biocides are limited, so we developed three models for daphnid toxicity using different strategies (linear regression, random forest, Monte Carlo (CORAL)) to overcome this limitation. All models gave satisfactory results in our datasets: the random forest model showed the best results with a determination coefficient r2 = 0.97 and 0.89, respectively, for the training (TS) and the validation sets (VS) while linear regression model and the CORAL model had similar but lower performance (r2 = 0.83 and 0.75, respectively, for TS and VS in the linear regression model and r2 = 0.74 and 0.75 for the CORAL model).

中文翻译：

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QSAR杀生物剂模型：预测大型蚤（Daphnia magna）急性毒性的实例。

杀生物剂是用于控制不希望的有害生物的多组分产品，这些有害生物会影响人类或动物的健康或破坏天然和人造产品。由于其广泛使用，水生和陆地生态系统可能会被杀生物剂污染。通过对陆地和水生生态系统模型生物的生态毒理学研究，评估了杀生物剂对环境的影响。我们专注于计算机模型的开发，以评估从淡水甲壳类水蚤（Daphnia magna）（水生毒理学中使用最广泛的模型生物之一）中不同来源收集的一系列杀生物剂的急性毒性（EC50）。专门针对杀生物剂的毒理学数据有限，因此我们使用不同的策略（线性回归，随机森林，蒙特卡洛（CORAL））来克服这一限制。所有模型在我们的数据集中都给出了令人满意的结果：随机森林模型以训练系数（TS）和验证集（VS）的最佳系数分别为r2 = 0.97和0.89，而线性回归模型和CORAL模型的结果最好。具有相似但较低的性能（线性回归模型中的TS和VS分别为r2 = 0.83和0.75，而CORAL模型中的r2 = 0.74和0.75）。