We developed models for predicting fish early-life stage (ELS) toxicities oriented to industrial chemicals. The training set was constructed without data from the Office of Pesticide Programs Pesticide Ecotoxicity Database, the main source for the pesticide-biased training set used in our previous work (SAR QSAR Environ. Res. 29:9, 725–742). In addition to the descriptors from the previous study, we also used water solubility to develop the new models, which were evaluated against the test set used in our previous study so that we could focus on the effects of the different training set and the additional descriptor. The statistics for the new models were hardly better than those for the previous models, which suggests, contrary to our expectations, that pesticide-biased data can successfully be used to develop models for predicting the fish ELS toxicities oriented to industrial chemicals. Acute Daphnia magna toxicity was important for the predictive QSAARs in both studies. A distance-based method for defining the applicability domains indicated that water solubility was a key indicator for detecting underestimated chemicals. The comparison of fish ELS toxicities for chemicals presented in different literatures revealed the uncertainty of the experimental data, which may lead to the low predictivity.

我们开发了用于预测针对工业化学品的鱼类早期生命期（ELS）毒性的模型。培训集的构建没有农药计划办公室农药生态毒性数据库的数据，农药在我们以前的工作中使用的农药偏见培训集的主要来源（SAR QSAR Environ。Res。29：9，725–742）。除了先前研究的描述子之外，我们还使用水溶性来开发新模型，并根据先前研究中使用的测试集对模型进行了评估，以便我们可以专注于不同训练集和附加描述子的影响。新模型的统计数据几乎不比以前的模型好，这表明，与我们的预期相反，农药偏倚的数据可以成功地用于开发模型，以预测针对工业化学品的鱼类ELS毒性。急性在这两项研究中，大型蚤（Daphnia magna）的毒性对于预测QSAAR都很重要。基于距离的定义适用范围的方法表明，水溶性是检测低估化学品的关键指标。不同文献中鱼类ELS对化学药品的毒性比较显示出实验数据的不确定性，这可能导致较低的可预测性。