Predicting the toxicity of organic toxicants to aquatic life through chemometric approach is challenging area. In this paper, a six-descriptor quantitative structure-activity/toxicity relationship (QSAR/QSTR) model was successfully developed for the toxicity pEC10 of organic chemicals against Pseudokirchneriella subcapitata, by applying support vector machine (SVM) together with genetic algorithm. A sufficiently large data set consisting of 334 organic chemicals was randomly divided into a training set (167 compounds) and a test set (167 compounds) with a ratio of 1:1. The optimal SVM model possesses coefficient of determination R2 of 0.76 and mean absolute error (MAE) of 0.60 for the training set and R2 of 0.75 and MAE of 0.61 for the test set. Compared with other models reported in the literature, our SVM model for the toxicity pEC10 shows significant statistical quality and satisfactory predictive ability, although it has fewer molecular descriptors and more samples in the test set. A QSTR model for pEC50 of organic chemicals against Pseudokirchneriella subcapitata was also developed with the same subsets and molecular descriptors.

中文翻译：

---

有机化学物质对拟人假单胞菌的定量结构-毒性关系。

通过化学计量方法预测有机毒物对水生生物的毒性是一个具有挑战性的领域。本文应用支持向量机（SVM）和遗传算法，成功建立了有机物对拟人假单胞菌的毒性pEC10的六描述定量构效关系模型（QSAR / QSTR）。将由334种有机化学物组成的足够大的数据集随机分为1：1的训练集（167种化合物）和测试集（167种化合物）。最佳SVM模型的确定系数R2为0.76，训练集的平均绝对误差（MAE）为0.60，测试集的R2为0.75，MAE为0.61。与文献报道的其他模型相比，我们的毒性pEC10的SVM模型显示出显着的统计质量和令人满意的预测能力，尽管它在测试集中具有更少的分子描述符和更多的样本。还开发了具有相同子集和分子描述子的针对伪装假单胞菌的有机化学物质pEC50的QSTR模型。