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首页> 外文期刊>Environmental toxicology and chemistry >Target Site Model: Predicting Mode of Action and Aquatic Organism Acute Toxicity Using Abraham Parameters and Feature-Weighted k-Nearest Neighbors Classification
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Target Site Model: Predicting Mode of Action and Aquatic Organism Acute Toxicity Using Abraham Parameters and Feature-Weighted k-Nearest Neighbors Classification

机译：目标网站模型：使用亚伯拉罕参数预测动作和水生生物急性毒性和特征加权k最近邻居分类

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摘要

A database of 1480 chemicals with 47 associated modes of action compiled from the literature encompasses a wide range of chemical classes (alkanes, polycyclic aromatic hydrocarbons, pesticides, and polar compounds) and includes toxicity data for 79 different aquatic genera. The data were split into a calibration group and a validation group (80/20) to apply k-nearest neighbors (k-NN) methodology to predict the toxic mode of action for the compound. Other approaches were tested (support vector machines and linear discriminant analysis) as well as variations in the k-NN technique (distance weighting, feature weighting). Best-prediction results were found with k = 3, in a voting platform with optimized feature weighting. Using the predicted mode of action, the appropriate polyparameter target site model for that mode of action is applied to calculate the 50% lethal concentration (LC50). Predicted LC50s for the validation database resulted in a root-mean squared error (RMSE) of 0.752. This can be compared to an RMSE of 0.655 for the same validation set using the reference mode of action labels. The complete database resulted in an RMSE of 0.793 for reference mode of action labels. This confirms that the classification model has sufficient accuracy for predicting the mode of action and for determining toxicity using the target site model. Environ Toxicol Chem 2019;38:375-386. (c) 2018 SETAC
机译：从文献中编制的47个相关动作模式的数据库包括各种化学类（烷烃，多环芳烃，杀虫剂和极性化合物），包括79种不同水生成的毒性数据。将数据分成校准组和验证组（80/20）以应用K-Collest邻居（K-NN）方法，以预测化合物的有毒作用模式。测试其他方法（支持向量机和线性判别分析）以及K-NN技术的变化（距离加权，特征加权）。在具有优化特征加权的投票平台中，用K = 3发现了最佳预测结果。使用预测的动作模式，适用于该作用模式模式的适当的息肉计目标网站模型来计算50％的致死浓度（LC50）。验证数据库的预测LC50S产生0.752的根本平方误差（RMSE）。这可以与使用参考操作标签的参考模式相同的验证设置的0.655的RMSE进行比较。完整的数据库导致RMSE为0.793，供参考操作标签。这证实了分类模型具有足够的准确性来预测使用目标站点模型来确定毒性。环境毒素化学2019; 38：375-386。 （c）2018 Setac

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