基于机器学习的有机化合物对黑头呆鱼毒性的预测
Machine learning-based prediction of toxicity of organic compounds towards fathead minnow.
作者信息
Chen Xingmei, Dang Limin, Yang Hai, Huang Xianwei, Yu Xinliang
机构信息
Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering Xiangtan Hunan 411104 China
出版信息
RSC Adv. 2020 Oct 1;10(59):36174-36180. doi: 10.1039/d0ra05906d. eCollection 2020 Sep 28.
Predicting the acute toxicity of a large dataset of diverse chemicals against fathead minnows () is challenging. In this paper, 963 organic compounds with acute toxicity towards fathead minnows were split into a training set (482 compounds) and a test set (481 compounds) with an approximate ratio of 1 : 1. Only six molecular descriptors were used to establish the quantitative structure-activity/toxicity relationship (QSAR/QSTR) model for 96 hour LC through a support vector machine (SVM) along with genetic algorithm. The optimal SVM model ( = 0.756) was verified using both internal (leave-one-out cross-validation) and external validations. The validation results ( = 0.699 and = 0.744) were satisfactory in predicting acute toxicity in fathead minnows compared with other models reported in the literature, although our SVM model has only six molecular descriptors and a large data set for the test set consisting of 481 compounds.
预测大量不同化学物质对黑头呆鱼()的急性毒性具有挑战性。在本文中,将963种对黑头呆鱼具有急性毒性的有机化合物按近似1∶1的比例分为训练集(482种化合物)和测试集(481种化合物)。仅使用六个分子描述符,通过支持向量机(SVM)结合遗传算法,建立了96小时半数致死浓度(LC)的定量构效/毒性关系(QSAR/QSTR)模型。使用内部验证(留一法交叉验证)和外部验证对最优SVM模型( = 0.756)进行了验证。尽管我们的SVM模型仅有六个分子描述符且测试集有一个由481种化合物组成的大数据集,但与文献中报道的其他模型相比,验证结果( = 0.699和 = 0.744)在预测黑头呆鱼的急性毒性方面令人满意。