机器学习预测有机化学品对稀有鮈鲫急性毒性

Prediction of acute toxicity of organic chemicals on rare minnow using machine learning

搜集有机化学品对稀有鮈鲫(Gobiocypris rarus)急性毒性数据,并采用部分斑马鱼(Danio rerio)和黑头呆鱼(Pimephales promelas)的急性毒性数据,建立了基于机器学习的有机化学品对稀有鮈鲫急性毒性预测方法.首先使用二分类模型判定有机化学品对稀有鮈鲫是否会产生急性毒性,若判定为有急性毒性,再使用回归模型预测其半致死浓度LC_(50).对不同机器学习模型进行对比,二分类模型中支持向量机最优,训练集和测试集的准确率分别为92.4%和88.6%;回归模型中弹性网络回归法最优,训练集和测试集的调整R^(2)分别为0.87和0.75,留一法交叉验证系数Q_(LOO)^(2)为0.52,外部验证系数Q_(EXT)^(2)为0.71.两种模型具有较好的准确性、稳健性和预测能力.第一电离势和正辛醇水分配系数对分类影响较大,拓扑荷对回归预测结果影响较大.

Acute toxicity data of organic chemicals were collected for rare minnows(Gobiocypris rarus).A machine learning method was developed to predict the acute toxicity of organic chemicals specially for rare minnow,using existing acute toxicity data for zebrafish(Danio rerio)and fathead minnow(Pimephales promelas).A binary model was used to determine whether the organic chemical have acute toxicity to rare minnow;if yes,a regression model was then utilized to predict the median lethal concentration LC_(50).Different machine learning models were compared,and it was found that the support vector machine performed the best in the binary classification model,with the accuracies of 92.4%in the training set and 88.6%in the test set,respectively.The elastic net regression method demonstrated the best performance in the regression model.The adjusted R^(2) of the training set was 0.87,while the adjusted R^(2) of the test set was 0.75.The cross-validation coefficient Q_(LOO)^(2) of the left-one-out method was 0.52,and the external validation coefficient Q_(EXT)^(2) was 0.71.The two models exhibited commendable accuracy,robustness,and predictive capability.The first ionization potential and n-octanol water partition coefficient had a greater effect on the classification,and the regression prediction results were more heavily influenced by the topological charge.The above results offer a precise and efficient prediction method for assessing the acute toxicity of the rare minnow,an endemic model organism in China,significantly expediting the environmental risk assessment of organic chemicals.