In silico prediction of serious eye irritation or corrosion potential of chemicals
In silico prediction of serious eye irritation or corrosion potential of chemicals
复制标题
通过计算机预测化学品的严重眼睛刺激或腐蚀潜力
DOI：
10.1039/c6ra25267b
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发表时间：
2017-01-01
期刊：
影响因子：
3.9
通讯作者：
Liu, Guixia
中科院分区：
文献类型：
--
作者：
Wang, Qin;Li, Xiao;Liu, Guixia
快速、正确地识别眼睛刺激物或腐蚀性化学物质是健康危害评估中的一个重要问题。本研究的目的是描述将化学品分类为刺激物/腐蚀物或非刺激物/非腐蚀物的计算机模拟方法的发展。从现有数据库和文献中收集了总共 5220 种化学品的严重眼睛刺激 (EI) 数据集和 2299 种化学品的眼睛腐蚀 (EC) 数据集。开发了结构-活动关系 (SAR) 模型，通过机器学习方法单独预测严重的 EI 或 EC。根据总体预测精度，Pub-SVM 模型对于严重 EI（总体分类精度 CA = 0.946）和 EC（CA = 0.959）均给出了最佳结果。训练集严重 EI 的敏感性和特异性分别为 97.3% 和 86.7%，外部验证集的敏感性和特异性分别为 96.9% 和 82.7%。同样，训练集的 EC 敏感性和特异性分别为 95.5% 和 96.2%，外部验证集的 EC 敏感性和特异性分别为 94.9% 和 96.2%。高特异性和敏感性表明我们的模型可靠且稳健，可用于预测化合物 EI/EC 的潜在严重性。此外，通过结合信息增益和子结构频率分析，识别出一些表征严重 EI/EC 的结构警报。
Rapidly and correctly identifying eye irritants or corrosive chemicals is an important issue in health hazard assessment. The purpose of this study is to describe the development of in silico methods for the classification of chemicals into irritants/corrosives or non-irritants/non-corrosives. A total of 5220 chemicals for a serious eye irritation (EI) dataset and 2299 chemicals as an eye corrosion (EC) dataset were collected from available databases and literature. Structure–activity relationship (SAR) models were developed to separately predict serious EI or EC via machine learning methods. According to the overall prediction accuracy, the Pub-SVM model gave the best results for both serious EI (overall classification accuracy CA = 0.946) and EC (CA = 0.959). The sensitivity and specificity of serious EI were 97.3% and 86.7% for the training set, and 96.9% and 82.7% for the external validation set, respectively. Similarly, the sensitivity and specificity of EC were 95.5% and 96.2% for the training set, and 94.9% and 96.2% for the external validation set, respectively. The high specificity and sensitivity indicated that our models were reliable and robust, which can be used to predict the potential seriousness of EI/EC of compounds. Moreover, several structural alerts for characterizing serious EI/EC were identified using the combination of information gain and substructure frequency analysis.