Lei Tailong, Chen Fu, Liu Hui, Sun Huiyong, Kang Yu, Li Dan, Li Youyong, Hou Tingjun
College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, P. R. China.
Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University , Suzhou, Jiangsu 215123, P. R. China.
Mol Pharm. 2017 Jul 3;14(7):2407-2421. doi: 10.1021/acs.molpharmaceut.7b00317. Epub 2017 Jun 21.
As a dangerous end point, respiratory toxicity can cause serious adverse health effects and even death. Meanwhile, it is a common and traditional issue in occupational and environmental protection. Pharmaceutical and chemical industries have a strong urge to develop precise and convenient computational tools to evaluate the respiratory toxicity of compounds as early as possible. Most of the reported theoretical models were developed based on the respiratory toxicity data sets with one single symptom, such as respiratory sensitization, and therefore these models may not afford reliable predictions for toxic compounds with other respiratory symptoms, such as pneumonia or rhinitis. Here, based on a diverse data set of mouse intraperitoneal respiratory toxicity characterized by multiple symptoms, a number of quantitative and qualitative predictions models with high reliability were developed by machine learning approaches. First, a four-tier dimension reduction strategy was employed to find an optimal set of 20 molecular descriptors for model building. Then, six machine learning approaches were used to develop the prediction models, including relevance vector machine (RVM), support vector machine (SVM), regularized random forest (RRF), extreme gradient boosting (XGBoost), naïve Bayes (NB), and linear discriminant analysis (LDA). Among all of the models, the SVM regression model shows the most accurate quantitative predictions for the test set (q = 0.707), and the XGBoost classification model achieves the most accurate qualitative predictions for the test set (MCC of 0.644, AUC of 0.893, and global accuracy of 82.62%). The application domains were analyzed, and all of the tested compounds fall within the application domain coverage. We also examined the structural features of the compounds and important fragments with large prediction errors. In conclusion, the SVM regression model and the XGBoost classification model can be employed as accurate prediction tools for respiratory toxicity.
作为一个危险的终点，呼吸道毒性可导致严重的健康不良影响甚至死亡。同时，它也是职业和环境保护领域一个常见且传统的问题。制药和化工行业迫切需要开发精确且便捷的计算工具，以便尽早评估化合物的呼吸道毒性。大多数已报道的理论模型是基于具有单一症状（如呼吸道致敏）的呼吸道毒性数据集开发的，因此这些模型可能无法对具有其他呼吸道症状（如肺炎或鼻炎）的有毒化合物提供可靠的预测。在此，基于一个具有多种症状特征的小鼠腹腔内呼吸道毒性的多样化数据集，通过机器学习方法开发了一些具有高可靠性的定量和定性预测模型。首先，采用四层降维策略来找到一组用于模型构建的最优的20个分子描述符。然后，使用六种机器学习方法来开发预测模型，包括相关向量机（RVM）、支持向量机（SVM）、正则化随机森林（RRF）、极端梯度提升（XGBoost）、朴素贝叶斯（NB）和线性判别分析（LDA）。在所有模型中，SVM回归模型对测试集显示出最准确的定量预测（q = 0.707），而XGBoost分类模型对测试集实现了最准确的定性预测（MCC为0.644，AUC为0.893，全局准确率为82.62%）。分析了应用域，所有测试化合物都在应用域覆盖范围内。我们还研究了化合物的结构特征以及具有较大预测误差的重要片段。总之，SVM回归模型和XGBoost分类模型可作为呼吸道毒性的精确预测工具。
