In Silico Estimation of Chemical Carcinogenicity with Binary and Ternary Classification Methods
In Silico Estimation of Chemical Carcinogenicity with Binary and Ternary Classification Methods
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用二元和三元分类方法计算机模拟化学致癌性估计
DOI：
10.1002/minf.201400127
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发表时间：
2015-04-01
影响因子：
3.6
通讯作者：
Tang, Yun
中科院分区：
文献类型：
--
作者：
Li, Xiao;Du, Zheng;Tang, Yun
致癌性是化学物质对人类健康最受关注的性质之一，因此及早识别化学物质的致癌性具有重要意义。在本研究中，从致癌效力数据库(CPDB)中收集了829种具有大鼠致癌活性的化合物。用6种类型的指纹代表分子，通过5种机器学习方法建立了30个二值和三值分类模型来预测化学致癌性。通过包含来自ISSCAN数据库的87种化学物质的外部验证集对模型进行评估。由MACCS key和KNN算法生成的最优二元模型的预测准确率为83.91 %，而由MACCS key和KNN算法生成的最佳三元模型的预测准确率为80.46 %。此外，利用最优二元和三元分类模型对含有2251种化合物的烟草烟雾组分的致癌性进行了评价。二分类模型预测981种化合物为致癌物质，三元分类模型预测110种化合物为强致癌物质，807种化合物为弱致癌物质。结果表明，我们的模型将有助于预测化学致癌性。
Carcinogenicity is one of the most concerned properties of chemicals to human health, thus it is important to identify chemical carcinogenicity as early as possible. In this study, 829 diverse compounds with rat carcinogenicity were collected from Carcinogenic Potency Database (CPDB). Using six types of fingerprints to represent the molecules, 30 binary and ternary classification models were generated to predict chemical carcinogenicity by five machine learning methods. The models were evaluated by an external validation set containing 87 chemicals from ISSCAN database. The best binary model was developed by MACCS keys and kNN algorithm with predictive accuracy at 83.91 %, while the best ternary model was also generated by MACCS keys and kNN algorithm with overall accuracy at 80.46 %. Furthermore, the best binary and ternary classification models were used to estimate carcinogenicity of tobacco smoke components containing 2251 compounds. 981 ones were predicted as carcinogens by binary classification model, while 110 compounds were predicted as strong carcinogens and 807 ones as weak carcinogens by ternary classification model. The results indicated that our models would be helpful for prediction of chemical carcinogenicity.