Novel naïve Bayes classification models for predicting the carcinogenicity of chemicals.
Novel naïve Bayes classification models for predicting the carcinogenicity of chemicals.
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DOI：
10.1016/j.fct.2016.09.005
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发表时间：
2016-11
期刊：
影响因子：
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通讯作者：
Hui Zhang;Z. Cao;Meng Li;Yu-Zhi Li;Cheng Peng
中科院分区：
文献类型：
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作者：
Hui Zhang;Z. Cao;Meng Li;Yu-Zhi Li;Cheng Peng
致癌性预测已成为制药行业的一个重要问题。本研究的目的是建立一种新的预测模型的化学品的致癌性，通过使用朴素贝叶斯分类器。建立的模型通过内部5折交叉验证和外部测试集进行验证。朴素贝叶斯分类器对训练集和外部测试集的平均总体预测准确率分别为90 ± 0.8%和68 ± 1.9%。此外，五个简单的分子描述符（例如，AlogP、分子量（MW）、H供体数、Apol和Wiener），并获得了与致癌性相关的亚结构。因此，我们希望所建立的朴素贝叶斯预测模型可以应用于筛选这种潜在致癌性不良反应的早期分子;所确定的5种致癌物质的简单分子描述符和亚结构，将有助于更好地了解化学物质的致癌性，并进一步为药物化学家设计新的候选药物和先导化合物优化提供指导，最终降低药物开发后期的损耗率。
The carcinogenicity prediction has become a significant issue for the pharmaceutical industry. The purpose of this investigation was to develop a novel prediction model of carcinogenicity of chemicals by using a naïve Bayes classifier. The established model was validated by the internal 5-fold cross validation and external test set. The naïve Bayes classifier gave an average overall prediction accuracy of 90 ± 0.8% for the training set and 68 ± 1.9% for the external test set. Moreover, five simple molecular descriptors (e.g., AlogP, Molecular weight (MW), No. of H donors, Apol and Wiener) considered as important for the carcinogenicity of chemicals were identified, and some substructures related to the carcinogenicity were achieved. Thus, we hope the established naïve Bayes prediction model could be applied to filter early-stage molecules for this potential carcinogenicity adverse effect; and the identified five simple molecular descriptors and substructures of carcinogens would give a better understanding of the carcinogenicity of chemicals, and further provide guidance for medicinal chemists in the design of new candidate drugs and lead optimization, ultimately reducing the attrition rate in later stages of drug development.