Fjodorova N, Novič M
a National Institute of Chemistry , Hajdrihova, Ljubljana , Slovenia.
SAR QSAR Environ Res. 2014;25(6):423-41. doi: 10.1080/1062936X.2014.898687. Epub 2014 Apr 9.
The primary goal of this study was to describe and compare the criteria used to assess carcinogenic activity. The statistically-based predictive quantitative structure-activity relationship (QSAR) models based on the counter propagation artificial neural network (CPANN) algorithm, and knowledge-based expert systems based on a decision tree structural alert (SA) approach (Toxtree application), were considered. The integration of the QSAR (CPANN models) and SAR (Toxtree SA application) approach contributed to the mechanistic understanding of the QSAR model considered. The mapping technique inherent to CPANN Kohonen enables us to relate the similarities or dissimilarities within a congeneric set of chemicals with particular SAs for carcinogenicity. The focus of our investigations was the similarities and dissimilarities of the features used in the QSAR and SAR methods. Due to the complexity of the carcinogenic endpoint, the integration of different approaches allows the models to be improved and provides a valuable technique for evaluating the safety of chemicals.
本研究的主要目标是描述和比较用于评估致癌活性的标准。考虑了基于反向传播人工神经网络（CPANN）算法的基于统计的预测性定量构效关系（QSAR）模型，以及基于决策树结构警报（SA）方法（Toxtree应用程序）的基于知识的专家系统。QSAR（CPANN模型）和SAR（Toxtree SA应用程序）方法的整合有助于对所考虑的QSAR模型进行机理理解。CPANN Kohonen固有的映射技术使我们能够将一组同类化学品中的相似性或差异与特定的致癌性SA联系起来。我们研究的重点是QSAR和SAR方法中使用的特征的异同。由于致癌终点的复杂性，不同方法的整合可以改进模型，并为评估化学品的安全性提供一种有价值的技术。
