AI and SAR approaches for predicting chemical carcinogenicity: Survey and status report
AI and SAR approaches for predicting chemical carcinogenicity: Survey and status report
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DOI：
10.1080/10629360290002055
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发表时间：
2002-03-01
影响因子：
3
通讯作者：
Benigni, R
中科院分区：
文献类型：
--
作者：
Richard, AM;Benigni, R
各种各样的人工智能（AI）和构效关系（SAR）方法已被应用于解决预测啮齿动物化学致癌性的一般问题。考虑到与该终点相关的化学结构和机制的多样性，这些方法的共同挑战是准确描述代表不同生物和化学机制领域的活性化学物质的类别，并在这些类别中确定负责调节活性的结构特征和特性。在下面的讨论中，我们对已应用于啮齿动物致癌性预测的人工智能和SAR方法进行了调查，并在美国国家癌症研究所/国家毒理学计划赞助的两次有组织的预测练习（PTE-1和PTE-2）的结果背景下对这些方法进行了一般性讨论。参与这些练习的大多数模型都成功地识别了活性致癌物的主要结构警报类别，但未能对这些类别中的活性更微妙的修饰物进行建模。此外，将基于机制的推理或生物数据与结构信息结合起来的方法优于仅限于结构信息的模型。最后，介绍了最近的一些致癌性建模工作，说明了在解决致癌性预测问题的某些方面取得的进展。第一个例子是用于预测芳香胺致癌效力的 QSAR 模型，表明在具有代表性的致癌物类别中取得成功是可能的。从第二个示例（新开发的用于预测药品致癌性的 FDA/OTR MultiCASE 模型）中，我们得出结论，训练集中化学物质的生物活性和性质的定义是派生模型的预测成功和特异性/敏感性特征的重要决定因素。
A wide variety of artificial intelligence (AI) and structure-activity relationship (SAR) approaches have been applied to tackling the general problem of predicting rodent chemical carcinogenicity. Given the diversity of chemical structures and mechanisms relative to this endpoint, the shared challenge of these approaches is to accurately delineate classes of active chemicals representing distinct biological and chemical mechanism domains, and within those classes determine the structural features and proper-ties responsible for modulating activity. In the following discussion, we present a survey of AI and SAR approaches that have been applied to the prediction of rodent carcinogenicity, and discuss these in general terms and in the context of the results of two organized prediction exercises (PTE-1 and PTE-2) sponsored by the US National Cancer Institute/National Toxicology Program. Most models participating in these exercises were successful in identifying major structural-alerting classes of active carcinogens, but failed in modeling the more subtle modifiers to activity within those classes. In addition, methods that incorporated mechanism-based reasoning or biological data along with structural information outperformed models limited to structural information exclusively. Finally, a few recent carcinogenicity-modeling efforts are presented illustrating progress in tackling some aspects of the carcinogenicity prediction problem. The first example, a QSAR model for predicting carcinogenic potency of aromatic amines, illustrates that success is possible within well-represented classes of carcinogens. From the second example, a newly developed FDA/OTR MultiCASE model for predicting the carcinogenicity of pharmaceuticals, we conclude that the definitions of biological activity and nature of chemicals in the training set are important determinants of the predictive success and specificity/sensitivity characteristics of a derived model.