Valid and predictive models for classifying Ames mutagenicity have been developed using conformal prediction. The models are Random Forest models using signature molecular descriptors. The investigation indicates, on excluding not-strongly mutagenic compounds (class B), that the validity for mutagenic compounds is increased for the predictions based on both public and the Division of Genetics and Mutagenesis, National Institute of Health Sciences of Japan (DGM/NIHS) data while less so when using only the latter data source. The former models only result in valid predictions for the majority, non-mutagenic, class whereas the latter models are valid for both classes, i.e. mutagenic and non-mutagenic compounds. These results demonstrate the importance of data consistency manifested through the superior predictive quality and validity of the models based only on DGM/NIHS generated data compared to a combination of this data with public data sources.

中文翻译：

---

在Ames / QSAR国际挑战项目中使用保形预测来预测Ames致突变性。

使用保形预测已开发出有效的和预测性的模型，用于对Ames致突变性进行分类。该模型是使用签名分子描述符的随机森林模型。该调查表明，除了排除突变程度不高的化合物（B类）以外，根据公共卫生和日本国立卫生科学研究院遗传与诱变司（DGM / NIHS）进行的预测，诱变化合物的有效性有所提高）数据，而仅使用后者的数据源则更少。前一种模型仅能对大多数非诱变类别进行有效预测，而后一种模型对两种类别均有效，即诱变和非诱变化合物。