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Figure 1 The architecture of the integrated mutagenicity model: cascading filters.

Performance of the default, prebuilt TopKat Ames mutagenicity model on Set A (orange) and Set B (red) for MW < 250 g/mol (solid line) or for 250 < MW < 500 g/mol (dashed).

The left plot shows the performance of the default TopKat Ames mutagenicity model on Sets C (orange), D (red), E (green), and F (black) as well as the performance for particular substructures in all sets (blue): nitroaromatics (purple), aryl-amines (brown), or polyaromatic (gray).

Our work shows that highly accurate predictive mutagenicity models can be built using machine learning methods based on chemical descriptors and trained using this set, and these models provide a complement to toxicophores based methods.

When using the growth inhibition and mutagenicity models in Bioclipse Decision Support, the substructure that contributed the most to the prediction of a given compound's bioactivity is highlighted.

The queries used are provided as Additional file 2. The TOPKAT Ames mutagenicity classification model in the Accelrys ADMET component collection in Pipeline Pilot was used for commercial model predictions.

Table 2 Confusion matrix of mutagenicity integrated model on the training set (3367 chemical compounds).

Table 1 Confusion matrix of the mutagenicity integrated model on the test set (837 chemical compounds).

The pre-built mutagenicity prediction model available to us in TopKat [80] was explored as a possible prediction method.

(16) When the mutagenicity of a model pyridyloxobutylating agent, 4- acetoxymethylnitrosamino -1- 3-pyridyl -1-butanone (NNKOAc), was investigated in CHO cells, it induced point mutations primarily at AT base pairs, suggesting that O-POB-dT might be mutagenic.

There is a long history of modeling mutagenicity on chemicals expected to be encountered from environmental and food exposure [9, 33 37].