ABSTRACT: Carcinogenicity is an important toxicological endpoint that poses high
concern to drug discovery. In this study, we developed a method to extract structural alerts
(SAs) and modulating factors of carcinogens on the basis of statistical analyses. First, the
Gaston algorithm, a frequent subgraph mining method, was used to detect substructures
that occurred at least six times. Then, a molecular fragments tree was built and pruned to
select high-quality SAs. The p-value of the parent node in the tree and that of its children
nodes were compared, and the nodes that had a higher statistical significance in binomial
tests were retained. Finally, modulating factors that suppressed the toxic effects of SAs were
extracted by three self-defining rules. The accuracy of the 77 SAs plus four SA/modulating
factor pairs model for the training set, and the test set was 0.70 and 0.65, respectively. Our model has higher predictive ability
than Benigni’s model, especially in the test set. The results highlight that this method is preferable in terms of prediction
accuracy, and the selected SAs are useful for prediction as well as interpretation. Moreover, our method is convenient to users in
that it can extract SAs from a database using an automated and unbiased manner that does not rely on a priori knowledge of
mechanism of action.