Estimation of Carcinogenicity Using Molecular Fragments Tree

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.