Multiple Instance Learning Improves Ames Mutagenicity Prediction for Problematic Molecular Species

The
prediction of Ames mutagenicity continues to be a concern in
both regulatory and pharmacological toxicology. Traditional quantitative
structure–activity relationship (QSAR) models of mutagenicity
make predictions based on molecular descriptors calculated on a chemical
data set used in their training. However, it is known that molecules
such as aromatic amines can be non-mutagenic themselves but metabolically
activated by S9 rodent liver enzyme in Ames tests forming molecules
such as iminoquinones or amine substituents that better stabilize
mutagenic nitrenium ions in known pathways of mutagenicity. Modern in silico modeling methods can implicitly model these metabolites
through consideration of the structural elements relevant to their
formation but do not include explicit modeling of these metabolites’
potential activity. These metabolites do not have a known individual
mutagenicity label and, in their current state, cannot be fitted into
a traditional QSAR model. Multiple instance learning (MIL) however
can be applied to a group of metabolites and their parent under a
single mutagenicity label. Here we trained MIL models on Ames data,
first with an aromatic amines data set (n = 457),
a class known to require metabolic activation, and subsequently on
a larger data set (n = 6505) incorporating multiple
molecular species. MIL was shown to be able to predict Ames mutagenicity
with performance in line with previously established models (balanced
accuracy = 0.778), suggesting its potential utility in Ames prediction
applications. Furthermore, the MIL model predicted well on identified
hard-to-predict molecule groups relative to the models in which these
molecule groups were identified. These results are presumably due
to the increased consideration of the metabolic contribution to the
mutagenic outcome. Further exploration of MIL as a supplement to existing
models could aid in the prediction of chemicals where implicit modeling
of metabolites cannot fully grasp their characteristics. This paper
demonstrates the potential of an MIL approach to modeling Ames tests
with S9 and is particularly relevant to metabolically activated xenobiotic
mutagens.