Prediction of Aquatic Toxicity Mode of Action Using Linear Discriminant and Random Forest Models

The
ability to determine the mode of action (MOA) for a diverse
group of chemicals is a critical part of ecological risk assessment
and chemical regulation. However, existing MOA assignment approaches
in ecotoxicology have been limited to a relatively few MOAs, have
high uncertainty, or rely on professional judgment. In this study,
machine based learning algorithms (linear discriminant analysis and
random forest) were used to develop models for assigning aquatic toxicity
MOA. These methods were selected since they have been shown to be
able to correlate diverse data sets and provide an indication of the
most important descriptors. A data set of MOA assignments for 924
chemicals was developed using a combination of high confidence assignments,
international consensus classifications, ASTER (ASessment Tools for
the Evaluation of Risk) predictions, and weight of evidence professional
judgment based an assessment of structure and literature information.
The overall data set was randomly divided into a training set (75%)
and a validation set (25%) and then used to develop linear discriminant
analysis (LDA) and random forest (RF) MOA assignment models. The LDA
and RF models had high internal concordance and specificity and were
able to produce overall prediction accuracies ranging from 84.5 to
87.7% for the validation set. These results demonstrate that computational
chemistry approaches can be used to determine the acute toxicity MOAs
across a large range of structures and mechanisms.