Structural Alerts and Random Forest Models in a Consensus Approach for Receptor Binding Molecular Initiating Events

A molecular initiating
event (MIE) is the gateway to an adverse
outcome pathway (AOP), a sequence of events ending in an adverse effect.
In silico predictions of MIEs are a vital tool in a modern, mechanism-focused
approach to chemical risk assessment. For 90 biological targets representing
important human MIEs, structural alert-based models have been constructed
with an automated procedure that uses Bayesian statistics to iteratively
select substructures. These models give impressive average performance
statistics (an average of 92% correct predictions across targets),
significantly improving on previous models. Random Forest models have
been constructed from physicochemical features for the same targets,
giving similarly impressive performance statistics (93% correct predictions).
A key difference between the models is interpretation of predictionsthe
structural alert models are transparent and easy to interpret, while
Random Forest models can only identify the most important physicochemical
features for making predictions. The two complementary models have
been combined in a consensus model, improving performance compared
to each individual model (94% correct predictions) and increasing
confidence in predictions. Variation in model performance has been
explained by calculating a modelability index (MODI), using Tanimoto
coefficient between Morgan fingerprints to identify nearest neighbor
chemicals. This work is an important step toward building confidence
in the use of in silico tools for assessment of toxicity.