Augmenting Expert Knowledge-Based Toxicity Alerts by Statistically Mined Molecular Fragments

Structural alerts are molecular substructures assumed
to be associated
with molecular initiating events in various toxic effects and an integral
part of in silico toxicology. However, alerts derived
using the knowledge of human experts often suffer from a lack of predictivity,
specificity, and satisfactory coverage. In this work, we present a
method to build hybrid QSAR models by combining expert knowledge-based
alerts and statistically mined molecular fragments. Our objective
was to find out if the combination is better than the individual systems.
Lasso regularization-based variable selection was applied on combined
sets of knowledge-based alerts and molecular fragments, but the variable
elimination was only allowed to happen on the molecular fragments.
We tested the concept on three toxicity end points, i.e., skin sensitization,
acute Daphnia toxicity, and Ames mutagenicity, which
covered both classification and regression problems. Results showed
the predictive performance of such hybrid models is, indeed, better
than the models based solely on expert alerts or statistically mined
fragments alone. The method also enables the discovery of activating
and mitigating/deactivating features for toxicity alerts and the identification
of new alerts, thereby reducing false positive and false negative
outcomes commonly associated with generic alerts and alerts with poor
coverage, respectively.