Deep Learning to Predict the Formation of Quinone Species in Drug Metabolism

Many adverse drug reactions are thought
to be caused by electrophilically
reactive drug metabolites that conjugate to nucleophilic sites within
DNA and proteins, causing cancer or toxic immune responses. Quinone
species, including quinone-imines, quinone-methides, and imine-methides,
are electrophilic Michael acceptors that are often highly reactive
and comprise over 40% of all known reactive metabolites. Quinone metabolites
are created by cytochromes P450 and peroxidases. For example, cytochromes
P450 oxidize acetaminophen to N-acetyl-p-benzoquinone imine, which is electrophilically reactive and covalently
binds to nucleophilic sites within proteins. This reactive quinone
metabolite elicits a toxic immune response when acetaminophen exceeds
a safe dose. Using a deep learning approach, this study reports the
first published method for predicting quinone formation: the formation
of a quinone species by metabolic oxidation. We model both one- and
two-step quinone formation, enabling accurate quinone formation predictions
in nonobvious cases. We predict atom pairs that form quinones with
an AUC accuracy of 97.6%, and we identify molecules that form quinones
with 88.2% AUC. By modeling the formation of quinones, one of the
most common types of reactive metabolites, our method provides a rapid
screening tool for a key drug toxicity risk. The XenoSite quinone
formation model is available at http://swami.wustl.edu/xenosite/p/quinone.