Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network

Drug
toxicity is frequently caused by electrophilic reactive metabolites
that covalently bind to proteins. Epoxides comprise a large class
of three-membered cyclic ethers. These molecules are electrophilic
and typically highly reactive due to ring tension and polarized carbon–oxygen
bonds. Epoxides are metabolites often formed by cytochromes P450 acting
on aromatic or double bonds. The specific location on a molecule that
undergoes epoxidation is its site of epoxidation (SOE). Identifying
a molecule’s SOE can aid in interpreting adverse events related
to reactive metabolites and direct modification to prevent epoxidation
for safer drugs. This study utilized a database of 702 epoxidation
reactions to build a model that accurately predicted sites of epoxidation.
The foundation for this model was an algorithm originally designed
to model sites of cytochromes P450 metabolism (called XenoSite) that
was recently applied to model the intrinsic reactivity of diverse
molecules with glutathione. This modeling algorithm systematically
and quantitatively summarizes the knowledge from hundreds of epoxidation
reactions with a deep convolution network. This network makes predictions
at both an atom and molecule level. The final epoxidation model constructed
with this approach identified SOEs with 94.9% area under the curve
(AUC) performance and separated epoxidized and non-epoxidized molecules
with 79.3% AUC. Moreover, within epoxidized molecules, the model separated
aromatic or double bond SOEs from all other aromatic or double bonds
with AUCs of 92.5% and 95.1%, respectively. Finally, the model separated
SOEs from sites of sp2 hydroxylation with 83.2% AUC. Our
model is the first of its kind and may be useful for the development
of safer drugs. The epoxidation model is available at http://swami.wustl.edu/xenosite.