Machine Learning Enables Accurate Prediction of Quinone Formation during Drug Metabolism

Metabolism helps in the elimination
of drugs from the human body
by making them more hydrophilic. Sometimes, drugs can be bioactivated
to highly reactive metabolites or intermediates during metabolism.
These reactive metabolites are often responsible for the toxicities
associated with the drugs. Identification of reactive metabolites
of drug candidates can be very helpful in the initial stages of drug
discovery. Quinones are soft electrophiles that are generated as reactive
intermediates during metabolism. Quinones make up more than 40% of
the reactive metabolites. In this work, a reliable data set of 510
molecules was used to develop machine learning and deep learning-based
predictive models to predict the formation of quinone-type metabolites.
For representing molecules, two-dimensional (2D) descriptors, PubChem
fingerprints, electro-topological state (E-state) fingerprints, and
metabolic reactivity-based descriptors were used. Developed models
were compared to the existing Xenosite web server using the untouched
test set of 102 molecules. The best model achieved an accuracy of
86.27%, while the Xenosite server could achieve an accuracy of only
52.94% on the test set. Descriptor analysis revealed that the presence
of greater numbers of polar moieties in a molecule can prevent the
formation of quinone-type metabolites. In addition, the presence of
a nitrogen atom in an aromatic ring and the presence of metabolophores
V51, V52, and V53 (SMARTCyp descriptors) decrease the probability
of quinone formation. Finally, a tool based on the best machine learning
models was developed, which is accessible at http://14.139.57.41/quinonepred/.