Prediction of Cytochrome P450 Substrates Using the Explainable Multitask Deep Learning Models

Cytochromes P450 (P450s or CYPs) are the most important
phase I
metabolic enzymes in the human body and are responsible for metabolizing
∼75% of the clinically used drugs. P450-mediated metabolism
is also closely associated with the formation of toxic metabolites
and drug–drug interactions. Therefore, it is of high importance
to predict if a compound is the substrate of a given P450 in the early
stage of drug development. In this study, we built the multitask learning
models to simultaneously predict the substrates of five major drug-metabolizing
P450 enzymes, namely, CYP3A4, 2C9, 2C19, 2D6, and 1A2, based on the
collected substrate data sets. Compared to the single-task model and
conventional machine learning models, the multitask fingerprints and
graph neural networks model achieved superior performance with the
average AUC values of 90.8% on the test set. Notably, the multitask
model demonstrated its good performance on the small amount of substrate
data sets such as CYP1A2, 2C9, and 2C19. In addition, the Shapley
additive explanation and the attention mechanism were used to reveal
specific substructures associated with P450 substrates, which were
further confirmed and complemented by the substructure mining tool
and the literature.