Prediction of Cytochrome P450 Inhibition Using a Deep Learning Approach and Substructure Pattern Recognition

Cytochrome P450 (CYP) is a family
of enzymes that are responsible
for about 75% of all metabolic reactions. Among them, CYP1A2, CYP2C9,
CYP2C19, CYP2D6, and CYP3A4 participate in the metabolism of most
drugs and mediate many adverse drug reactions. Therefore, it is necessary
to estimate the chemical inhibition of Cytochrome P450 enzymes in
drug discovery and the food industry. In the past few decades, many
computational models have been reported, and some provided good performance.
However, there are still several issues that should be resolved for
these models, such as single isoform, models with unbalanced performance,
lack of structural characteristics analysis, and poor availability.
In the present study, the deep learning models based on python using
the Keras framework and TensorFlow were developed for the chemical
inhibition of each CYP isoform. These models were established based
on a large data set containing 85715 compounds extracted from the
PubChem bioassay database. On external validation, the models provided
good AUC values with 0.97, 0.94, 0.94, 0.96, and 0.94 for CYP1A2,
CYP2C9, CYP2C19, CYP2D6, and CYP3A4, respectively. The models can
be freely accessed on the Web server named CYPi-DNNpredictor (cypi.sapredictor.cn), and
the codes for the model were made open source in the Supporting Information.
In addition, we also analyzed the structural characteristics of chemicals
with CYP450 inhibition and detected the structural alerts (SAs), which
should be responsible for the inhibition. The SAs were also made available
online, named CYPi-SAdetector (cypisa.sapredictor.cn). The models can be used as a powerful
tool for the prediction of CYP450 inhibitors, and the SAs should provide
useful information for the mechanisms of Cytochrome P450 inhibition.