2 research outputs found
Development of In Silico Models for Predicting Potential Time-Dependent Inhibitors of Cytochrome P450 3A4
Cytochrome P450 3A4 (CYP3A4) is one of the major drug
metabolizing
enzymes in the human body and metabolizes ∼30–50% of
clinically used drugs. Inhibition of CYP3A4 must always be considered
in the development of new drugs. Time-dependent inhibition (TDI) is
an important P450 inhibition type that could cause undesired drug–drug
interactions. Therefore, identification of CYP3A4 TDI by a rapid convenient
way is of great importance to any new drug discovery effort. Here,
we report the development of in silico classification models for prediction
of potential CYP3A4 time-dependent inhibitors. On the basis of the
CYP3A4 TDI data set that we manually collected from literature and
databases, both conventional machine learning and deep learning models
were constructed. The comparisons of different sampling strategies,
molecular representations, and machine-learning algorithms showed
the benefits of a balanced data set and the deep-learning model featured
by GraphConv. The generalization ability of the best model was tested
by screening an external data set, and the prediction results were
validated by biological experiments. In addition, several structural
alerts that are relevant to CYP3A4 time-dependent inhibitors were
identified via information gain and frequency analysis. We anticipate
that our effort would be useful for identification of potential CYP3A4
time-dependent inhibitors in drug discovery and design
Development of In Silico Models for Predicting Potential Time-Dependent Inhibitors of Cytochrome P450 3A4
Cytochrome P450 3A4 (CYP3A4) is one of the major drug
metabolizing
enzymes in the human body and metabolizes ∼30–50% of
clinically used drugs. Inhibition of CYP3A4 must always be considered
in the development of new drugs. Time-dependent inhibition (TDI) is
an important P450 inhibition type that could cause undesired drug–drug
interactions. Therefore, identification of CYP3A4 TDI by a rapid convenient
way is of great importance to any new drug discovery effort. Here,
we report the development of in silico classification models for prediction
of potential CYP3A4 time-dependent inhibitors. On the basis of the
CYP3A4 TDI data set that we manually collected from literature and
databases, both conventional machine learning and deep learning models
were constructed. The comparisons of different sampling strategies,
molecular representations, and machine-learning algorithms showed
the benefits of a balanced data set and the deep-learning model featured
by GraphConv. The generalization ability of the best model was tested
by screening an external data set, and the prediction results were
validated by biological experiments. In addition, several structural
alerts that are relevant to CYP3A4 time-dependent inhibitors were
identified via information gain and frequency analysis. We anticipate
that our effort would be useful for identification of potential CYP3A4
time-dependent inhibitors in drug discovery and design