Cytochrome P450 3A4 (CYP3A4) is one of the major drug-metabolizing
enzymes in the human body and is responsible for the metabolism of
∼50% of clinically used drugs. Therefore, the identification
of the compound’s sites of metabolism (SOMs) mediated by CYP3A4
is of utmost importance in the early stage of drug discovery and development.
Herein, docking-based approaches incorporating geometric features
were used for SOMs prediction of CYP3A4 substrates. The cross-docking
poses of a relatively large data set containing 474 substrates were
analyzed in depth, and a widely observed geometric pattern called
the close proximity of SOMs was derived from the poses. On the basis
of the close proximity, several structure-based models have been constructed,
which demonstrated better performance than those structure-based models
using the criterion of Fe-SOM distance. For further improving the
prediction performance, the structure-based models were also combined
with the well-known ligand-based model SMARTCyp. One combined model
exhibited good performance on the SOMs prediction of an external substrate
set containing kinase inhibitors, PROTACs, approved drugs, and some
lead compounds.
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