Empirical Regioselectivity Models for Human Cytochromes P450 3A4, 2D6, and 2C9

Cytochromes P450 3A4, 2D6, and 2C9 metabolize a large fraction of drugs. Knowing where these enzymes
will preferentially oxidize a molecule, the regioselectivity, allows medicinal chemists to plan how best to
block its metabolism. We present QSAR-based regioselectivity models for these enzymes calibrated against
compiled literature data of drugs and drug-like compounds. These models are purely empirical and use only
the structures of the substrates, in contrast to those models that simulate a specific mechanism like hydrogen
radical abstraction, and/or use explicit models of active sites. Our most predictive models use three substructure
descriptors and two physical property descriptors. Descriptor importances from the random forest QSAR
method show that other factors than the immediate chemical environment and the accessibility of the hydrogen
affect regioselectivity in all three isoforms. The cross-validated predictions of the models are compared to
predictions from our earlier mechanistic model (Singh et al. J. Med. Chem. 2003, 46, 1330−1336) and
predictions from MetaSite (Cruciani et al. J. Med. Chem. 2005, 48, 6970−6979).