In-silico models were generated to predict the extent of inhibition of cytochrome P450 isoenzymes using a set of relatively interpretable descriptors in conjunction with partial least squares (PLS) and regression trees (RT). The former was chosen due to the conservative nature of the resultant models built and the latter to more effectively account for any non-linearity between dependent and independent variables. All models are statistically significant and agree with the known SAR and they could be used as a guide to P450 liability through a classification based on the continuous pIC50 prediction given by the model. A compound is classified as having either a high or low P450 liability if the predicted pIC(50) is at least one root mean square error (RMSE) from the high/low pIC(50) cut-off of 5. If predicted within an RMSE of the cut-off we cannot be confident a compound will be experimentally low or high so an indeterminate classification is given. Hybrid models using bulk descriptors and fragmental descriptors do significantly better in modeling CYP450 inhibition, than bulk property QSAR descriptors alone.