Abstract: The present invention relates to the prediction of drug metabolizing enzymes (DME) inhibitors. Inhibition of DMEs leads to adverse drug-drug interaction, hence predicting inhibition of DMEs by determined molecules is critical for preventing drug toxicity. Inventors elaborated a protocol of integrated in silico protein structure-based and machine learning approach to predict inhibition of DMEs. In particular, the present invention relates to a method for training a model for predicting inhibition of DMEs, comprising a selection of a number of descriptors among an initial set comprising physicochemical descriptors and binding energies on at least one enzyme configuration, and a training of a classification model on a learning database of known inhibitors or non-inhibitors based on the selected descriptors as inputs. This approach successfully predicted inhibition of CYP2C9, CYP2D6, SULT1A1, SULT1A3 and UGT1 A1.