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.

Abstract: Pituitary adenylate cyclase-activating polypeptide (PACAP) and its receptor system can be manipulated positively or negatively to regulate mitosis in neuronal precursor cells. The ligand/receptor system involves PACAP, PACAP receptor, PAC1, and related antagonists. The methods of regulation of the present invention may model be used to define cell cycle regulation in the developing neurons. The present invention may also be used to control or cure diseases related to or caused by damage to or destruction of neuronal cells.

Abstract: Pituitary adenylate cyclase-activating polypeptide (PACAP) and its receptor system can be manipulated positively or negatively to regulate mitosis in neuronal precursor cells. The ligand/receptor system involves PACAP, PACAP receptor, PAC,, and related antagonists. The methods of regulation of the present invention may model be used to define cell cycle regulation in the developing neurons. The present invention may also be used to control or cure diseases related to or caused by damage to or destruction of neuronal cells.