QSAR Model based on Molecular Descriptors Built to Predict the CB1 Binding Affinity of JWH Cannabinoids

Abstract

The aim of this study was to determine the relationship between the CB1 binding affinity and the relevant molecular descriptors characterizing JWH synthetic cannabinoids, based on the combined use of molecular modeling and chemometric techniques, i.e. Principal Component Analysis (PCA) and Multiple Linear Regression (MLR). Molecular modeling has been used to evaluate the minimum energy of each JWH compound and to calculate their stereoelectronic properties, i.e. their molecular descriptors. PCA has been used to select the molecular descriptors, which contain useful information with respect to CB1 binding affinity. The MLR method has been used to supply the best predictive regression QSAR model. The analysis of the molecular descriptors found to be the most relevant for the QSAR model is presented. The main advantage of the presented QSAR model is that it can be used to predict the binding affinity of ligands prior to their synthesis.