Predictive QSAR modeling study on berberine derivatives with hypolipidemic activity
Berberine (BBR), isolated from a Chinese herb, is identified as a new cholesterol-lowering small molecule, and hundreds of berberine derivatives have been obtained for optimization of their hypolipidemic activities in recent years. However, so far there is no available quantitative structure–activity relationship (QSAR) model used for the development of novel BBR analogues with hypolipidemic activities, mainly due to lack of lipid-lowering molecular mechanisms and target identification of BBR. In this paper, the tactics using ligand efficiency indice instead of pIC50 as the activity could be adopted for the development of BBR QSAR models. A series of 59 BBR derivatives with hypolipidemic activities have been studied and split randomly into three sets of training and test sets. Statistical quality of most building models shows obviously robust. Best calculated model that employs LLE indice as the activity (Model 6) has the following statistical parameters: for training set R2 = .984, Q2 = 0.981, RMSE = 0.1160, and for test set R2 = .989, RMSE = 0.0067. This model would be used for the development of novel BBR analogues with lipid-lowering activities as a hit discovery tool.