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Predictive QSAR modeling study on berberine derivatives with hypolipidemic activity
Author(s) -
Yu Pan,
Li Dongdong,
Ni Junjun,
Zhao Linguo,
Ding Gang,
Wang Zhenzhong,
Xiao Wei
Publication year - 2018
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/cbdd.13150
Subject(s) - berberine , quantitative structure–activity relationship , chemistry , test set , training set , molecular descriptor , medicinal herbs , computational biology , traditional medicine , stereochemistry , machine learning , computer science , biochemistry , artificial intelligence , medicine , biology
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 pIC 50 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 R 2 = .984, Q 2 = 0.981, RMSE = 0.1160, and for test set R 2 = .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.