3D-QSAR and Molecular Docking Studies of Novel GPR52 Agonists
Qingshan Gu, Jingxuan Hou, Hui Gao, Meiqi Shi, Ying Zhuang, Qingkun Wu, Lu Zheng · Journal of Computational Biophysics and Chemistry · 2023
To study the three-dimensional quantitative structure–activity relationship (3D-QSAR) of a series of GPR52 agonists, comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) were used to establish 3D-QSAR models. The cross-validation coefficient ([Formula: see text]), non-cross validation coefficient ([Formula: see text]) and standard error of estimate (SEE) of the constructed CoMFA model were 0.679, 0.981 and 0.058, respectively. While those for the CoMSIA model were 0.661, 0.846 and 0.110, respectively. The internal and external validation parameters showed that the constructed 3D-QSAR models had good prediction ability and significant statistical reliability. According to the information provided by 3D-QSAR analysis, new compounds were designed, and the molecular docking together with absorption, distribution, metabolism, exclusion, toxicity (ADMET) prediction results showed that the new compounds had good GPR52 agonistic activity.