Quantitative Structure Activity Relationship Studies on a Novel Indolediones as Long Chain Fatty Acid Elongase 6 (ELOVL6) Inhibitors
Junxia Zheng, Zhiwei Wu, Mibei Dai, Zhihui Xu, Xiaomei Li, Shanshan Zhu, Chuyun Lin, Peijian Hu, Luo Zhang, Huarong Huang, Suqing Zhao, Kun Zhang and Pinghua Sun
Pages 422-429 (8)
A series of indoledione derivatives displaying potent activities against ELOVL6 were selected to establish three-dimensional quantitative structure-activity relationships (3D-QSAR) using CoMFA and CoMSIA methods. A training set of 30 active compounds was used to develop the models while a test set of 8 compounds was used for the external validation. The CoMFA analysis predicted a q2 value of 0.817 and an r2 value of 0.990. The best CoMSIA model, based on a combination of steric, electrostatic and hydrophobic effects, predicted a q2 value of 0.760 and an r2 value of 0.959. These models were graphically interpreted using CoMFA and CoMSIA contour plots which provided insight into the structural requirements for increasing the activity of a compound. The results obtained from this study provide a solid basis for future rational design of more active ELOVL6 inhibitors.
QSAR, CoMFA, CoMSIA, Indolediones, ELOVL6, Diabetes, Fatty Acid, Inhibitors, Converting Enzyme, Elongase 6, stearoyl-CoA, lipogenic tissues
Guangdong Province Key Laboratory of Pharmacodynamic Constituents of TCM and New Drugs Research, College of Pharmacy, Jinan University, Guangzhou 510632, P. R. China.