Fragment-based strategy for structural optimization in combination with 3D-QSAR

被引:13
|
作者
Yuan, Haoliang [1 ]
Tai, Wenting [1 ]
Hu, Shihe [1 ]
Liu, Haichun [1 ]
Zhang, Yanmin [1 ]
Yao, Sihui [1 ]
Ran, Ting [1 ]
Lu, Shuai [1 ]
Ke, Zhipeng [1 ]
Xiong, Xiao [1 ]
Xu, Jinxing [1 ]
Chen, Yadong [1 ]
Lu, Tao [1 ,2 ]
机构
[1] China Pharmaceut Univ, Sch Sci, Lab Mol Design & Drug Discovery, Nanjing 210009, Jiangsu, Peoples R China
[2] China Pharmaceut Univ, State Key Lab Nat Med, Nanjing 210009, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Fragment-based drug design; 3D-QSAR; c-Met; Lead discovery; Structural optimization; C-MET INHIBITORS; DE-NOVO DESIGN; DRUG DISCOVERY; KINASE INHIBITOR; LEAD DISCOVERY; COMPUTATIONAL TECHNIQUES; LIGAND DESIGN; BINDING; IDENTIFICATION; MECHANISMS;
D O I
10.1007/s10822-013-9687-x
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Fragment-based drug design has emerged as an important methodology for lead discovery and drug design. Different with other studies focused on fragment library design and active fragment identification, a fragment-based strategy was developed in combination with three-dimensional quantitative structure-activity relationship (3D-QSAR) for structural optimization in this study. Based on a validated scaffold or fragment hit, a series of structural optimization was conducted to convert it to lead compounds, including 3D-QSAR modelling, active site analysis, fragment-based structural optimization and evaluation of new molecules. 3D-QSAR models and active site analysis provided sufficient information for confirming the SAR and pharmacophoric features for fragments. This strategy was evaluated through the structural optimization on a c-Met inhibitor scaffold 5H-benzo[4,5]cyclohepta[1,2-b]pyridin-5-one, which resulted in an c-Met inhibitor with high inhibitory activity. Our study suggested the effectiveness of this fragment-based strategy and the druggability of our newly explored active region. The reliability of this strategy indicated it could also be applied to facilitate lead optimization of other targets.
引用
收藏
页码:897 / 915
页数:19
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