Suppr超能文献

用于鉴定血管内皮生长因子受体-2抑制剂的二维相似性搜索、药效团和分子对接技术的组合

A combination of 2D similarity search, pharmacophore, and molecular docking techniques for the identification of vascular endothelial growth factor receptor-2 inhibitors.

作者信息

Ai Guanhua, Tian Caiping, Deng Dawei, Fida Guissi, Chen Haiyan, Ma Yuxiang, Ding Li, Gu Yueqing

机构信息

State Key Laboratory of Natural Medicines, School of Life Science and Technology, Department of Biomedical Engineering, China Pharmaceutical University, Nanjing, China.

出版信息

Anticancer Drugs. 2015 Apr;26(4):399-409. doi: 10.1097/CAD.0000000000000199.

Abstract

The human vascular endothelial growth factor receptor-2 (VEGFR-2) has been an attractive target for the inhibition of angiogenesis. In the current study, we used a hybrid protocol of virtual screening methods to retrieve new VEGFR-2 inhibitors from the Zinc-Specs Database (441 574 compounds). The hybrid protocol included the initial screening of candidates by comparing the 2D similarity to five reported top active inhibitors of 13 VEGFR-2 X-ray crystallography structures, followed by the pharmacophore modeling of virtual screening on the basis of receptor-ligand interactions and further narrowing by LibDOCK to obtain the final hits. Two compounds (AN-919/41439526 and AK-968/40939851) with a high libscore were selected as the final hits for a subsequent cell cytotoxicity study. The two compounds screened exerted significant inhibitory effects on the proliferation of cancer cells (U87 and MCF-7). The results indicated that the hybrid procedure is an effective approach for screening specific receptor inhibitors.

摘要

人血管内皮生长因子受体-2(VEGFR-2)一直是抑制血管生成的一个有吸引力的靶点。在当前研究中,我们使用了虚拟筛选方法的混合方案,从锌规格数据库(441574种化合物)中检索新的VEGFR-2抑制剂。该混合方案包括通过将二维相似性与13种VEGFR-2 X射线晶体学结构的5种已报道的顶级活性抑制剂进行比较来初步筛选候选物,然后基于受体-配体相互作用进行虚拟筛选的药效团建模,并通过LibDOCK进一步筛选以获得最终命中物。选择两种具有高libscore的化合物(AN-919/41439526和AK-968/40939851)作为后续细胞毒性研究的最终命中物。筛选出的这两种化合物对癌细胞(U87和MCF-7)的增殖具有显著抑制作用。结果表明,该混合程序是筛选特异性受体抑制剂的有效方法。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验