首页> 外文期刊>Journal of Chemometrics >Grid potential analysis, virtual screening studies and ADME/T profiling on Narylsulfonylindoles as anti-HIV-1 agents
【24h】

Grid potential analysis, virtual screening studies and ADME/T profiling on Narylsulfonylindoles as anti-HIV-1 agents

机译:网格潜力分析,虚拟筛选研究和作为抗HIV-1药物的Narylsulfonylindoles的ADME / T分析

获取原文
获取原文并翻译 | 示例
           

摘要

A grid potential analysis employing a novel approach of 3D quantitative structure-activity relationships (QSAR) as AutoGPA module in MOE2009.10 was performed on a dataset of 42 compounds of N-arylsulfonylindoles as anti-HIV-1 agents. The uniqueness of AutoGPA module is that it automatically builds the 3D-QSAR model on the pharmacophorebased molecular alignment. The AutoGPA-based 3D-QSAR model obtained in the present study gave the cross-validated Q~2 value of 0.588, r_(pred)~2 value of 0.701, r_m~2 statistics of 0.732 and Fisher value of 94.264. The results of 3D-QSAR analysis indicated that hydrophobic groups at R1 and R2 positions and electron releasing groups at R3 position are favourable for good activity. To find similar analogues, virtual screening on ZINC database was carried out using generated AutoGPA-based 3D-QSAR model and showed good prediction. In addition to those mentioned earlier, in-silico ADME absorption, distribution, metabolism and excretion profiling and toxicity risk assessment test was performed, and results showed that majority of compounds from current dataset and newly virtually screened hits generated were within their standard limit. Supporting information may be found in the online version of this paper
机译:在MOE2009.10中使用3D定量结构-活性关系(QSAR)作为AutoGPA模块的新颖方法,对42种N-芳基磺酰吲哚类化合物作为抗HIV-1试剂进行了数据分析。 AutoGPA模块的独特之处在于,它可以在基于药效团的分子比对中自动构建3D-QSAR模型。本研究获得的基于AutoGPA的3D-QSAR模型的交叉验证Q〜2值为0.588,r_(pred)〜2值为0.701,r_m〜2统计值为0.732,Fisher值为94.264。 3D-QSAR分析的结果表明,R1和R2位置的疏水基团和R3位置的电子释放基团有利于良好的活性。为了找到类似的类似物,使用生成的基于AutoGPA的3D-QSAR模型对ZINC数据库进行了虚拟筛选,并显示出良好的预测效果。除了前面提到的那些,还进行了硅内ADME吸收,分布,代谢和排泄分析以及毒性风险评估测试,结果表明,来自当前数据集和新近进行虚拟筛选的命中的大多数化合物均在其标准范围内。支持信息可以在本文的在线版本中找到

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号