N-(benzo[b]thiophen-7-ylmethyl)-N-(2,4-dichlorobenzyl)-1H-imidazol-1-amine | 1079267-22-3

• 分子结构分类: 有机化合物 - 有机杂环化合物 - 苯并噻吩类
• 英文名称: N-(benzo[b]thiophen-7-ylmethyl)-N-(2,4-dichlorobenzyl)-1H-imidazol-1-amine
• 英文别名: N-(1-benzothiophen-7-ylmethyl)-N-[(2,4-dichlorophenyl)methyl]imidazol-1-amine
• CAS: 1079267-22-3
• 化学式: C₁₉H₁₅Cl₂N₃S
• 分子量: 388.32
• InChiKey: WEXVQCQOUIGLFU-UHFFFAOYSA-N

计算性质

• 辛醇/水分配系数(LogP)：
  5.8
• 重原子数：
  25
• 可旋转键数：
  5
• 环数：
  4.0
• sp3杂化的碳原子比例：
  0.11
• 拓扑面积：
  49.3
• 氢给体数：
  0
• 氢受体数：
  3

反应信息

• 作为产物：
  描述：

  7-溴甲基-苯并[b]噻吩 、 N-(2,4-dichlorobenzyl)-1H-imidazol-1-amine 在 18-冠醚-6 、 potassium tert-butylate 作用下， 以 乙醚 为溶剂， 反应 4.0h， 以26%的产率得到N-(benzo[b]thiophen-7-ylmethyl)-N-(2,4-dichlorobenzyl)-1H-imidazol-1-amine
  参考文献：
  名称：

  CYP19 (aromatase): Exploring the scaffold flexibility for novel selective inhibitors

  摘要：

  Several derivatives out of a series of antifungal agents exhibited a good inhibitory potency against aromatase as well as a fairly good selectivity toward CYP17, even if lacking H-bond accepting substituents. Their common structural feature is a flexible backbone that did not fit into previously reported CYP19 models. Thus, a ligand-based approach was exploited to develop a novel statistically robust, self-consistent and predictive 3D-QSAR model herein proposed as a helpful tool to design new aromatase inhibitors. (C) 2008 Elsevier Ltd. All rights reserved.

  DOI：

  10.1016/j.bmc.2008.08.046

文献信息

• CYP19 (aromatase): Exploring the scaffold flexibility for novel selective inhibitors
  作者：Sabrina Castellano、Giorgio Stefancich、Rino Ragno、Katarzyna Schewe、Marisabella Santoriello、Antonia Caroli、Rolf W. Hartmann、Gianluca Sbardella

  DOI：10.1016/j.bmc.2008.08.046

  日期：2008.9

  Several derivatives out of a series of antifungal agents exhibited a good inhibitory potency against aromatase as well as a fairly good selectivity toward CYP17, even if lacking H-bond accepting substituents. Their common structural feature is a flexible backbone that did not fit into previously reported CYP19 models. Thus, a ligand-based approach was exploited to develop a novel statistically robust, self-consistent and predictive 3D-QSAR model herein proposed as a helpful tool to design new aromatase inhibitors. (C) 2008 Elsevier Ltd. All rights reserved.