N,5-双(4-硝基苯基)-1,3,4-噻二唑-2-胺 | 827580-70-1

• 分子结构分类: 有机化合物 - 苯类化合物 - 苯和取代衍生物
• 中文名称: N,5-双(4-硝基苯基)-1,3,4-噻二唑-2-胺
• 英文名称: 2-(4-nitrophenylamino)-5-(4-nitrophenyl)-1,3,4-thiadiazole
• 英文别名: N-(4-nitrophenyl)-5-(4-nitrophenyl)-1,3,4-thiadiazol-2-amine；N,5-bis(4-nitrophenyl)-1,3,4-thiadiazol-2-amine
• CAS: 827580-70-1
• 化学式: C₁₄H₉N₅O₄S
• 分子量: 343.323
• InChiKey: CEUUXYYPBICAHW-UHFFFAOYSA-N

计算性质

• 辛醇/水分配系数(LogP)：
  3.6
• 重原子数：
  24
• 可旋转键数：
  3
• 环数：
  3.0
• sp3杂化的碳原子比例：
  0.0
• 拓扑面积：
  158
• 氢给体数：
  1
• 氢受体数：
  8

反应信息

• 作为产物：
  描述：

  4-硝基苯酰肼 在 硫酸 作用下， 以 乙醇 为溶剂， 反应 3.5h， 生成 N,5-双(4-硝基苯基)-1,3,4-噻二唑-2-胺
  参考文献：
  名称：

  1,3,4-Thiadiazole Derivatives. Synthesis, Structure Elucidation, and Structure−Antituberculosis Activity Relationship Investigation

  摘要：

  A series of 2,5-disubstituted-1,3,4-thiadiazoles were synthesized, the compounds structures were elucidated and screened for the antituberculosis activity against Mycobacterium tuberculosis H37Rv using the BACTEC 460 radiometric system. Among the tested compounds, 2-phenylamino-5-(4-fluorophenyl)-1,3,4-thiadiazole 22 showed the highest inhibitory activity. The relationships between the structures of compounds and their antituberculosis activity were investigated by the Electronic-Topological Method (ETM) and feed forward neural networks (FFNNs) trained with the back-propagation algorithm. As a result of the approach, a system of pharmacophores and anti-pharmacophores has been found that effectively separates compounds of the examination set into groups of active and inactive compounds. The system can be applied to the screening and design of new active compounds possessing skeletons similar to those used in the present study.

  DOI：

  10.1021/jm0495632

文献信息

• Efficient synthesis of 1,3,4-thiadiazoles using hydrogen bond donor (thio)urea derivatives as organocatalysts
  作者：Shahnaz Rostamizadeh、Reza Aryan、Hamid Reza Ghaieni、Ali Mohammad Amani

  DOI：10.1002/jhet.367

  日期：——

  A simple and efficient procedure for the synthesis of 1,3,4‐thiadiazoles has been achieved using thiourea as organocatalyst. In this study, the steric and electronic effects using structurally different derivatives of urea and thiourea in different solvents were evaluated. The best yields and the rate of the reactions were obtained using 30 mol % of thiourea as catalyst in acetonitrile at room temperature

  使用硫脲作为有机催化剂，已经实现了一种简单而有效的合成1,3,4-噻二唑的方法。在这项研究中，评估了在不同溶剂中使用结构不同的脲和硫脲衍生物的空间和电子效应。在室温下，使用30mol％的硫脲作为催化剂在乙腈中获得最佳的产率和反应速率。产物的分子结构由1 H和13 C NMR光谱数据确定。J.杂环化​​学。（2010）。
• An efficient one-pot procedure for the preparation of 1,3,4-thiadiazoles in ionic liquid [bmim]BF4 as dual solvent and catalyst
  作者：Shahnaz Rostamizadeh、Reza Aryan、Hamid R. Ghaieni、Ali M. Amani

  DOI：10.1002/hc.20432

  日期：2008.4

  The one-pot three component condensation of hydrazine hydrate with substituted phenylisothiocyanates followed by the addition of substituted benzaldehydes in the presence of ionic liquid 1-butyl-3-methylimidazolium tetrafluoroborate ([bmim]BF4) and in the absence of any other catalyst under mild condition afforded 1,3,4-thiadiazoles in excellent yields. The reaction workup is simple, and the ionic liquid

  在离子液体 1-丁基-3-甲基咪唑鎓四氟硼酸盐 ([bmim]BF4) 存在下，在没有任何其他催化剂的情况下，水合肼与取代的异硫氰酸苯酯的一锅三组分缩合，然后加入取代的苯甲醛条件下以极好的收率提供了 1,3,4-噻二唑。反应后处理简单，离子液体很容易从反应中回收再利用。© 2008 Wiley Periodicals, Inc. 杂原子化学 19:320–324, 2008; 在线发表于 Wiley InterScience (www.interscience.wiley.com)。DOI 10.1002/hc.20432
• 1,3,4-Thiadiazole Derivatives. Synthesis, Structure Elucidation, and Structure−Antituberculosis Activity Relationship Investigation
  作者：Elçin E. Oruç、Sevim Rollas、Fatma Kandemirli、Nathaly Shvets、Anatholy S. Dimoglo

  DOI：10.1021/jm0495632

  日期：2004.12.1

  A series of 2,5-disubstituted-1,3,4-thiadiazoles were synthesized, the compounds structures were elucidated and screened for the antituberculosis activity against Mycobacterium tuberculosis H37Rv using the BACTEC 460 radiometric system. Among the tested compounds, 2-phenylamino-5-(4-fluorophenyl)-1,3,4-thiadiazole 22 showed the highest inhibitory activity. The relationships between the structures of compounds and their antituberculosis activity were investigated by the Electronic-Topological Method (ETM) and feed forward neural networks (FFNNs) trained with the back-propagation algorithm. As a result of the approach, a system of pharmacophores and anti-pharmacophores has been found that effectively separates compounds of the examination set into groups of active and inactive compounds. The system can be applied to the screening and design of new active compounds possessing skeletons similar to those used in the present study.