ZGlyNEt2 | 79990-06-0

• 分子结构分类: 有机化合物 - 有机酸及其衍生物 - 羧酸及其衍生物
• 英文名称: ZGlyNEt2
• 英文别名: Benzyl N-[(diethylcarbamoyl)methyl]carbamate；benzyl N-[2-(diethylamino)-2-oxoethyl]carbamate
• CAS: 79990-06-0
• 化学式: C₁₄H₂₀N₂O₃
• 分子量: 264.324
• InChiKey: HMKYEXVMONNYQW-UHFFFAOYSA-N

计算性质

• 辛醇/水分配系数(LogP)：
  1.7
• 重原子数：
  19
• 可旋转键数：
  7
• 环数：
  1.0
• sp3杂化的碳原子比例：
  0.43
• 拓扑面积：
  58.6
• 氢给体数：
  1
• 氢受体数：
  3

反应信息

• 作为反应物：
  描述：

  ZGlyNEt2 在 10% Pd/C 、 氢气 作用下， 以 乙醇 为溶剂， 20.0 ℃ 、405.33 kPa 条件下， 反应 16.0h， 以85%的产率得到2-氨基-N,N-二乙基乙酰胺
  参考文献：
  名称：

  新的支链大环配体及其侧臂，两个基于尿素的阴离子受体：合成，结合研究和晶体结构

  摘要：

  两种新型阴离子阴离子分子的合成与表征 4（N），10（N）-双-[2-（4-硝基苯基脲基）乙酰氨基] -1,7-二甲基-1,4,7,10-四氮杂环十二烷（L1）和1-（（二乙基氨基甲酰基）甲基）-3-（4-硝基苯基）脲（L2）被报告。L1是分支的四氮杂大环 轴承二 对硝基苯基脲基 团体作为侧臂，而L2具有与L1侧臂相同的线性链和结合部分。探索了用于获得L1的最佳合成路线，从而提供了新中间体4的合成，该中间体是进一步官能化的支化大环化合物的通用组成部分主机。两种配体对卤化物系列和醋酸盐 阴离子（G）由 核磁共振 和 紫外可见光谱 在一个 二甲基亚砜–0.5％ 水解决方案。两种配体均与F−，氯-和醋酸−而溴-而我-没有。这核磁共振实验证明结合是通过 H-键到 尿素 碎片。 氟化物 阴离子足以使质子去质 尿素 团体两种配体的结合，因此妨碍了对两种配体的加成常数的确定；相反，这是可能的氯-和醋酸-。L1形成化学计量为1：1（[G

  DOI：

  10.1039/b719342d
• 作为产物：
  描述：

  ZGly, triethylammonium salt 以 N,N-二甲基甲酰胺 为溶剂， 反应 3.0h， 生成 ZGlyNEt2
  参考文献：
  名称：

  Peptide synthesis with benzo- and naphthosultones

  摘要：

  DOI：

  10.1021/jo00196a006

文献信息

• Application of Predictive QSAR Models to Database Mining:  Identification and Experimental Validation of Novel Anticonvulsant Compounds
  作者：Min Shen、Cécile Béguin、Alexander Golbraikh、James P. Stables、Harold Kohn、Alexander Tropsha

  DOI：10.1021/jm030584q

  日期：2004.4.1

  We have developed a drug discovery strategy that employs variable selection quantitative structure-activity relationship (QSAR) models for chemical database mining. The approach starts with the development of rigorously validated QSAR models obtained with the variable selection k nearest neighbor (kNN) method (or, in principle, with any other robust model-building technique). Model validation. is based on several statistical criteria, including the randomization of the target property (Y-randomization), independent assessment of the training set model's predictive power using external test sets, and the establishment of the model's applicability domain. All successful models are employed in database mining concurrently; in each case, only variables selected as a result of model building (termed descriptor pharmacophore) are used in chemical similarity searches comparing active compounds of the training set (queries) with those in chemical databases. Specific biological activity (characteristic of the training set compounds) of external database entries found to be within a predefined similarity threshold of the training set molecules is predicted on the basis of the validated QSAR models using the applicability domain criteria. Compounds judged to have high predicted activities by all or the majority of all models are considered as consensus hits. We report on the application of this computational strategy for the first time for the discovery of anticonvulsant agents in the Maybridge and National Cancer Institute (NCI) databases containing ca. 250 000 compounds combined. Forty-eight anticonvulsant agents of the functionalized amino acid (FAA) series were used to build kNN variable selection QSAR models. The 10 best models were applied to mining chemical databases, and 22 compounds were selected as consensus hits. Nine compounds were synthesized and tested at the NIH Epilepsy Branch, Rockville, MD using the same biological test that was employed to assess the anticonvulsant activity of the training set compounds; of these nine, four were exact database hits and five were derived from the hits by minor chemical modifications. Seven of these nine compounds were confirmed to be active, indicating an exceptionally high hit rate. The approach described in this report can be used as a general rational drug discovery tool.