N-[(Z)-2-chloro-2-phenyl-1-(piperidine-1-carbonyl)vinyl]-4-nitro-benzamide | 1323140-65-3

• 分子结构分类: 有机化合物 - 苯类化合物 - 苯和取代衍生物
• 英文名称: N-[(Z)-2-chloro-2-phenyl-1-(piperidine-1-carbonyl)vinyl]-4-nitro-benzamide
• 英文别名: N-[(Z)-1-chloro-3-oxo-1-phenyl-3-piperidin-1-ylprop-1-en-2-yl]-4-nitrobenzamide
• CAS: 1323140-65-3
• 化学式: C₂₁H₂₀ClN₃O₄
• 分子量: 413.861
• InChiKey: XCHXNTRTVYFKPT-HNENSFHCSA-N

计算性质

• 辛醇/水分配系数(LogP)：
  3.9
• 重原子数：
  29
• 可旋转键数：
  4
• 环数：
  3.0
• sp3杂化的碳原子比例：
  0.24
• 拓扑面积：
  95.2
• 氢给体数：
  1
• 氢受体数：
  4

反应信息

• 作为产物：
  描述：

  2-(p-Nitrophenyl)-4-benzylidene-2-oxazolin-5-one 在 磺酰氯 作用下， 以 二氯甲烷 为溶剂， 反应 4.0h， 生成 N-[(Z)-2-chloro-2-phenyl-1-(piperidine-1-carbonyl)vinyl]-4-nitro-benzamide
  参考文献：
  名称：

  Synthesis and Quantitative Structure-activity Relationships Study for Arylpropenamide Derivatives as Inhibitors of Hepatitis B Virus Replication

  摘要：

  A series of new arylpropenamide derivatives containing different aryl groups were synthesized, characterized, and evaluated for their anti‐hepatitis B virus (HBV) activities. A new high accuracy QSAR model of arylpropenamide was constructed based on a more completely activities data and calculation parameter. The 2D‐QSAR equations, by using DFT and multiple linear regression analysis methods, revealed that higher value of thermal energy (TE) and lower entropy (Sө) increase the anti‐HBV activities of the arylpropenamide molecules. Predictive 3D‐QSAR models were established by SYBYL multifit molecular alignment rule. The optimum models were all statistically significant with cross‐validated and conventional coefficients, indicating that they were reliable enough for activity prediction.

  DOI：

  10.1111/cbdd.12774

文献信息

• Phenylpropenamide derivatives: Anti-hepatitis B virus activity of the Z isomer, SAR and the search for novel analogs
  作者：Peiyuan Wang、Devan Naduthambi、Ralph T. Mosley、Congrong Niu、Phillip A. Furman、Michael J. Otto、Michael J. Sofia

  DOI：10.1016/j.bmcl.2011.05.077

  日期：2011.8

  Phenylpropenamides have been reported to be a class of non-nucleoside inhibitors of the hepatitis B virus (HBV). This class of compounds was explored with the objective of developing potent anti-HBV agents, with a novel mechanism of action, that could be combined with nucleos(t) ide analogs currently used to treat HBV infection. To accomplish this objective a series of substituted arylpropenamide derivatives were prepared and the E and Z geometrical isomers were separated. The structural identity of each of the E and Z isomers was determined by single crystal X-ray crystallography. Contrary to previous reports, the activity of this class of molecules resides in the Z isomer. Further structure-activity relationship studies around the active Z isomer identified compounds that displayed potent antiviral activity against HBV with EC90 value of approximately 0.5 mu M in vitro. Attempts to develop ring constrained analogs did not lead to active HBV inhibitors. (C) 2011 Elsevier Ltd. All rights reserved.
• Synthesis and Quantitative Structure-activity Relationships Study for Arylpropenamide Derivatives as Inhibitors of Hepatitis B Virus Replication
  作者：Ma Min、Jiang Xingjun、Wang Xueding、Zou Hao、Yang Weiqing、Zhang Yuanyuan、Peng Changrong、Li Zicheng、Yang Jing、Du Quan、Ma Menglin

  DOI：10.1111/cbdd.12774

  日期：2016.9

  A series of new arylpropenamide derivatives containing different aryl groups were synthesized, characterized, and evaluated for their anti‐hepatitis B virus (HBV) activities. A new high accuracy QSAR model of arylpropenamide was constructed based on a more completely activities data and calculation parameter. The 2D‐QSAR equations, by using DFT and multiple linear regression analysis methods, revealed that higher value of thermal energy (TE) and lower entropy (Sө) increase the anti‐HBV activities of the arylpropenamide molecules. Predictive 3D‐QSAR models were established by SYBYL multifit molecular alignment rule. The optimum models were all statistically significant with cross‐validated and conventional coefficients, indicating that they were reliable enough for activity prediction.