1,3-Dihydroxy-2-nitrobenzo[b]xanthen-12-one | 1068126-30-6

• 分子结构分类: 有机化合物 - 有机杂环化合物 - 苯并吡喃
• 英文名称: 1,3-Dihydroxy-2-nitrobenzo[b]xanthen-12-one
• CAS: 1068126-30-6
• 化学式: C₁₇H₉NO₆
• 分子量: 323.262
• InChiKey: HWONXTBCOAUQJP-UHFFFAOYSA-N

计算性质

• 辛醇/水分配系数(LogP)：
  4.4
• 重原子数：
  24
• 可旋转键数：
  0
• 环数：
  4.0
• sp3杂化的碳原子比例：
  0.0
• 拓扑面积：
  113
• 氢给体数：
  2
• 氢受体数：
  6

反应信息

• 作为反应物：
  描述：

  1,3-Dihydroxy-2-nitrobenzo[b]xanthen-12-one 在 palladium on activated charcoal 氢气 作用下， 以 四氢呋喃 为溶剂， 反应 10.0h， 以59%的产率得到2-Amino-1,3-dihydroxybenzo[b]xanthen-12-one
  参考文献：
  名称：

  Synthesis, inhibitory activities, and QSAR study of xanthone derivatives as α-glucosidase inhibitors

  摘要：

  Xanthones and their derivatives have been reported to exhibit strong inhibitory activities toward alpha-glucosidase. To provide deep insight into the correlation between inhibitory activities and structures of xanthones, multiple linear regression (MLR) method was employed to establish QSAR models for 43 xanthone derivatives that have diverse structures. Among the 38 typical descriptors investigated, Hs (number of H-bond forming substituents), N-pi (number of aromatic rings), and S (softness value) can be utilized to model the inhibitory activity. Thus, inhibitory activities of xanthone derivatives can be regulated by H-bond forming substituents, pi-stacking-forming aromatic rings and softness values on the xanthone skeleton. The accuracy and predictive power of the proposed QSAR model were verified by LOO validation, Y-randomization, and test group validation with newly synthesized xanthone derivatives. (C) 2008 Elsevier Ltd. All rights reserved.

  DOI：

  10.1016/j.bmc.2008.06.043
• 作为产物：
  描述：

  1,3-dihydroxy-12H-benzo[b]xanthen-12-one 在 硝酸 、 溶剂黄146 作用下， 反应 4.0h， 以42%的产率得到1,3-Dihydroxy-2-nitrobenzo[b]xanthen-12-one
  参考文献：
  名称：

  Synthesis, inhibitory activities, and QSAR study of xanthone derivatives as α-glucosidase inhibitors

  摘要：

  Xanthones and their derivatives have been reported to exhibit strong inhibitory activities toward alpha-glucosidase. To provide deep insight into the correlation between inhibitory activities and structures of xanthones, multiple linear regression (MLR) method was employed to establish QSAR models for 43 xanthone derivatives that have diverse structures. Among the 38 typical descriptors investigated, Hs (number of H-bond forming substituents), N-pi (number of aromatic rings), and S (softness value) can be utilized to model the inhibitory activity. Thus, inhibitory activities of xanthone derivatives can be regulated by H-bond forming substituents, pi-stacking-forming aromatic rings and softness values on the xanthone skeleton. The accuracy and predictive power of the proposed QSAR model were verified by LOO validation, Y-randomization, and test group validation with newly synthesized xanthone derivatives. (C) 2008 Elsevier Ltd. All rights reserved.

  DOI：

  10.1016/j.bmc.2008.06.043

文献信息

• Synthesis, inhibitory activities, and QSAR study of xanthone derivatives as α-glucosidase inhibitors
  作者：Yan Liu、Zhuofeng Ke、Jianfang Cui、Wen-Hua Chen、Lin Ma、Bo Wang

  DOI：10.1016/j.bmc.2008.06.043

  日期：2008.8

  Xanthones and their derivatives have been reported to exhibit strong inhibitory activities toward alpha-glucosidase. To provide deep insight into the correlation between inhibitory activities and structures of xanthones, multiple linear regression (MLR) method was employed to establish QSAR models for 43 xanthone derivatives that have diverse structures. Among the 38 typical descriptors investigated, Hs (number of H-bond forming substituents), N-pi (number of aromatic rings), and S (softness value) can be utilized to model the inhibitory activity. Thus, inhibitory activities of xanthone derivatives can be regulated by H-bond forming substituents, pi-stacking-forming aromatic rings and softness values on the xanthone skeleton. The accuracy and predictive power of the proposed QSAR model were verified by LOO validation, Y-randomization, and test group validation with newly synthesized xanthone derivatives. (C) 2008 Elsevier Ltd. All rights reserved.