3-acetylamino-7-hydroxy-8-methoxycoumarin | 875153-79-0

• 分子结构分类: 有机化合物 - 苯丙烷和聚酮 - 香豆素及其衍生物
• 英文名称: 3-acetylamino-7-hydroxy-8-methoxycoumarin
• 英文别名: N-(7-Hydroxy-8-methoxy-2-oxo-2H-chr；N-(7-hydroxy-8-methoxy-2-oxochromen-3-yl)acetamide
• CAS: 875153-79-0
• 化学式: C₁₂H₁₁NO₅
• 分子量: 249.223
• InChiKey: HPIBBNXKJNLMLD-UHFFFAOYSA-N

计算性质

• 辛醇/水分配系数(LogP)：
  0.9
• 重原子数：
  18
• 可旋转键数：
  2
• 环数：
  2.0
• sp3杂化的碳原子比例：
  0.17
• 拓扑面积：
  84.9
• 氢给体数：
  2
• 氢受体数：
  5

反应信息

• 作为反应物：
  描述：

  3-acetylamino-7-hydroxy-8-methoxycoumarin 在 sodium hydroxide 、 potassium carbonate 作用下， 以 乙醇 、 丙酮 为溶剂， 反应 23.0h， 生成 N-(9-Methoxy-3-methyl-7-oxo-7H-furo
  参考文献：
  名称：

  A QSAR Model for in Silico Screening of MAO-A Inhibitors. Prediction, Synthesis, and Biological Assay of Novel Coumarins

  摘要：

  This work explores the potential of the MARCH-INSIDE methodology to seek a QSAR for MAO-A inhibitors from a heterogeneous series of compounds. A Markov model was used to quickly calculate the molecular electron delocalization, polarizability, refractivity, and n-octanol/water partition coefficients for a series of 1406 active/nonactive compounds. LDA was subsequently used to fit a classification function. The model showed 92.8% and 91.8% global accuracy and predictability in training and validation studies. This QSAR model was validated through a virtual screening of a series of cournarin derivatives. The 15 selected compounds were prepared and evaluated as in vitro MAO-A inhibitors. The theoretical prediction was' compared with the experimental results and the model correctly predicted 13 compounds with only two mistakes on compounds with activities very close to the cutoff point established for the model. Consequently, this method represents a useful tool for the "in silico" screening of MAO-A inhibitors.

  DOI：

  10.1021/jm0509849
• 作为产物：
  描述：

  2,4-dihydroxy-3-methoxybenzaldehyde 在 Ac₂ONa 、 乙酸酐 、 溶剂黄146 作用下， 生成 3-acetylamino-7-hydroxy-8-methoxycoumarin
  参考文献：
  名称：

  A QSAR Model for in Silico Screening of MAO-A Inhibitors. Prediction, Synthesis, and Biological Assay of Novel Coumarins

  摘要：

  This work explores the potential of the MARCH-INSIDE methodology to seek a QSAR for MAO-A inhibitors from a heterogeneous series of compounds. A Markov model was used to quickly calculate the molecular electron delocalization, polarizability, refractivity, and n-octanol/water partition coefficients for a series of 1406 active/nonactive compounds. LDA was subsequently used to fit a classification function. The model showed 92.8% and 91.8% global accuracy and predictability in training and validation studies. This QSAR model was validated through a virtual screening of a series of cournarin derivatives. The 15 selected compounds were prepared and evaluated as in vitro MAO-A inhibitors. The theoretical prediction was' compared with the experimental results and the model correctly predicted 13 compounds with only two mistakes on compounds with activities very close to the cutoff point established for the model. Consequently, this method represents a useful tool for the "in silico" screening of MAO-A inhibitors.

  DOI：

  10.1021/jm0509849

文献信息

• A QSAR Model for in Silico Screening of MAO-A Inhibitors. Prediction, Synthesis, and Biological Assay of Novel Coumarins
  作者：Lourdes Santana、Eugenio Uriarte、Humberto González-Díaz、Giuseppe Zagotto、Ramón Soto-Otero、Estefanía Méndez-Álvarez

  DOI：10.1021/jm0509849

  日期：2006.2.1

  This work explores the potential of the MARCH-INSIDE methodology to seek a QSAR for MAO-A inhibitors from a heterogeneous series of compounds. A Markov model was used to quickly calculate the molecular electron delocalization, polarizability, refractivity, and n-octanol/water partition coefficients for a series of 1406 active/nonactive compounds. LDA was subsequently used to fit a classification function. The model showed 92.8% and 91.8% global accuracy and predictability in training and validation studies. This QSAR model was validated through a virtual screening of a series of cournarin derivatives. The 15 selected compounds were prepared and evaluated as in vitro MAO-A inhibitors. The theoretical prediction was' compared with the experimental results and the model correctly predicted 13 compounds with only two mistakes on compounds with activities very close to the cutoff point established for the model. Consequently, this method represents a useful tool for the "in silico" screening of MAO-A inhibitors.