6-氯-1-(2,6-二甲基-苯基)-7-甲氧基-2-甲基-1,2,3,4-四氢-异喹啉 | 1026343-25-8

• 分子结构分类: 有机化合物 - 有机杂环化合物 - 四氢异喹啉
• 中文名称: 6-氯-1-(2,6-二甲基-苯基)-7-甲氧基-2-甲基-1,2,3,4-四氢-异喹啉
• 英文名称: 6-chloro-1-(2,6-dimethylphenyl)-7-methoxy-2-methyl-3,4-dihydro-1H-isoquinoline
• CAS: 1026343-25-8
• 化学式: C₁₉H₂₂ClNO
• 分子量: 315.843
• InChiKey: CYHDOQPTCOXMRA-UHFFFAOYSA-N

计算性质

• 辛醇/水分配系数(LogP)：
  4.8
• 重原子数：
  22
• 可旋转键数：
  2
• 环数：
  3.0
• sp3杂化的碳原子比例：
  0.37
• 拓扑面积：
  12.5
• 氢给体数：
  0
• 氢受体数：
  2

上下游信息

• 上游原料

  中文名称	英文名称	CAS号	化学式	分子量
  ——	6-Chloro-1-(2,6-dimethylphenyl)-7-methoxy-1,2,3,4-tetrahydroisoquinoline	1026596-21-3	C18H20ClNO	301.816
  6-氯-1-(2,6-二甲基-苯基)-7-甲氧基-3,4-二氢-异喹啉	6-Chloro-1-(2,6-dimethylphenyl)-7-methoxy-3,4-dihydroisoquinoline	1027263-80-4	C18H18ClNO	299.8

反应信息

• 作为反应物：
  描述：

  6-氯-1-(2,6-二甲基-苯基)-7-甲氧基-2-甲基-1,2,3,4-四氢-异喹啉 在 氢溴酸 作用下， 生成 6-chloro-1-(2,6-dimethylphenyl)-2-methyl-3,4-dihydro-1H-isoquinolin-7-ol
  参考文献：
  名称：

  Quantitative Structure−Activity Relationship Modeling of Dopamine D₁ Antagonists Using Comparative Molecular Field Analysis, Genetic Algorithms−Partial Least-Squares, and K Nearest Neighbor Methods

  摘要：

  Several quantitative structure-activity relationship (QSAR) methods were applied to 29 chemically diverse D-1 dopamine antagonists. In addition to conventional 3D comparative molecular field analysis (CoMFA), cross-validated R-2 guided region selection (q(2)-GRS) CoMFA (see ref 1) was employed, as were two novel variable selection QSAR methods recently developed in one of our laboratories. These latter methods included genetic algorithm-partial least squares (GA-PLS) and K nearest neighbor (KNN) procedures (see refs 2-4), which utilize 2D topological descriptors of chemical structures. Each QSAR approach resulted in a highly predictive model, with cross-validated R-2 (q(2)) values of 0.57 for CoMFA, 0.54 for q(2)-GRS, 0.73 for GA-PLS, and 0.79 for KNN. The success of all of the QSAR methods indicates the presence of an intrinsic structure-activity relationship in this group of compounds and affords more robust design and prediction of biological activities of novel D1 ligands.

  DOI：

  10.1021/jm980415j
• 作为产物：
  描述：

  2-(3-氯-4-甲氧基苯基)-乙胺 在 sodium tetrahydroborate 、 甲酸 、 phosphorus pentoxide 、 三氯氧磷 作用下， 反应 2.0h， 生成 6-氯-1-(2,6-二甲基-苯基)-7-甲氧基-2-甲基-1,2,3,4-四氢-异喹啉
  参考文献：
  名称：

  Quantitative Structure−Activity Relationship Modeling of Dopamine D₁ Antagonists Using Comparative Molecular Field Analysis, Genetic Algorithms−Partial Least-Squares, and K Nearest Neighbor Methods

  摘要：

  Several quantitative structure-activity relationship (QSAR) methods were applied to 29 chemically diverse D-1 dopamine antagonists. In addition to conventional 3D comparative molecular field analysis (CoMFA), cross-validated R-2 guided region selection (q(2)-GRS) CoMFA (see ref 1) was employed, as were two novel variable selection QSAR methods recently developed in one of our laboratories. These latter methods included genetic algorithm-partial least squares (GA-PLS) and K nearest neighbor (KNN) procedures (see refs 2-4), which utilize 2D topological descriptors of chemical structures. Each QSAR approach resulted in a highly predictive model, with cross-validated R-2 (q(2)) values of 0.57 for CoMFA, 0.54 for q(2)-GRS, 0.73 for GA-PLS, and 0.79 for KNN. The success of all of the QSAR methods indicates the presence of an intrinsic structure-activity relationship in this group of compounds and affords more robust design and prediction of biological activities of novel D1 ligands.

  DOI：

  10.1021/jm980415j

文献信息

• Quantitative Structure−Activity Relationship Modeling of Dopamine D₁ Antagonists Using Comparative Molecular Field Analysis, Genetic Algorithms−Partial Least-Squares, and K Nearest Neighbor Methods
  作者：Brian Hoffman、Sung Jin Cho、Weifan Zheng、Steven Wyrick、David E. Nichols、Richard B. Mailman、Alexander Tropsha

  DOI：10.1021/jm980415j

  日期：1999.8.1

  Several quantitative structure-activity relationship (QSAR) methods were applied to 29 chemically diverse D-1 dopamine antagonists. In addition to conventional 3D comparative molecular field analysis (CoMFA), cross-validated R-2 guided region selection (q(2)-GRS) CoMFA (see ref 1) was employed, as were two novel variable selection QSAR methods recently developed in one of our laboratories. These latter methods included genetic algorithm-partial least squares (GA-PLS) and K nearest neighbor (KNN) procedures (see refs 2-4), which utilize 2D topological descriptors of chemical structures. Each QSAR approach resulted in a highly predictive model, with cross-validated R-2 (q(2)) values of 0.57 for CoMFA, 0.54 for q(2)-GRS, 0.73 for GA-PLS, and 0.79 for KNN. The success of all of the QSAR methods indicates the presence of an intrinsic structure-activity relationship in this group of compounds and affords more robust design and prediction of biological activities of novel D1 ligands.