6-氯-7-甲氧基-1-对甲苯基-1,2,3,4-四氢异喹啉 | 1026846-54-7

• 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-7-甲氧基-1-对甲苯基-1,2,3,4-四氢异喹啉
• 英文名称: 6-Chloro-7-methoxy-1-(4-methylphenyl)-1,2,3,4-tetrahydroisoquinoline
• CAS: 1026846-54-7
• 化学式: C₁₇H₁₈ClNO
• 分子量: 287.789
• InChiKey: VGUCQZJFNMSQEC-UHFFFAOYSA-N

计算性质

• 辛醇/水分配系数(LogP)：
  4
• 重原子数：
  20
• 可旋转键数：
  2
• 环数：
  3.0
• sp3杂化的碳原子比例：
  0.29
• 拓扑面积：
  21.3
• 氢给体数：
  1
• 氢受体数：
  2

反应信息

• 作为反应物：
  描述：

  6-氯-7-甲氧基-1-对甲苯基-1,2,3,4-四氢异喹啉 在 甲酸 、 氢溴酸 作用下， 反应 2.0h， 生成 6-chloro-2-methyl-1-(4-methylphenyl)-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：

  对甲氧基苯乙腈 在 sodium tetrahydroborate 、 lithium aluminium tetrahydride 、 phosphorus pentoxide 、 氯 、 三氯氧磷 作用下， 生成 6-氯-7-甲氧基-1-对甲苯基-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.