6-氯-1-(2,6-二甲基-苯基)-7-甲氧基-3,4-二氢-异喹啉 | 1027263-80-4

• 分子结构分类: 有机化合物 - 有机杂环化合物 - 二氢异喹啉
• 中文名称: 6-氯-1-(2,6-二甲基-苯基)-7-甲氧基-3,4-二氢-异喹啉
• 英文名称: 6-Chloro-1-(2,6-dimethylphenyl)-7-methoxy-3,4-dihydroisoquinoline
• CAS: 1027263-80-4
• 化学式: C₁₈H₁₈ClNO
• 分子量: 299.8
• InChiKey: NPCBTXYQEOEDHG-UHFFFAOYSA-N

计算性质

• 辛醇/水分配系数(LogP)：
  3.8
• 重原子数：
  21
• 可旋转键数：
  2
• 环数：
  3.0
• sp3杂化的碳原子比例：
  0.28
• 拓扑面积：
  21.6
• 氢给体数：
  0
• 氢受体数：
  2

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-3,4-二氢-异喹啉 在 sodium tetrahydroborate 作用下， 生成 6-Chloro-1-(2,6-dimethylphenyl)-7-methoxy-1,2,3,4-tetrahydroisoquinoline
  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：

  3-氯-4-甲氧基苯乙腈 在 lithium aluminium tetrahydride 、 phosphorus pentoxide 、 三氯氧磷 作用下， 生成 6-氯-1-(2,6-二甲基-苯基)-7-甲氧基-3,4-二氢-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