N-(6-chloropyridazin-3-yl)-1,3-benzothiazol-6-amine | 1032184-69-2

• 分子结构分类: 有机化合物 - 有机杂环化合物 - 苯并噻唑类
• 英文名称: N-(6-chloropyridazin-3-yl)-1,3-benzothiazol-6-amine
• CAS: 1032184-69-2
• 化学式: C₁₁H₇ClN₄S
• 分子量: 262.722
• InChiKey: CQKSUZXYIXZKMN-UHFFFAOYSA-N

计算性质

• 辛醇/水分配系数(LogP)：
  3.1
• 重原子数：
  17
• 可旋转键数：
  2
• 环数：
  3.0
• sp3杂化的碳原子比例：
  0.0
• 拓扑面积：
  78.9
• 氢给体数：
  1
• 氢受体数：
  5

反应信息

• 作为反应物：
  描述：

  N-(6-chloropyridazin-3-yl)-1,3-benzothiazol-6-amine 、 3-羧基苯硼酸 在 四(三苯基膦)钯 、 sodium carbonate 作用下， 以 乙醇 、 甲苯 为溶剂， 生成 3-[6-(苯并噻唑-6-基氨基)哒嗪-3-基]苯甲酸
  参考文献：
  名称：

  Development and Experimental Validation of a Docking Strategy for the Generation of Kinase-Targeted Libraries

  摘要：

  A high-throughput docking strategy for the filtering of in silico compounds and the generation of kinase-targeted libraries is described. Systematic docking and scoring in three kinase crystal 3D structures of 123 structurally diverse kinase ligands led to the determination of six thresholds for each kinase. These thresholds were used as filters for the virtual screening of two collections of compounds: a collection of more than 2500 drugs and drug-like compounds (negative control) and a kinase-targeted library of 1440 compounds. This strategy was then experimentally validated by testing 60 compounds from the kinase-targeted library on 41 kinases from five different families. The 60 compounds were split into those passing all the thresholds and the others (30 compounds in each group). The overall hit enrichment was 6.70-fold higher in the first group, validating our approach for the generation of kinase-targeted libraries and the identification of scaffolds with high kinase inhibitory potential.

  DOI：

  10.1021/jm701367r
• 作为产物：
  描述：

  3,6-二氯哒嗪 、 6-氨基苯并噻唑 以 乙醇 为溶剂， 生成 N-(6-chloropyridazin-3-yl)-1,3-benzothiazol-6-amine
  参考文献：
  名称：

  Development and Experimental Validation of a Docking Strategy for the Generation of Kinase-Targeted Libraries

  摘要：

  A high-throughput docking strategy for the filtering of in silico compounds and the generation of kinase-targeted libraries is described. Systematic docking and scoring in three kinase crystal 3D structures of 123 structurally diverse kinase ligands led to the determination of six thresholds for each kinase. These thresholds were used as filters for the virtual screening of two collections of compounds: a collection of more than 2500 drugs and drug-like compounds (negative control) and a kinase-targeted library of 1440 compounds. This strategy was then experimentally validated by testing 60 compounds from the kinase-targeted library on 41 kinases from five different families. The 60 compounds were split into those passing all the thresholds and the others (30 compounds in each group). The overall hit enrichment was 6.70-fold higher in the first group, validating our approach for the generation of kinase-targeted libraries and the identification of scaffolds with high kinase inhibitory potential.

  DOI：

  10.1021/jm701367r

文献信息

• Development and Experimental Validation of a Docking Strategy for the Generation of Kinase-Targeted Libraries
  作者：Rafael Gozalbes、Laurence Simon、Nicolas Froloff、Eric Sartori、Claude Monteils、Romuald Baudelle

  DOI：10.1021/jm701367r

  日期：2008.6.1

  A high-throughput docking strategy for the filtering of in silico compounds and the generation of kinase-targeted libraries is described. Systematic docking and scoring in three kinase crystal 3D structures of 123 structurally diverse kinase ligands led to the determination of six thresholds for each kinase. These thresholds were used as filters for the virtual screening of two collections of compounds: a collection of more than 2500 drugs and drug-like compounds (negative control) and a kinase-targeted library of 1440 compounds. This strategy was then experimentally validated by testing 60 compounds from the kinase-targeted library on 41 kinases from five different families. The 60 compounds were split into those passing all the thresholds and the others (30 compounds in each group). The overall hit enrichment was 6.70-fold higher in the first group, validating our approach for the generation of kinase-targeted libraries and the identification of scaffolds with high kinase inhibitory potential.